diff --git a/CS学习规划/index.html b/CS学习规划/index.html index feadcbed..6546f27c 100644 --- a/CS学习规划/index.html +++ b/CS学习规划/index.html @@ -1,2 +1,2 @@ - 一个仅供参考的CS学习规划 - CS自学指南
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一个仅供参考的 CS 学习规划

计算机领域方向庞杂，知识浩如烟海，每个细分领域如果深究下去都可以说学无止境。因此，一个清晰明确的学习规划是非常重要的。这一节的内容是对后续整本书的内容的一个概览，你可以将其看作是这本书的目录，按需选择自己感兴趣的内容进行学习。

不过，在开始学习之前，先向小白们强烈推荐一个科普向系列视频 Crash Course: Computer Science，在短短 8 个小时里非常生动且全面地科普了关于计算机科学的方方面面：计算机的历史、计算机是如何运作的、组成计算机的各个重要模块、计算机科学中的重要思想等等等等。正如它的口号所说的 Computers are not magic!，希望看完这个视频之后，大家能对计算机科学有个全貌性地感知，从而怀着兴趣去面对下面浩如烟海的更为细致且深入的学习内容。

必学工具

俗话说：磨刀不误砍柴工。如果你是一个刚刚接触计算机的24k纯小白，学会一些工具将会让你事半功倍。

学会提问：也许你会惊讶，提问也算计算机必备技能吗，还放在第一条？我觉得在开源社区中，学会提问是一项非常重要的能力，它包含两方面的事情。其一是会变相地培养你自主解决问题的能力，因为从形成问题、描述问题并发布、他人回答、最后再到理解回答这个周期是非常长的，如果遇到什么鸡毛蒜皮的事情都希望别人最好远程桌面手把手帮你完成，那计算机的世界基本与你无缘了。其二，如果真的经过尝试还无法解决，可以借助开源社区的帮助，但这时候如何通过简洁的文字让别人瞬间理解你的处境以及目的，就显得尤为重要。推荐阅读提问的智慧这篇文章，这不仅能提高你解决问题的概率和效率，也能让开源社区里无偿提供解答的人们拥有一个好心情。

MIT-Missing-Semester 这门课覆盖了这些工具中绝大部分，而且有相当详细的使用指导，强烈建议小白学习。

翻墙：由于一些众所周知的原因，谷歌、GitHub 等网站在大陆无法访问。然而很多时候，谷歌和 StackOverflow 可以解决你在开发过程中遇到的 99% 的问题。因此，学会翻墙几乎是一个内地 CSer 的必备技能。（考虑到法律问题，这个文档提供的翻墙方式仅对拥有北大邮箱的用户适用）。

命令行：熟练使用命令行是一种常常被忽视，或被认为难以掌握的技能，但实际上，它会极大地提高你作为工程师的灵活性以及生产力。命令行的艺术是一份非常经典的教程，它源于 Quora 的一个提问，但在各路大神的贡献努力下已经成为了一个 GitHub 十万 stars 的顶流项目，被翻译成了十几种语言。教程不长，非常建议大家反复通读，在实践中内化吸收。同时，掌握 Shell 脚本编程也是一项不容忽视的技术，可以参考这个教程。

IDE (Integrated Development Environment)：集成开发环境，说白了就是你写代码的地方。作为一个码农，IDE 的重要性不言而喻，但由于很多 IDE 是为大型工程项目设计的，体量较大，功能也过于丰富。其实如今一些轻便的文本编辑器配合丰富的插件生态基本可以满足日常的轻量编程需求。个人常用的编辑器是 VS Code 和 Sublime（前者的插件配置非常简单，后者略显复杂但颜值很高）。当然对于大型项目我还是会采用略重型的 IDE，例如 Pycharm (Python)，IDEA (Java) 等等（免责申明：所有的 IDE 都是世界上最好的 IDE）。

Vim：一款命令行编辑工具。这是一个学习曲线有些陡峭的编辑器，不过学会它我觉得是非常有必要的，因为它将极大地提高你的开发效率。现在绝大多数 IDE 也都支持 Vim 插件，让你在享受现代开发环境的同时保留极客的炫酷（yue）。

Git：一款代码版本控制工具。Git的学习曲线可能更为陡峭，但出自 Linux 之父 Linus 之手的 Git 绝对是每个学 CS 的童鞋必须掌握的神器之一。

GitHub：基于 Git 的代码托管平台。全世界最大的代码开源社区，大佬集聚地。

GNU Make：一款工程构建工具。善用 GNU Make 会让你养成代码模块化的习惯，同时也能让你熟悉一些大型工程的编译链接流程。

CMake：一款功能比 GNU Make 更为强大的构建工具，建议掌握 GNU Make 之后再加以学习。

LaTex：逼格提升 论文排版工具。

Docker：一款相较于虚拟机更轻量级的软件打包与环境部署工具。

实用工具箱：除了上面提到的这些在开发中使用频率极高的工具之外，我还收集了很多实用有趣的免费工具，例如一些下载工具、设计工具、学习网站等等。

Thesis：毕业论文 Word 写作教程。

好书推荐

私以为一本好的教材应当是以人为本的，而不是炫技式的理论堆砌。告诉读者“是什么”固然重要，但更好的应当是教材作者将其在这个领域深耕几十年的经验融汇进书中，向读者娓娓道来“为什么”以及未来应该“怎么做”。

链接戳这里

环境配置

你以为的开发 —— 在 IDE 里疯狂码代码数小时。

实际上的开发 —— 配环境配几天还没开始写代码。

PC 端环境配置

如果你是 Mac 用户，那么你很幸运，这份指南 将会手把手地带你搭建起整套开发环境。如果你是 Windows 用户，可以参考这个相对简略的教程。

另外大家可以参考一份灵感来自 6.NULL MIT-Missing-Semester 的 环境配置指南，重点在于终端的美化配置。此外还包括常用软件源（如 GitHub, Anaconda, PyPI 等）的加速与替换以及一些 IDE 的配置与激活教程。

服务器端环境配置

推荐一个非常不错的 GitHub 项目 DevOps-Guide，其中涵盖了非常多的运维方面的基础知识和教程，例如 Docker, Kubernetes, Linux, CI-CD, GitHub Actions 等等。

课程地图

正如这章开头提到的，这份课程地图仅仅是一个仅供参考的课程规划，我作为一个临近毕业的本科生。深感自己没有权利也没有能力向别人宣扬“应该怎么学”。因此如果你觉得以下的课程分类与选择有不合理之处，我全盘接受，并深感抱歉。你可以在下一节定制属于你的课程地图

以下课程类别中除了含有 基础 和 入门 字眼的以外，并无明确的先后次序，大家只要满足某个课程的先修要求，完全可以根据自己的需要和喜好选择想要学习的课程。

另外由于贡献者的不断增加，这份课程地图已经从最初我的学习经历，发展成为很多 CS 自学者的资源合集，其中难免有内容交叉甚至重复的。之所以都列出来，还是希望集百家之长，给大家尽可能多的选择与参考。

数学基础

微积分与线性代数

作为大一新生，学好微积分线代是和写代码至少同等重要的事情，相信已经有无数的前人经验提到过这一点，但我还是要不厌其烦地再强调一遍：学好微积分线代真的很重要！你也许会吐槽这些东西岂不是考完就忘，那我觉得你是并没有把握住它们本质，对它们的理解还没有达到刻骨铭心的程度。如果觉得老师课上讲的内容晦涩难懂，不妨参考 MIT 的 Calculus Course 和 18.06: Linear Algebra 的课程 notes，至少于我而言，它帮助我深刻理解了微积分和线性代数的许多本质。顺道再安利一个油管数学网红 3Blue1Brown，他的频道有很多用生动形象的动画阐释数学本质内核的视频，兼具深度和广度，质量非常高。

信息论入门

作为计算机系的学生，及早了解一些信息论的基础知识，我觉得是大有裨益的。但大多信息论课程都面向高年级本科生甚至研究生，对新手极不友好。而 MIT 的 6.050J: Information theory and Entropy 这门课正是为大一新生量身定制的，几乎没有先修要求，涵盖了编码、压缩、通信、信息熵等等内容，非常有趣。

数学进阶

离散数学与概率论

集合论、图论、概率论等等是算法推导与证明的重要工具，也是后续高阶数学课程的基础。但我觉得这类课程的讲授很容易落入理论化与形式化的窠臼，让课堂成为定理结论的堆砌，而无法使学生深刻把握理论的本质，进而造成学了就背，考了就忘的怪圈。如果能在理论教学中穿插算法运用实例，学生在拓展算法知识的同时也能窥见理论的力量和魅力。

UCB CS70 : discrete Math and probability theory 和 UCB CS126 : Probability theory 是 UC Berkeley 的概率论课程，前者覆盖了离散数学和概率论基础，后者则涉及随机过程以及深入的理论内容。两者都非常注重理论和实践的结合，有丰富的算法实际运用实例，后者还有大量的 Python 编程作业来让学生运用概率论的知识解决实际问题。

数值分析

作为计算机系的学生，培养计算思维是很重要的，实际问题的建模、离散化，计算机的模拟、分析，是一项很重要的能力。而这两年开始风靡的，由 MIT 打造的 Julia 编程语言以其 C 一样的速度和 Python 一样友好的语法在数值计算领域有一统天下之势，MIT 的许多数学课程也开始用 Julia 作为教学工具，把艰深的数学理论用直观清晰的代码展示出来。

ComputationalThinking 是 MIT 开设的一门计算思维入门课，所有课程内容全部开源，可以在课程网站直接访问。这门课利用 Julia 编程语言，在图像处理、社会科学与数据科学、气候学建模三个 topic 下带领学生理解算法、数学建模、数据分析、交互设计、图例展示，让学生体验计算与科学的美妙结合。内容虽然不难，但给我最深刻的感受就是，科学的魅力并不是故弄玄虚的艰深理论，不是诘屈聱牙的术语行话，而是用直观生动的案例，用简练深刻的语言，让每个普通人都能理解。

上完上面的体验课之后，如果意犹未尽的话，不妨试试 MIT 的 18.330 : Introduction to numerical analysis，这门课的编程作业同样会用 Julia 编程语言，不过难度和深度上都上了一个台阶。内容涉及了浮点编码、Root finding、线性系统、微分方程等等方面，整门课的主旨就是让你利用离散化的计算机表示去估计和逼近一个数学上连续的概念。这门课的教授还专门撰写了一本配套的开源教材 Fundamentals of Numerical Computation，里面附有丰富的 Julia 代码实例和严谨的公式推导。

如果你还意犹未尽的话，还有 MIT 的数值分析研究生课程 18.335: Introduction to numerical method 供你参考。

微分方程

如果世间万物的运动发展都能用方程来刻画和描述，这是一件多么酷的事情呀！虽然几乎任何一所学校的 CS 培养方案中都没有微分方程相关的必修课程，但我还是觉得掌握它会赋予你一个新的视角来审视这个世界。

由于微分方程中往往会用到很多复变函数的知识，所以大家可以参考 MIT18.04: Complex variables functions 的课程 notes 来补齐先修知识。

MIT18.03: differential equations) 主要覆盖了常微分方程的求解，在此基础之上 MIT18.152: Partial differential equations) 则会深入偏微分方程的建模与求解。掌握了微分方程这一有利工具，相信对于你的实际问题的建模能力以及从众多噪声变量中把握本质的直觉都会有很大帮助。

数学高阶

作为计算机系的学生，我经常听到数学无用论的论断，对此我不敢苟同但也无权反对，但若凡事都硬要争出个有用和无用的区别来，倒也着实无趣，因此下面这些面向高年级甚至研究生的数学课程，大家按兴趣自取所需。

凸优化

Standford EE364A: Convex Optimization

信息论

MIT6.441: Information Theory

应用统计学

MIT18.650: Statistics for Applications

初等数论

MIT18.781: Theory of Numbers

密码学

Standford CS255: Cryptography

编程入门

Languages are tools, you choose the right tool to do the right thing. Since there's no universally perfect tool, there's no universally perfect language.

Shell

Python

C++

Rust

OCaml

电子基础

电路基础

作为计算机系的学生，了解一些基础的电路知识，感受从传感器收集数据到数据分析再到算法预测整条流水线，对于后续知识的学习以及计算思维的培养还是很有帮助的。EE16A&B: Designing Information Devices and Systems I&II 是伯克利 EE 学生的大一入门课，其中 EE16A 注重通过电路从实际环境中收集和分析数据，而 EE16B 则侧重从这些收集到的数据进行分析并做出预测行为。

信号与系统

信号与系统是一门我觉得非常值得一上的课，最初学它只是为了满足我对傅里叶变换的好奇，但学完之后我才不禁感叹，傅立叶变换给我提供了一个全新的视角去看待这个世界，就如同微分方程一样，让你沉浸在用数学去精确描绘和刻画这个世界的优雅与神奇之中。

MIT 6.003: signal and systems 提供了全部的课程录影、书面作业以及答案。也可以去看这门课的远古版本

而 UCB EE120: Signal and Systems 关于傅立叶变换的 notes 写得非常好，并且提供了6 个非常有趣的 Python 编程作业，让你实践中运用信号与系统的理论与算法。

数据结构与算法

数据结构与算法

算法设计与分析

软件工程

入门课

一份“能跑”的代码，和一份高质量的工业级代码是有本质区别的。因此我非常推荐低年级的同学学习一下 MIT 6.031: Software Construction 这门课，它会以 Java 语言为基础，以丰富细致的阅读材料和精心设计的编程练习传授如何编写不易出 bug、简明易懂、易于维护修改的高质量代码。大到宏观数据结构设计，小到如何写注释，遵循这些前人总结的细节和经验，对于你此后的编程生涯大有裨益。

专业课

当然，如果你想系统性地上一门软件工程的课程，那我推荐的是伯克利的 UCB CS169: software engineering。但需要提醒的是，和大多学校（包括贵校）的软件工程课程不同，这门课不会涉及传统的 design and document 模式，即强调各种类图、流程图及文档设计，而是采用近些年流行起来的小团队快速迭代 Agile Develepment 开发模式以及利用云平台的 Software as a service 服务模式。

体系结构

入门课

从小我就一直听说，计算机的世界是由 01 构成的，我不理解但大受震撼。如果你的内心也怀有这份好奇，不妨花一到两个月的时间学习 Coursera: Nand2Tetris 这门无门槛的计算机课程。这门麻雀虽小五脏俱全的课程会从 01 开始让你亲手造出一台计算机，并在上面运行俄罗斯方块小游戏。一门课里涵盖了编译、虚拟机、汇编、体系结构、数字电路、逻辑门等等从上至下、从软至硬的各类知识，非常全面。难度上也是通过精心的设计，略去了众多现代计算机复杂的细节，提取出了最核心本质的东西，力图让每个人都能理解。在低年级，如果就能从宏观上建立对整个计算机体系的鸟瞰图，是大有裨益的。

专业课

当然，如果想深入现代计算机体系结构的复杂细节，还得上一门大学本科难度的课程 UCB CS61C: Great Ideas in Computer Architecture。UC Berkeley 作为 RISC-V 架构的发源地，在体系结构领域算得上首屈一指。其课程非常注重实践，你会在 Project 中手写汇编构造神经网络，从零开始搭建一个 CPU，这些实践都会让你对计算机体系结构有更为深入的理解，而不是仅停留于“取指译码执行访存写回”的单调背诵里。

系统入门

计算机系统是一个庞杂而深刻的主题，在深入学习某个细分领域之前，对各个领域有一个宏观概念性的理解，对一些通用性的设计原则有所知晓，会让你在之后的深入学习中不断强化一些最为核心乃至哲学的概念，而不会桎梏于复杂的内部细节和各种 trick。因为在我看来，学习系统最关键的还是想让你领悟到这些最核心的东西，从而能够设计和实现出属于自己的系统。

MIT6.033: System Engineering 是 MIT 的系统入门课，主题涉及了操作系统、网络、分布式和系统安全，除了知识点的传授外，这门课还会讲授一些写作和表达上的技巧，让你学会如何设计并向别人介绍和分析自己的系统。这本书配套的教材 Principles of Computer System Design: An Introduction 也写得非常好，推荐大家阅读。

CMU 15-213: Introduction to Computer System 是 CMU 的系统入门课，内容覆盖了体系结构、操作系统、链接、并行、网络等等，兼具广度和深度，配套的教材 Computer Systems: A Programmer's Perspective 也是质量极高，强烈建议阅读。

操作系统

操作系统作为各类纷繁复杂的底层硬件虚拟化出一套规范优雅的抽象，给所有应用软件提供丰富的功能支持。了解操作系统的设计原则和内部原理对于一个不满足于当调包侠的程序员来说是大有裨益的。出于对操作系统的热爱，我上过国内外很多操作系统课程，它们各有侧重和优劣，大家可以根据兴趣各取所需。

MIT 6.S081: Operating System Engineering，MIT 著名 PDOS 实验室出品，11 个 Project 让你在一个实现非常优雅的类Unix操作系统xv6上增加各类功能模块。这门课也让我深刻认识到，做系统不是靠 PPT 念出来的，是得几万行代码一点点累起来的。

UCB CS162: Operating System，伯克利的操作系统课，采用和 Stanford 同样的 Project —— 一个教学用操作系统 Pintos。我作为北京大学2022年春季学期操作系统实验班的助教，引入并改善了这个 Project，课程资源也会全部开源，具体参见课程网站。

NJU: Operating System Design and Implementation，南京大学的蒋炎岩老师开设的操作系统课程。蒋老师以其独到的系统视角结合丰富的代码示例将众多操作系统的概念讲得深入浅出，此外这门课的全部课程内容都是中文的，非常方便大家学习。

并行与分布式系统

想必这两年各类 CS 讲座里最常听到的话就是“摩尔定律正在走向终结”，此话不假，当单核能力达到上限时，多核乃至众核架构如日中天。硬件的变化带来的是上层编程逻辑的适应与改变，要想充分利用硬件性能，编写并行程序几乎成了程序员的必备技能。与此同时，深度学习的兴起对计算机算力与存储的要求都达到了前所未有的高度，大规模集群的部署和优化也成为热门技术话题。

并行计算

CMU 15-418/Stanford CS149: Parallel Computing

分布式系统

MIT 6.824: Distributed System

系统安全

不知道你当年选择计算机是不是因为怀着一个中二的黑客梦想，但现实却是成为黑客道阻且长。

理论

UCB CS161: Computer Security 是伯克利的系统安全课程，会涵盖栈攻击、密码学、网站安全、网络安全等等内容。

实践

掌握这些理论知识之后，还需要在实践中培养和锻炼这些“黑客素养”。CTF 夺旗赛是一项比较热门的系统安全比赛，赛题中会融会贯通地考察你对计算机各个领域知识的理解和运用。北大今年也成功举办了第 0 届和第 1 届，鼓励大家后期踊跃参与，在实践中提高自己。下面列举一些我平时学习（摸鱼）用到的资源：

计算机网络

计网著名教材《自顶向下方法》的配套学习资源 Computer Networking: A Top-Down Approach。

没有什么能比自己写个 TCP/IP 协议栈更能加深对计算机网络的理解了，所以不妨试试 Stanford CS144: Computer Network，8 个 Project 带你实现整个协议栈。

数据库系统

没有什么能比自己写个关系型数据库更能加深对数据库系统的理解了。

C++版

CMU 15-445: Introduction to Database System

Java版

UCB CS186: Introduction to Database System

编译原理

没有什么能比自己写个编译器更能加深对编译器的理解了。

Stanford CS143: Compilers

计算机图形学

Stanford CS148 Games101 Games103 Games202

Web开发

网站的开发很少在计算机的培养方案里被重视，但其实掌握这项技能还是好处多多的，例如搭建自己的个人主页，抑或是给自己的课程项目做一个精彩的展示网页。

两周速成版

MIT web development course

系统学习版

Stanford CS142: Web Applications

数据科学

UCB Data100: Principles and Techniques of Data Science

人工智能

入门课

Harvard CS50’s Introduction to AI with Python

专业课

UCB CS188: Introduction to Artificial Intelligence

机器学习

入门课

Coursera: Machine Learning

专业课

深度学习

入门课

计算机视觉

Stanford CS231n: CNN for Visual Recognition

自然语言处理

Stanford CS224n: Natural Language Processing

图神经网络

Stanford CS224w: Machine Learning with Graphs

强化学习

UCB CS285: Deep Reinforcement Learning

定制属于你的课程地图

授人以鱼不如授人以渔。

以上的课程规划难免带有强烈的个人偏好，不一定适合所有人，更多是起到抛砖引玉的作用。如果你想挑选自己感兴趣的方向和内容加以学习，可以参考我在下面列出来的资源。

最后更新: 2022年9月10日

一个仅供参考的 CS 学习规划

计算机领域方向庞杂，知识浩如烟海，每个细分领域如果深究下去都可以说学无止境。因此，一个清晰明确的学习规划是非常重要的。这一节的内容是对后续整本书的内容的一个概览，你可以将其看作是这本书的目录，按需选择自己感兴趣的内容进行学习。

不过，在开始学习之前，先向小白们强烈推荐一个科普向系列视频 Crash Course: Computer Science，在短短 8 个小时里非常生动且全面地科普了关于计算机科学的方方面面：计算机的历史、计算机是如何运作的、组成计算机的各个重要模块、计算机科学中的重要思想等等等等。正如它的口号所说的 Computers are not magic!，希望看完这个视频之后，大家能对计算机科学有个全貌性地感知，从而怀着兴趣去面对下面浩如烟海的更为细致且深入的学习内容。

必学工具

俗话说：磨刀不误砍柴工。如果你是一个刚刚接触计算机的24k纯小白，学会一些工具将会让你事半功倍。

学会提问：也许你会惊讶，提问也算计算机必备技能吗，还放在第一条？我觉得在开源社区中，学会提问是一项非常重要的能力，它包含两方面的事情。其一是会变相地培养你自主解决问题的能力，因为从形成问题、描述问题并发布、他人回答、最后再到理解回答这个周期是非常长的，如果遇到什么鸡毛蒜皮的事情都希望别人最好远程桌面手把手帮你完成，那计算机的世界基本与你无缘了。其二，如果真的经过尝试还无法解决，可以借助开源社区的帮助，但这时候如何通过简洁的文字让别人瞬间理解你的处境以及目的，就显得尤为重要。推荐阅读提问的智慧这篇文章，这不仅能提高你解决问题的概率和效率，也能让开源社区里无偿提供解答的人们拥有一个好心情。

MIT-Missing-Semester 这门课覆盖了这些工具中绝大部分，而且有相当详细的使用指导，强烈建议小白学习。

翻墙：由于一些众所周知的原因，谷歌、GitHub 等网站在大陆无法访问。然而很多时候，谷歌和 StackOverflow 可以解决你在开发过程中遇到的 99% 的问题。因此，学会翻墙几乎是一个内地 CSer 的必备技能。（考虑到法律问题，这个文档提供的翻墙方式仅对拥有北大邮箱的用户适用）。

命令行：熟练使用命令行是一种常常被忽视，或被认为难以掌握的技能，但实际上，它会极大地提高你作为工程师的灵活性以及生产力。命令行的艺术是一份非常经典的教程，它源于 Quora 的一个提问，但在各路大神的贡献努力下已经成为了一个 GitHub 十万 stars 的顶流项目，被翻译成了十几种语言。教程不长，非常建议大家反复通读，在实践中内化吸收。同时，掌握 Shell 脚本编程也是一项不容忽视的技术，可以参考这个教程。

IDE (Integrated Development Environment)：集成开发环境，说白了就是你写代码的地方。作为一个码农，IDE 的重要性不言而喻，但由于很多 IDE 是为大型工程项目设计的，体量较大，功能也过于丰富。其实如今一些轻便的文本编辑器配合丰富的插件生态基本可以满足日常的轻量编程需求。个人常用的编辑器是 VS Code 和 Sublime（前者的插件配置非常简单，后者略显复杂但颜值很高）。当然对于大型项目我还是会采用略重型的 IDE，例如 Pycharm (Python)，IDEA (Java) 等等（免责申明：所有的 IDE 都是世界上最好的 IDE）。

Vim：一款命令行编辑工具。这是一个学习曲线有些陡峭的编辑器，不过学会它我觉得是非常有必要的，因为它将极大地提高你的开发效率。现在绝大多数 IDE 也都支持 Vim 插件，让你在享受现代开发环境的同时保留极客的炫酷（yue）。

Git：一款代码版本控制工具。Git的学习曲线可能更为陡峭，但出自 Linux 之父 Linus 之手的 Git 绝对是每个学 CS 的童鞋必须掌握的神器之一。

GitHub：基于 Git 的代码托管平台。全世界最大的代码开源社区，大佬集聚地。

GNU Make：一款工程构建工具。善用 GNU Make 会让你养成代码模块化的习惯，同时也能让你熟悉一些大型工程的编译链接流程。

CMake：一款功能比 GNU Make 更为强大的构建工具，建议掌握 GNU Make 之后再加以学习。

LaTex：逼格提升 论文排版工具。

Docker：一款相较于虚拟机更轻量级的软件打包与环境部署工具。

实用工具箱：除了上面提到的这些在开发中使用频率极高的工具之外，我还收集了很多实用有趣的免费工具，例如一些下载工具、设计工具、学习网站等等。

Thesis：毕业论文 Word 写作教程。

好书推荐

私以为一本好的教材应当是以人为本的，而不是炫技式的理论堆砌。告诉读者“是什么”固然重要，但更好的应当是教材作者将其在这个领域深耕几十年的经验融汇进书中，向读者娓娓道来“为什么”以及未来应该“怎么做”。

链接戳这里

环境配置

你以为的开发 —— 在 IDE 里疯狂码代码数小时。

实际上的开发 —— 配环境配几天还没开始写代码。

PC 端环境配置

如果你是 Mac 用户，那么你很幸运，这份指南 将会手把手地带你搭建起整套开发环境。如果你是 Windows 用户，可以参考这个相对简略的教程。

另外大家可以参考一份灵感来自 6.NULL MIT-Missing-Semester 的 环境配置指南，重点在于终端的美化配置。此外还包括常用软件源（如 GitHub, Anaconda, PyPI 等）的加速与替换以及一些 IDE 的配置与激活教程。

服务器端环境配置

推荐一个非常不错的 GitHub 项目 DevOps-Guide，其中涵盖了非常多的运维方面的基础知识和教程，例如 Docker, Kubernetes, Linux, CI-CD, GitHub Actions 等等。

课程地图

正如这章开头提到的，这份课程地图仅仅是一个仅供参考的课程规划，我作为一个临近毕业的本科生。深感自己没有权利也没有能力向别人宣扬“应该怎么学”。因此如果你觉得以下的课程分类与选择有不合理之处，我全盘接受，并深感抱歉。你可以在下一节定制属于你的课程地图

以下课程类别中除了含有 基础 和 入门 字眼的以外，并无明确的先后次序，大家只要满足某个课程的先修要求，完全可以根据自己的需要和喜好选择想要学习的课程。

另外由于贡献者的不断增加，这份课程地图已经从最初我的学习经历，发展成为很多 CS 自学者的资源合集，其中难免有内容交叉甚至重复的。之所以都列出来，还是希望集百家之长，给大家尽可能多的选择与参考。

数学基础

微积分与线性代数

作为大一新生，学好微积分线代是和写代码至少同等重要的事情，相信已经有无数的前人经验提到过这一点，但我还是要不厌其烦地再强调一遍：学好微积分线代真的很重要！你也许会吐槽这些东西岂不是考完就忘，那我觉得你是并没有把握住它们本质，对它们的理解还没有达到刻骨铭心的程度。如果觉得老师课上讲的内容晦涩难懂，不妨参考 MIT 的 Calculus Course 和 18.06: Linear Algebra 的课程 notes，至少于我而言，它帮助我深刻理解了微积分和线性代数的许多本质。顺道再安利一个油管数学网红 3Blue1Brown，他的频道有很多用生动形象的动画阐释数学本质内核的视频，兼具深度和广度，质量非常高。

信息论入门

作为计算机系的学生，及早了解一些信息论的基础知识，我觉得是大有裨益的。但大多信息论课程都面向高年级本科生甚至研究生，对新手极不友好。而 MIT 的 6.050J: Information theory and Entropy 这门课正是为大一新生量身定制的，几乎没有先修要求，涵盖了编码、压缩、通信、信息熵等等内容，非常有趣。

数学进阶

离散数学与概率论

集合论、图论、概率论等等是算法推导与证明的重要工具，也是后续高阶数学课程的基础。但我觉得这类课程的讲授很容易落入理论化与形式化的窠臼，让课堂成为定理结论的堆砌，而无法使学生深刻把握理论的本质，进而造成学了就背，考了就忘的怪圈。如果能在理论教学中穿插算法运用实例，学生在拓展算法知识的同时也能窥见理论的力量和魅力。

UCB CS70 : discrete Math and probability theory 和 UCB CS126 : Probability theory 是 UC Berkeley 的概率论课程，前者覆盖了离散数学和概率论基础，后者则涉及随机过程以及深入的理论内容。两者都非常注重理论和实践的结合，有丰富的算法实际运用实例，后者还有大量的 Python 编程作业来让学生运用概率论的知识解决实际问题。

数值分析

作为计算机系的学生，培养计算思维是很重要的，实际问题的建模、离散化，计算机的模拟、分析，是一项很重要的能力。而这两年开始风靡的，由 MIT 打造的 Julia 编程语言以其 C 一样的速度和 Python 一样友好的语法在数值计算领域有一统天下之势，MIT 的许多数学课程也开始用 Julia 作为教学工具，把艰深的数学理论用直观清晰的代码展示出来。

ComputationalThinking 是 MIT 开设的一门计算思维入门课，所有课程内容全部开源，可以在课程网站直接访问。这门课利用 Julia 编程语言，在图像处理、社会科学与数据科学、气候学建模三个 topic 下带领学生理解算法、数学建模、数据分析、交互设计、图例展示，让学生体验计算与科学的美妙结合。内容虽然不难，但给我最深刻的感受就是，科学的魅力并不是故弄玄虚的艰深理论，不是诘屈聱牙的术语行话，而是用直观生动的案例，用简练深刻的语言，让每个普通人都能理解。

上完上面的体验课之后，如果意犹未尽的话，不妨试试 MIT 的 18.330 : Introduction to numerical analysis，这门课的编程作业同样会用 Julia 编程语言，不过难度和深度上都上了一个台阶。内容涉及了浮点编码、Root finding、线性系统、微分方程等等方面，整门课的主旨就是让你利用离散化的计算机表示去估计和逼近一个数学上连续的概念。这门课的教授还专门撰写了一本配套的开源教材 Fundamentals of Numerical Computation，里面附有丰富的 Julia 代码实例和严谨的公式推导。

如果你还意犹未尽的话，还有 MIT 的数值分析研究生课程 18.335: Introduction to numerical method 供你参考。

微分方程

如果世间万物的运动发展都能用方程来刻画和描述，这是一件多么酷的事情呀！虽然几乎任何一所学校的 CS 培养方案中都没有微分方程相关的必修课程，但我还是觉得掌握它会赋予你一个新的视角来审视这个世界。

由于微分方程中往往会用到很多复变函数的知识，所以大家可以参考 MIT18.04: Complex variables functions 的课程 notes 来补齐先修知识。

MIT18.03: differential equations 主要覆盖了常微分方程的求解，在此基础之上 MIT18.152: Partial differential equations 则会深入偏微分方程的建模与求解。掌握了微分方程这一有力工具，相信对于你的实际问题的建模能力以及从众多噪声变量中把握本质的直觉都会有很大帮助。

数学高阶

作为计算机系的学生，我经常听到数学无用论的论断，对此我不敢苟同但也无权反对，但若凡事都硬要争出个有用和无用的区别来，倒也着实无趣，因此下面这些面向高年级甚至研究生的数学课程，大家按兴趣自取所需。

凸优化

Standford EE364A: Convex Optimization

信息论

MIT6.441: Information Theory

应用统计学

MIT18.650: Statistics for Applications

初等数论

MIT18.781: Theory of Numbers

密码学

Standford CS255: Cryptography

编程入门

Languages are tools, you choose the right tool to do the right thing. Since there's no universally perfect tool, there's no universally perfect language.

Shell

Python

C++

Rust

OCaml

电子基础

电路基础

作为计算机系的学生，了解一些基础的电路知识，感受从传感器收集数据到数据分析再到算法预测整条流水线，对于后续知识的学习以及计算思维的培养还是很有帮助的。EE16A&B: Designing Information Devices and Systems I&II 是伯克利 EE 学生的大一入门课，其中 EE16A 注重通过电路从实际环境中收集和分析数据，而 EE16B 则侧重从这些收集到的数据进行分析并做出预测行为。

信号与系统

信号与系统是一门我觉得非常值得一上的课，最初学它只是为了满足我对傅里叶变换的好奇，但学完之后我才不禁感叹，傅立叶变换给我提供了一个全新的视角去看待这个世界，就如同微分方程一样，让你沉浸在用数学去精确描绘和刻画这个世界的优雅与神奇之中。

MIT 6.003: signal and systems 提供了全部的课程录影、书面作业以及答案。也可以去看这门课的远古版本

而 UCB EE120: Signal and Systems 关于傅立叶变换的 notes 写得非常好，并且提供了6 个非常有趣的 Python 编程作业，让你实践中运用信号与系统的理论与算法。

数据结构与算法

算法是计算机科学的核心，也是几乎一切专业课程的基础。如何将实际问题通过数学抽象转化为算法问题，并选用合适的数据结构在时间和内存大小的限制下将其解决是算法课的永恒主题。如果你受够了老师的照本宣科，那么我强烈推荐伯克利的 UCB CS61B: Data Structures and Algorithms 和普林斯顿的 Coursera: Algorithms I & II，这两门课的都讲得深入浅出并且会有丰富且有趣的编程实验将理论与知识结合起来。此外，对一些更高级的算法以及 NP 问题感兴趣的同学可以学习伯克利的算法设计与分析课程 UCB CS170: Efficient Algorithms and Intractable Problems。

软件工程

入门课

一份“能跑”的代码，和一份高质量的工业级代码是有本质区别的。因此我非常推荐低年级的同学学习一下 MIT 6.031: Software Construction 这门课，它会以 Java 语言为基础，以丰富细致的阅读材料和精心设计的编程练习传授如何编写不易出 bug、简明易懂、易于维护修改的高质量代码。大到宏观数据结构设计，小到如何写注释，遵循这些前人总结的细节和经验，对于你此后的编程生涯大有裨益。

专业课

当然，如果你想系统性地上一门软件工程的课程，那我推荐的是伯克利的 UCB CS169: software engineering。但需要提醒的是，和大多学校（包括贵校）的软件工程课程不同，这门课不会涉及传统的 design and document 模式，即强调各种类图、流程图及文档设计，而是采用近些年流行起来的小团队快速迭代 Agile Develepment 开发模式以及利用云平台的 Software as a service 服务模式。

体系结构

入门课

从小我就一直听说，计算机的世界是由 01 构成的，我不理解但大受震撼。如果你的内心也怀有这份好奇，不妨花一到两个月的时间学习 Coursera: Nand2Tetris 这门无门槛的计算机课程。这门麻雀虽小五脏俱全的课程会从 01 开始让你亲手造出一台计算机，并在上面运行俄罗斯方块小游戏。一门课里涵盖了编译、虚拟机、汇编、体系结构、数字电路、逻辑门等等从上至下、从软至硬的各类知识，非常全面。难度上也是通过精心的设计，略去了众多现代计算机复杂的细节，提取出了最核心本质的东西，力图让每个人都能理解。在低年级，如果就能从宏观上建立对整个计算机体系的鸟瞰图，是大有裨益的。

专业课

当然，如果想深入现代计算机体系结构的复杂细节，还得上一门大学本科难度的课程 UCB CS61C: Great Ideas in Computer Architecture。UC Berkeley 作为 RISC-V 架构的发源地，在体系结构领域算得上首屈一指。其课程非常注重实践，你会在 Project 中手写汇编构造神经网络，从零开始搭建一个 CPU，这些实践都会让你对计算机体系结构有更为深入的理解，而不是仅停留于“取指译码执行访存写回”的单调背诵里。

系统入门

计算机系统是一个庞杂而深刻的主题，在深入学习某个细分领域之前，对各个领域有一个宏观概念性的理解，对一些通用性的设计原则有所知晓，会让你在之后的深入学习中不断强化一些最为核心乃至哲学的概念，而不会桎梏于复杂的内部细节和各种 trick。因为在我看来，学习系统最关键的还是想让你领悟到这些最核心的东西，从而能够设计和实现出属于自己的系统。

MIT6.033: System Engineering 是 MIT 的系统入门课，主题涉及了操作系统、网络、分布式和系统安全，除了知识点的传授外，这门课还会讲授一些写作和表达上的技巧，让你学会如何设计并向别人介绍和分析自己的系统。这本书配套的教材 Principles of Computer System Design: An Introduction 也写得非常好，推荐大家阅读。

CMU 15-213: Introduction to Computer System 是 CMU 的系统入门课，内容覆盖了体系结构、操作系统、链接、并行、网络等等，兼具广度和深度，配套的教材 Computer Systems: A Programmer's Perspective 也是质量极高，强烈建议阅读。

操作系统

没有什么能比自己写个内核更能加深对操作系统的理解了。

操作系统作为各类纷繁复杂的底层硬件虚拟化出一套规范优雅的抽象，给所有应用软件提供丰富的功能支持。了解操作系统的设计原则和内部原理对于一个不满足于当调包侠的程序员来说是大有裨益的。出于对操作系统的热爱，我上过国内外很多操作系统课程，它们各有侧重和优劣，大家可以根据兴趣各取所需。

MIT 6.S081: Operating System Engineering，MIT 著名 PDOS 实验室出品，11 个 Project 让你在一个实现非常优雅的类Unix操作系统xv6上增加各类功能模块。这门课也让我深刻认识到，做系统不是靠 PPT 念出来的，是得几万行代码一点点累起来的。

UCB CS162: Operating System，伯克利的操作系统课，采用和 Stanford 同样的 Project —— 一个教学用操作系统 Pintos。我作为北京大学2022年春季学期操作系统实验班的助教，引入并改善了这个 Project，课程资源也会全部开源，具体参见课程网站。

NJU: Operating System Design and Implementation，南京大学的蒋炎岩老师开设的操作系统课程。蒋老师以其独到的系统视角结合丰富的代码示例将众多操作系统的概念讲得深入浅出，此外这门课的全部课程内容都是中文的，非常方便大家学习。

并行与分布式系统

想必这两年各类 CS 讲座里最常听到的话就是“摩尔定律正在走向终结”，此话不假，当单核能力达到上限时，多核乃至众核架构如日中天。硬件的变化带来的是上层编程逻辑的适应与改变，要想充分利用硬件性能，编写并行程序几乎成了程序员的必备技能。与此同时，深度学习的兴起对计算机算力与存储的要求都达到了前所未有的高度，大规模集群的部署和优化也成为热门技术话题。

并行计算

CMU 15-418/Stanford CS149: Parallel Computing

分布式系统

MIT 6.824: Distributed System

系统安全

不知道你当年选择计算机是不是因为怀着一个中二的黑客梦想，但现实却是成为黑客道阻且长。

理论课程

UCB CS161: Computer Security 是伯克利的系统安全课程，会涵盖栈攻击、密码学、网站安全、网络安全等等内容。

实践课程

掌握这些理论知识之后，还需要在实践中培养和锻炼这些“黑客素养”。CTF 夺旗赛是一项比较热门的系统安全比赛，赛题中会融会贯通地考察你对计算机各个领域知识的理解和运用。北大今年也成功举办了第 0 届和第 1 届，鼓励大家后期踊跃参与，在实践中提高自己。下面列举一些我平时学习（摸鱼）用到的资源：

计算机网络

没有什么能比自己写个 TCP/IP 协议栈更能加深对计算机网络的理解了。

大名鼎鼎的 Stanford CS144: Computer Network，8 个 Project 带你实现整个 TCP/IP 协议栈。

如果你只是想在理论上对计算机网络有所了解，那么推荐计网著名教材《自顶向下方法》的配套学习资源 Computer Networking: A Top-Down Approach。

数据库系统

没有什么能比自己写个关系型数据库更能加深对数据库系统的理解了。

CMU 的著名数据库神课 CMU 15-445: Introduction to Database System 会通过 4 个 Project 带你为一个用于教学的关系型数据库 bustub 添加各种功能。实验的评测框架也免费开源了，非常适合大家自学。此外课程实验会用到 C++11 的众多新特性，也是一个锻炼 C++ 代码能力的好机会。

Berkeley 作为著名开源数据库 postgres 的发源地也不遑多让，UCB CS186: Introduction to Database System 会让你用 Java 语言实现一个支持 SQL 并发查询、B+ 树索引和故障恢复的关系型数据库。

编译原理

没有什么能比自己写个编译器更能加深对编译器的理解了。

Stanford CS143: Compilers 带你手写编译器。

Web开发

前后端开发很少在计算机的培养方案里被重视，但其实掌握这项技能还是好处多多的，例如搭建自己的个人主页，抑或是给自己的课程项目做一个精彩的展示网页。

两周速成版

MIT web development course

系统学习版

Stanford CS142: Web Applications

计算机图形学

数据科学

UCB Data100: Principles and Techniques of Data Science

人工智能

入门课

Harvard CS50’s Introduction to AI with Python

专业课

UCB CS188: Introduction to Artificial Intelligence

机器学习

入门课

Coursera: Machine Learning

专业课

深度学习

入门课

计算机视觉

Stanford CS231n: CNN for Visual Recognition

自然语言处理

Stanford CS224n: Natural Language Processing

图神经网络

Stanford CS224w: Machine Learning with Graphs

强化学习

UCB CS285: Deep Reinforcement Learning

定制属于你的课程地图

授人以鱼不如授人以渔。

以上的课程规划难免带有强烈的个人偏好，不一定适合所有人，更多是起到抛砖引玉的作用。如果你想挑选自己感兴趣的方向和内容加以学习，可以参考我在下面列出来的资源。

最后更新: 2022年10月8日
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一个仅供参考的 CS 学习规划

计算机领域方向庞杂，知识浩如烟海，每个细分领域如果深究下去都可以说学无止境。因此，一个清晰明确的学习规划是非常重要的。这一节的内容是对后续整本书的内容的一个概览，你可以将其看作是这本书的目录，按需选择自己感兴趣的内容进行学习。

不过，在开始学习之前，先向小白们强烈推荐一个科普向系列视频 Crash Course: Computer Science，在短短 8 个小时里非常生动且全面地科普了关于计算机科学的方方面面：计算机的历史、计算机是如何运作的、组成计算机的各个重要模块、计算机科学中的重要思想等等等等。正如它的口号所说的 Computers are not magic!，希望看完这个视频之后，大家能对计算机科学有个全貌性地感知，从而怀着兴趣去面对下面浩如烟海的更为细致且深入的学习内容。

必学工具

俗话说：磨刀不误砍柴工。如果你是一个刚刚接触计算机的24k纯小白，学会一些工具将会让你事半功倍。

学会提问：也许你会惊讶，提问也算计算机必备技能吗，还放在第一条？我觉得在开源社区中，学会提问是一项非常重要的能力，它包含两方面的事情。其一是会变相地培养你自主解决问题的能力，因为从形成问题、描述问题并发布、他人回答、最后再到理解回答这个周期是非常长的，如果遇到什么鸡毛蒜皮的事情都希望别人最好远程桌面手把手帮你完成，那计算机的世界基本与你无缘了。其二，如果真的经过尝试还无法解决，可以借助开源社区的帮助，但这时候如何通过简洁的文字让别人瞬间理解你的处境以及目的，就显得尤为重要。推荐阅读提问的智慧这篇文章，这不仅能提高你解决问题的概率和效率，也能让开源社区里无偿提供解答的人们拥有一个好心情。

MIT-Missing-Semester 这门课覆盖了这些工具中绝大部分，而且有相当详细的使用指导，强烈建议小白学习。

翻墙：由于一些众所周知的原因，谷歌、GitHub 等网站在大陆无法访问。然而很多时候，谷歌和 StackOverflow 可以解决你在开发过程中遇到的 99% 的问题。因此，学会翻墙几乎是一个内地 CSer 的必备技能。（考虑到法律问题，这个文档提供的翻墙方式仅对拥有北大邮箱的用户适用）。

命令行：熟练使用命令行是一种常常被忽视，或被认为难以掌握的技能，但实际上，它会极大地提高你作为工程师的灵活性以及生产力。命令行的艺术是一份非常经典的教程，它源于 Quora 的一个提问，但在各路大神的贡献努力下已经成为了一个 GitHub 十万 stars 的顶流项目，被翻译成了十几种语言。教程不长，非常建议大家反复通读，在实践中内化吸收。同时，掌握 Shell 脚本编程也是一项不容忽视的技术，可以参考这个教程。

IDE (Integrated Development Environment)：集成开发环境，说白了就是你写代码的地方。作为一个码农，IDE 的重要性不言而喻，但由于很多 IDE 是为大型工程项目设计的，体量较大，功能也过于丰富。其实如今一些轻便的文本编辑器配合丰富的插件生态基本可以满足日常的轻量编程需求。个人常用的编辑器是 VS Code 和 Sublime（前者的插件配置非常简单，后者略显复杂但颜值很高）。当然对于大型项目我还是会采用略重型的 IDE，例如 Pycharm (Python)，IDEA (Java) 等等（免责申明：所有的 IDE 都是世界上最好的 IDE）。

Vim：一款命令行编辑工具。这是一个学习曲线有些陡峭的编辑器，不过学会它我觉得是非常有必要的，因为它将极大地提高你的开发效率。现在绝大多数 IDE 也都支持 Vim 插件，让你在享受现代开发环境的同时保留极客的炫酷（yue）。

Git：一款代码版本控制工具。Git的学习曲线可能更为陡峭，但出自 Linux 之父 Linus 之手的 Git 绝对是每个学 CS 的童鞋必须掌握的神器之一。

GitHub：基于 Git 的代码托管平台。全世界最大的代码开源社区，大佬集聚地。

GNU Make：一款工程构建工具。善用 GNU Make 会让你养成代码模块化的习惯，同时也能让你熟悉一些大型工程的编译链接流程。

CMake：一款功能比 GNU Make 更为强大的构建工具，建议掌握 GNU Make 之后再加以学习。

LaTex：逼格提升 论文排版工具。

Docker：一款相较于虚拟机更轻量级的软件打包与环境部署工具。

实用工具箱：除了上面提到的这些在开发中使用频率极高的工具之外，我还收集了很多实用有趣的免费工具，例如一些下载工具、设计工具、学习网站等等。

Thesis：毕业论文 Word 写作教程。

好书推荐

私以为一本好的教材应当是以人为本的，而不是炫技式的理论堆砌。告诉读者“是什么”固然重要，但更好的应当是教材作者将其在这个领域深耕几十年的经验融汇进书中，向读者娓娓道来“为什么”以及未来应该“怎么做”。

链接戳这里

环境配置

你以为的开发 —— 在 IDE 里疯狂码代码数小时。

实际上的开发 —— 配环境配几天还没开始写代码。

PC 端环境配置

如果你是 Mac 用户，那么你很幸运，这份指南 将会手把手地带你搭建起整套开发环境。如果你是 Windows 用户，可以参考这个相对简略的教程。

另外大家可以参考一份灵感来自 6.NULL MIT-Missing-Semester 的 环境配置指南，重点在于终端的美化配置。此外还包括常用软件源（如 GitHub, Anaconda, PyPI 等）的加速与替换以及一些 IDE 的配置与激活教程。

服务器端环境配置

推荐一个非常不错的 GitHub 项目 DevOps-Guide，其中涵盖了非常多的运维方面的基础知识和教程，例如 Docker, Kubernetes, Linux, CI-CD, GitHub Actions 等等。

课程地图

正如这章开头提到的，这份课程地图仅仅是一个仅供参考的课程规划，我作为一个临近毕业的本科生。深感自己没有权利也没有能力向别人宣扬“应该怎么学”。因此如果你觉得以下的课程分类与选择有不合理之处，我全盘接受，并深感抱歉。你可以在下一节定制属于你的课程地图

以下课程类别中除了含有 基础 和 入门 字眼的以外，并无明确的先后次序，大家只要满足某个课程的先修要求，完全可以根据自己的需要和喜好选择想要学习的课程。

另外由于贡献者的不断增加，这份课程地图已经从最初我的学习经历，发展成为很多 CS 自学者的资源合集，其中难免有内容交叉甚至重复的。之所以都列出来，还是希望集百家之长，给大家尽可能多的选择与参考。

数学基础

微积分与线性代数

作为大一新生，学好微积分线代是和写代码至少同等重要的事情，相信已经有无数的前人经验提到过这一点，但我还是要不厌其烦地再强调一遍：学好微积分线代真的很重要！你也许会吐槽这些东西岂不是考完就忘，那我觉得你是并没有把握住它们本质，对它们的理解还没有达到刻骨铭心的程度。如果觉得老师课上讲的内容晦涩难懂，不妨参考 MIT 的 Calculus Course 和 18.06: Linear Algebra 的课程 notes，至少于我而言，它帮助我深刻理解了微积分和线性代数的许多本质。顺道再安利一个油管数学网红 3Blue1Brown，他的频道有很多用生动形象的动画阐释数学本质内核的视频，兼具深度和广度，质量非常高。

信息论入门

作为计算机系的学生，及早了解一些信息论的基础知识，我觉得是大有裨益的。但大多信息论课程都面向高年级本科生甚至研究生，对新手极不友好。而 MIT 的 6.050J: Information theory and Entropy 这门课正是为大一新生量身定制的，几乎没有先修要求，涵盖了编码、压缩、通信、信息熵等等内容，非常有趣。

数学进阶

离散数学与概率论

集合论、图论、概率论等等是算法推导与证明的重要工具，也是后续高阶数学课程的基础。但我觉得这类课程的讲授很容易落入理论化与形式化的窠臼，让课堂成为定理结论的堆砌，而无法使学生深刻把握理论的本质，进而造成学了就背，考了就忘的怪圈。如果能在理论教学中穿插算法运用实例，学生在拓展算法知识的同时也能窥见理论的力量和魅力。

UCB CS70 : discrete Math and probability theory 和 UCB CS126 : Probability theory 是 UC Berkeley 的概率论课程，前者覆盖了离散数学和概率论基础，后者则涉及随机过程以及深入的理论内容。两者都非常注重理论和实践的结合，有丰富的算法实际运用实例，后者还有大量的 Python 编程作业来让学生运用概率论的知识解决实际问题。

数值分析

作为计算机系的学生，培养计算思维是很重要的，实际问题的建模、离散化，计算机的模拟、分析，是一项很重要的能力。而这两年开始风靡的，由 MIT 打造的 Julia 编程语言以其 C 一样的速度和 Python 一样友好的语法在数值计算领域有一统天下之势，MIT 的许多数学课程也开始用 Julia 作为教学工具，把艰深的数学理论用直观清晰的代码展示出来。

ComputationalThinking 是 MIT 开设的一门计算思维入门课，所有课程内容全部开源，可以在课程网站直接访问。这门课利用 Julia 编程语言，在图像处理、社会科学与数据科学、气候学建模三个 topic 下带领学生理解算法、数学建模、数据分析、交互设计、图例展示，让学生体验计算与科学的美妙结合。内容虽然不难，但给我最深刻的感受就是，科学的魅力并不是故弄玄虚的艰深理论，不是诘屈聱牙的术语行话，而是用直观生动的案例，用简练深刻的语言，让每个普通人都能理解。

上完上面的体验课之后，如果意犹未尽的话，不妨试试 MIT 的 18.330 : Introduction to numerical analysis，这门课的编程作业同样会用 Julia 编程语言，不过难度和深度上都上了一个台阶。内容涉及了浮点编码、Root finding、线性系统、微分方程等等方面，整门课的主旨就是让你利用离散化的计算机表示去估计和逼近一个数学上连续的概念。这门课的教授还专门撰写了一本配套的开源教材 Fundamentals of Numerical Computation，里面附有丰富的 Julia 代码实例和严谨的公式推导。

如果你还意犹未尽的话，还有 MIT 的数值分析研究生课程 18.335: Introduction to numerical method 供你参考。

微分方程

如果世间万物的运动发展都能用方程来刻画和描述，这是一件多么酷的事情呀！虽然几乎任何一所学校的 CS 培养方案中都没有微分方程相关的必修课程，但我还是觉得掌握它会赋予你一个新的视角来审视这个世界。

由于微分方程中往往会用到很多复变函数的知识，所以大家可以参考 MIT18.04: Complex variables functions 的课程 notes 来补齐先修知识。

MIT18.03: differential equations) 主要覆盖了常微分方程的求解，在此基础之上 MIT18.152: Partial differential equations) 则会深入偏微分方程的建模与求解。掌握了微分方程这一有利工具，相信对于你的实际问题的建模能力以及从众多噪声变量中把握本质的直觉都会有很大帮助。

数学高阶

作为计算机系的学生，我经常听到数学无用论的论断，对此我不敢苟同但也无权反对，但若凡事都硬要争出个有用和无用的区别来，倒也着实无趣，因此下面这些面向高年级甚至研究生的数学课程，大家按兴趣自取所需。

凸优化

Standford EE364A: Convex Optimization

信息论

MIT6.441: Information Theory

应用统计学

MIT18.650: Statistics for Applications

初等数论

MIT18.781: Theory of Numbers

密码学

Standford CS255: Cryptography

编程入门

Languages are tools, you choose the right tool to do the right thing. Since there's no universally perfect tool, there's no universally perfect language.

Shell

Python

C++

Rust

OCaml

电子基础

电路基础

作为计算机系的学生，了解一些基础的电路知识，感受从传感器收集数据到数据分析再到算法预测整条流水线，对于后续知识的学习以及计算思维的培养还是很有帮助的。EE16A&B: Designing Information Devices and Systems I&II 是伯克利 EE 学生的大一入门课，其中 EE16A 注重通过电路从实际环境中收集和分析数据，而 EE16B 则侧重从这些收集到的数据进行分析并做出预测行为。

信号与系统

信号与系统是一门我觉得非常值得一上的课，最初学它只是为了满足我对傅里叶变换的好奇，但学完之后我才不禁感叹，傅立叶变换给我提供了一个全新的视角去看待这个世界，就如同微分方程一样，让你沉浸在用数学去精确描绘和刻画这个世界的优雅与神奇之中。

MIT 6.003: signal and systems 提供了全部的课程录影、书面作业以及答案。也可以去看这门课的远古版本

而 UCB EE120: Signal and Systems 关于傅立叶变换的 notes 写得非常好，并且提供了6 个非常有趣的 Python 编程作业，让你实践中运用信号与系统的理论与算法。

数据结构与算法

数据结构与算法

算法设计与分析

软件工程

入门课

一份“能跑”的代码，和一份高质量的工业级代码是有本质区别的。因此我非常推荐低年级的同学学习一下 MIT 6.031: Software Construction 这门课，它会以 Java 语言为基础，以丰富细致的阅读材料和精心设计的编程练习传授如何编写不易出 bug、简明易懂、易于维护修改的高质量代码。大到宏观数据结构设计，小到如何写注释，遵循这些前人总结的细节和经验，对于你此后的编程生涯大有裨益。

专业课

当然，如果你想系统性地上一门软件工程的课程，那我推荐的是伯克利的 UCB CS169: software engineering。但需要提醒的是，和大多学校（包括贵校）的软件工程课程不同，这门课不会涉及传统的 design and document 模式，即强调各种类图、流程图及文档设计，而是采用近些年流行起来的小团队快速迭代 Agile Develepment 开发模式以及利用云平台的 Software as a service 服务模式。

体系结构

入门课

从小我就一直听说，计算机的世界是由 01 构成的，我不理解但大受震撼。如果你的内心也怀有这份好奇，不妨花一到两个月的时间学习 Coursera: Nand2Tetris 这门无门槛的计算机课程。这门麻雀虽小五脏俱全的课程会从 01 开始让你亲手造出一台计算机，并在上面运行俄罗斯方块小游戏。一门课里涵盖了编译、虚拟机、汇编、体系结构、数字电路、逻辑门等等从上至下、从软至硬的各类知识，非常全面。难度上也是通过精心的设计，略去了众多现代计算机复杂的细节，提取出了最核心本质的东西，力图让每个人都能理解。在低年级，如果就能从宏观上建立对整个计算机体系的鸟瞰图，是大有裨益的。

专业课

当然，如果想深入现代计算机体系结构的复杂细节，还得上一门大学本科难度的课程 UCB CS61C: Great Ideas in Computer Architecture。UC Berkeley 作为 RISC-V 架构的发源地，在体系结构领域算得上首屈一指。其课程非常注重实践，你会在 Project 中手写汇编构造神经网络，从零开始搭建一个 CPU，这些实践都会让你对计算机体系结构有更为深入的理解，而不是仅停留于“取指译码执行访存写回”的单调背诵里。

系统入门

计算机系统是一个庞杂而深刻的主题，在深入学习某个细分领域之前，对各个领域有一个宏观概念性的理解，对一些通用性的设计原则有所知晓，会让你在之后的深入学习中不断强化一些最为核心乃至哲学的概念，而不会桎梏于复杂的内部细节和各种 trick。因为在我看来，学习系统最关键的还是想让你领悟到这些最核心的东西，从而能够设计和实现出属于自己的系统。

MIT6.033: System Engineering 是 MIT 的系统入门课，主题涉及了操作系统、网络、分布式和系统安全，除了知识点的传授外，这门课还会讲授一些写作和表达上的技巧，让你学会如何设计并向别人介绍和分析自己的系统。这本书配套的教材 Principles of Computer System Design: An Introduction 也写得非常好，推荐大家阅读。

CMU 15-213: Introduction to Computer System 是 CMU 的系统入门课，内容覆盖了体系结构、操作系统、链接、并行、网络等等，兼具广度和深度，配套的教材 Computer Systems: A Programmer's Perspective 也是质量极高，强烈建议阅读。

操作系统

操作系统作为各类纷繁复杂的底层硬件虚拟化出一套规范优雅的抽象，给所有应用软件提供丰富的功能支持。了解操作系统的设计原则和内部原理对于一个不满足于当调包侠的程序员来说是大有裨益的。出于对操作系统的热爱，我上过国内外很多操作系统课程，它们各有侧重和优劣，大家可以根据兴趣各取所需。

MIT 6.S081: Operating System Engineering，MIT 著名 PDOS 实验室出品，11 个 Project 让你在一个实现非常优雅的类Unix操作系统xv6上增加各类功能模块。这门课也让我深刻认识到，做系统不是靠 PPT 念出来的，是得几万行代码一点点累起来的。

UCB CS162: Operating System，伯克利的操作系统课，采用和 Stanford 同样的 Project —— 一个教学用操作系统 Pintos。我作为北京大学2022年春季学期操作系统实验班的助教，引入并改善了这个 Project，课程资源也会全部开源，具体参见课程网站。

NJU: Operating System Design and Implementation，南京大学的蒋炎岩老师开设的操作系统课程。蒋老师以其独到的系统视角结合丰富的代码示例将众多操作系统的概念讲得深入浅出，此外这门课的全部课程内容都是中文的，非常方便大家学习。

并行与分布式系统

想必这两年各类 CS 讲座里最常听到的话就是“摩尔定律正在走向终结”，此话不假，当单核能力达到上限时，多核乃至众核架构如日中天。硬件的变化带来的是上层编程逻辑的适应与改变，要想充分利用硬件性能，编写并行程序几乎成了程序员的必备技能。与此同时，深度学习的兴起对计算机算力与存储的要求都达到了前所未有的高度，大规模集群的部署和优化也成为热门技术话题。

并行计算

CMU 15-418/Stanford CS149: Parallel Computing

分布式系统

MIT 6.824: Distributed System

系统安全

不知道你当年选择计算机是不是因为怀着一个中二的黑客梦想，但现实却是成为黑客道阻且长。

理论

UCB CS161: Computer Security 是伯克利的系统安全课程，会涵盖栈攻击、密码学、网站安全、网络安全等等内容。

实践

掌握这些理论知识之后，还需要在实践中培养和锻炼这些“黑客素养”。CTF 夺旗赛是一项比较热门的系统安全比赛，赛题中会融会贯通地考察你对计算机各个领域知识的理解和运用。北大今年也成功举办了第 0 届和第 1 届，鼓励大家后期踊跃参与，在实践中提高自己。下面列举一些我平时学习（摸鱼）用到的资源：

计算机网络

计网著名教材《自顶向下方法》的配套学习资源 Computer Networking: A Top-Down Approach。

没有什么能比自己写个 TCP/IP 协议栈更能加深对计算机网络的理解了，所以不妨试试 Stanford CS144: Computer Network，8 个 Project 带你实现整个协议栈。

数据库系统

没有什么能比自己写个关系型数据库更能加深对数据库系统的理解了。

C++版

CMU 15-445: Introduction to Database System

Java版

UCB CS186: Introduction to Database System

编译原理

没有什么能比自己写个编译器更能加深对编译器的理解了。

Stanford CS143: Compilers

计算机图形学

Stanford CS148 Games101 Games103 Games202

Web开发

网站的开发很少在计算机的培养方案里被重视，但其实掌握这项技能还是好处多多的，例如搭建自己的个人主页，抑或是给自己的课程项目做一个精彩的展示网页。

两周速成版

MIT web development course

系统学习版

Stanford CS142: Web Applications

数据科学

UCB Data100: Principles and Techniques of Data Science

人工智能

入门课

Harvard CS50’s Introduction to AI with Python

专业课

UCB CS188: Introduction to Artificial Intelligence

机器学习

入门课

Coursera: Machine Learning

专业课

深度学习

入门课

计算机视觉

Stanford CS231n: CNN for Visual Recognition

自然语言处理

Stanford CS224n: Natural Language Processing

图神经网络

Stanford CS224w: Machine Learning with Graphs

强化学习

UCB CS285: Deep Reinforcement Learning

定制属于你的课程地图

授人以鱼不如授人以渔。

以上的课程规划难免带有强烈的个人偏好，不一定适合所有人，更多是起到抛砖引玉的作用。如果你想挑选自己感兴趣的方向和内容加以学习，可以参考我在下面列出来的资源。

Last update: September 10, 2022

一个仅供参考的 CS 学习规划

计算机领域方向庞杂，知识浩如烟海，每个细分领域如果深究下去都可以说学无止境。因此，一个清晰明确的学习规划是非常重要的。这一节的内容是对后续整本书的内容的一个概览，你可以将其看作是这本书的目录，按需选择自己感兴趣的内容进行学习。

不过，在开始学习之前，先向小白们强烈推荐一个科普向系列视频 Crash Course: Computer Science，在短短 8 个小时里非常生动且全面地科普了关于计算机科学的方方面面：计算机的历史、计算机是如何运作的、组成计算机的各个重要模块、计算机科学中的重要思想等等等等。正如它的口号所说的 Computers are not magic!，希望看完这个视频之后，大家能对计算机科学有个全貌性地感知，从而怀着兴趣去面对下面浩如烟海的更为细致且深入的学习内容。

必学工具

俗话说：磨刀不误砍柴工。如果你是一个刚刚接触计算机的24k纯小白，学会一些工具将会让你事半功倍。

学会提问：也许你会惊讶，提问也算计算机必备技能吗，还放在第一条？我觉得在开源社区中，学会提问是一项非常重要的能力，它包含两方面的事情。其一是会变相地培养你自主解决问题的能力，因为从形成问题、描述问题并发布、他人回答、最后再到理解回答这个周期是非常长的，如果遇到什么鸡毛蒜皮的事情都希望别人最好远程桌面手把手帮你完成，那计算机的世界基本与你无缘了。其二，如果真的经过尝试还无法解决，可以借助开源社区的帮助，但这时候如何通过简洁的文字让别人瞬间理解你的处境以及目的，就显得尤为重要。推荐阅读提问的智慧这篇文章，这不仅能提高你解决问题的概率和效率，也能让开源社区里无偿提供解答的人们拥有一个好心情。

MIT-Missing-Semester 这门课覆盖了这些工具中绝大部分，而且有相当详细的使用指导，强烈建议小白学习。

翻墙：由于一些众所周知的原因，谷歌、GitHub 等网站在大陆无法访问。然而很多时候，谷歌和 StackOverflow 可以解决你在开发过程中遇到的 99% 的问题。因此，学会翻墙几乎是一个内地 CSer 的必备技能。（考虑到法律问题，这个文档提供的翻墙方式仅对拥有北大邮箱的用户适用）。

命令行：熟练使用命令行是一种常常被忽视，或被认为难以掌握的技能，但实际上，它会极大地提高你作为工程师的灵活性以及生产力。命令行的艺术是一份非常经典的教程，它源于 Quora 的一个提问，但在各路大神的贡献努力下已经成为了一个 GitHub 十万 stars 的顶流项目，被翻译成了十几种语言。教程不长，非常建议大家反复通读，在实践中内化吸收。同时，掌握 Shell 脚本编程也是一项不容忽视的技术，可以参考这个教程。

IDE (Integrated Development Environment)：集成开发环境，说白了就是你写代码的地方。作为一个码农，IDE 的重要性不言而喻，但由于很多 IDE 是为大型工程项目设计的，体量较大，功能也过于丰富。其实如今一些轻便的文本编辑器配合丰富的插件生态基本可以满足日常的轻量编程需求。个人常用的编辑器是 VS Code 和 Sublime（前者的插件配置非常简单，后者略显复杂但颜值很高）。当然对于大型项目我还是会采用略重型的 IDE，例如 Pycharm (Python)，IDEA (Java) 等等（免责申明：所有的 IDE 都是世界上最好的 IDE）。

Vim：一款命令行编辑工具。这是一个学习曲线有些陡峭的编辑器，不过学会它我觉得是非常有必要的，因为它将极大地提高你的开发效率。现在绝大多数 IDE 也都支持 Vim 插件，让你在享受现代开发环境的同时保留极客的炫酷（yue）。

Git：一款代码版本控制工具。Git的学习曲线可能更为陡峭，但出自 Linux 之父 Linus 之手的 Git 绝对是每个学 CS 的童鞋必须掌握的神器之一。

GitHub：基于 Git 的代码托管平台。全世界最大的代码开源社区，大佬集聚地。

GNU Make：一款工程构建工具。善用 GNU Make 会让你养成代码模块化的习惯，同时也能让你熟悉一些大型工程的编译链接流程。

CMake：一款功能比 GNU Make 更为强大的构建工具，建议掌握 GNU Make 之后再加以学习。

LaTex：逼格提升 论文排版工具。

Docker：一款相较于虚拟机更轻量级的软件打包与环境部署工具。

实用工具箱：除了上面提到的这些在开发中使用频率极高的工具之外，我还收集了很多实用有趣的免费工具，例如一些下载工具、设计工具、学习网站等等。

Thesis：毕业论文 Word 写作教程。

好书推荐

私以为一本好的教材应当是以人为本的，而不是炫技式的理论堆砌。告诉读者“是什么”固然重要，但更好的应当是教材作者将其在这个领域深耕几十年的经验融汇进书中，向读者娓娓道来“为什么”以及未来应该“怎么做”。

链接戳这里

环境配置

你以为的开发 —— 在 IDE 里疯狂码代码数小时。

实际上的开发 —— 配环境配几天还没开始写代码。

PC 端环境配置

如果你是 Mac 用户，那么你很幸运，这份指南 将会手把手地带你搭建起整套开发环境。如果你是 Windows 用户，可以参考这个相对简略的教程。

另外大家可以参考一份灵感来自 6.NULL MIT-Missing-Semester 的 环境配置指南，重点在于终端的美化配置。此外还包括常用软件源（如 GitHub, Anaconda, PyPI 等）的加速与替换以及一些 IDE 的配置与激活教程。

服务器端环境配置

推荐一个非常不错的 GitHub 项目 DevOps-Guide，其中涵盖了非常多的运维方面的基础知识和教程，例如 Docker, Kubernetes, Linux, CI-CD, GitHub Actions 等等。

课程地图

正如这章开头提到的，这份课程地图仅仅是一个仅供参考的课程规划，我作为一个临近毕业的本科生。深感自己没有权利也没有能力向别人宣扬“应该怎么学”。因此如果你觉得以下的课程分类与选择有不合理之处，我全盘接受，并深感抱歉。你可以在下一节定制属于你的课程地图

以下课程类别中除了含有 基础 和 入门 字眼的以外，并无明确的先后次序，大家只要满足某个课程的先修要求，完全可以根据自己的需要和喜好选择想要学习的课程。

另外由于贡献者的不断增加，这份课程地图已经从最初我的学习经历，发展成为很多 CS 自学者的资源合集，其中难免有内容交叉甚至重复的。之所以都列出来，还是希望集百家之长，给大家尽可能多的选择与参考。

数学基础

微积分与线性代数

作为大一新生，学好微积分线代是和写代码至少同等重要的事情，相信已经有无数的前人经验提到过这一点，但我还是要不厌其烦地再强调一遍：学好微积分线代真的很重要！你也许会吐槽这些东西岂不是考完就忘，那我觉得你是并没有把握住它们本质，对它们的理解还没有达到刻骨铭心的程度。如果觉得老师课上讲的内容晦涩难懂，不妨参考 MIT 的 Calculus Course 和 18.06: Linear Algebra 的课程 notes，至少于我而言，它帮助我深刻理解了微积分和线性代数的许多本质。顺道再安利一个油管数学网红 3Blue1Brown，他的频道有很多用生动形象的动画阐释数学本质内核的视频，兼具深度和广度，质量非常高。

信息论入门

作为计算机系的学生，及早了解一些信息论的基础知识，我觉得是大有裨益的。但大多信息论课程都面向高年级本科生甚至研究生，对新手极不友好。而 MIT 的 6.050J: Information theory and Entropy 这门课正是为大一新生量身定制的，几乎没有先修要求，涵盖了编码、压缩、通信、信息熵等等内容，非常有趣。

数学进阶

离散数学与概率论

集合论、图论、概率论等等是算法推导与证明的重要工具，也是后续高阶数学课程的基础。但我觉得这类课程的讲授很容易落入理论化与形式化的窠臼，让课堂成为定理结论的堆砌，而无法使学生深刻把握理论的本质，进而造成学了就背，考了就忘的怪圈。如果能在理论教学中穿插算法运用实例，学生在拓展算法知识的同时也能窥见理论的力量和魅力。

UCB CS70 : discrete Math and probability theory 和 UCB CS126 : Probability theory 是 UC Berkeley 的概率论课程，前者覆盖了离散数学和概率论基础，后者则涉及随机过程以及深入的理论内容。两者都非常注重理论和实践的结合，有丰富的算法实际运用实例，后者还有大量的 Python 编程作业来让学生运用概率论的知识解决实际问题。

数值分析

作为计算机系的学生，培养计算思维是很重要的，实际问题的建模、离散化，计算机的模拟、分析，是一项很重要的能力。而这两年开始风靡的，由 MIT 打造的 Julia 编程语言以其 C 一样的速度和 Python 一样友好的语法在数值计算领域有一统天下之势，MIT 的许多数学课程也开始用 Julia 作为教学工具，把艰深的数学理论用直观清晰的代码展示出来。

ComputationalThinking 是 MIT 开设的一门计算思维入门课，所有课程内容全部开源，可以在课程网站直接访问。这门课利用 Julia 编程语言，在图像处理、社会科学与数据科学、气候学建模三个 topic 下带领学生理解算法、数学建模、数据分析、交互设计、图例展示，让学生体验计算与科学的美妙结合。内容虽然不难，但给我最深刻的感受就是，科学的魅力并不是故弄玄虚的艰深理论，不是诘屈聱牙的术语行话，而是用直观生动的案例，用简练深刻的语言，让每个普通人都能理解。

上完上面的体验课之后，如果意犹未尽的话，不妨试试 MIT 的 18.330 : Introduction to numerical analysis，这门课的编程作业同样会用 Julia 编程语言，不过难度和深度上都上了一个台阶。内容涉及了浮点编码、Root finding、线性系统、微分方程等等方面，整门课的主旨就是让你利用离散化的计算机表示去估计和逼近一个数学上连续的概念。这门课的教授还专门撰写了一本配套的开源教材 Fundamentals of Numerical Computation，里面附有丰富的 Julia 代码实例和严谨的公式推导。

如果你还意犹未尽的话，还有 MIT 的数值分析研究生课程 18.335: Introduction to numerical method 供你参考。

微分方程

如果世间万物的运动发展都能用方程来刻画和描述，这是一件多么酷的事情呀！虽然几乎任何一所学校的 CS 培养方案中都没有微分方程相关的必修课程，但我还是觉得掌握它会赋予你一个新的视角来审视这个世界。

由于微分方程中往往会用到很多复变函数的知识，所以大家可以参考 MIT18.04: Complex variables functions 的课程 notes 来补齐先修知识。

MIT18.03: differential equations 主要覆盖了常微分方程的求解，在此基础之上 MIT18.152: Partial differential equations 则会深入偏微分方程的建模与求解。掌握了微分方程这一有力工具，相信对于你的实际问题的建模能力以及从众多噪声变量中把握本质的直觉都会有很大帮助。

数学高阶

作为计算机系的学生，我经常听到数学无用论的论断，对此我不敢苟同但也无权反对，但若凡事都硬要争出个有用和无用的区别来，倒也着实无趣，因此下面这些面向高年级甚至研究生的数学课程，大家按兴趣自取所需。

凸优化

Standford EE364A: Convex Optimization

信息论

MIT6.441: Information Theory

应用统计学

MIT18.650: Statistics for Applications

初等数论

MIT18.781: Theory of Numbers

密码学

Standford CS255: Cryptography

编程入门

Languages are tools, you choose the right tool to do the right thing. Since there's no universally perfect tool, there's no universally perfect language.

Shell

Python

C++

Rust

OCaml

电子基础

电路基础

作为计算机系的学生，了解一些基础的电路知识，感受从传感器收集数据到数据分析再到算法预测整条流水线，对于后续知识的学习以及计算思维的培养还是很有帮助的。EE16A&B: Designing Information Devices and Systems I&II 是伯克利 EE 学生的大一入门课，其中 EE16A 注重通过电路从实际环境中收集和分析数据，而 EE16B 则侧重从这些收集到的数据进行分析并做出预测行为。

信号与系统

信号与系统是一门我觉得非常值得一上的课，最初学它只是为了满足我对傅里叶变换的好奇，但学完之后我才不禁感叹，傅立叶变换给我提供了一个全新的视角去看待这个世界，就如同微分方程一样，让你沉浸在用数学去精确描绘和刻画这个世界的优雅与神奇之中。

MIT 6.003: signal and systems 提供了全部的课程录影、书面作业以及答案。也可以去看这门课的远古版本

而 UCB EE120: Signal and Systems 关于傅立叶变换的 notes 写得非常好，并且提供了6 个非常有趣的 Python 编程作业，让你实践中运用信号与系统的理论与算法。

数据结构与算法

算法是计算机科学的核心，也是几乎一切专业课程的基础。如何将实际问题通过数学抽象转化为算法问题，并选用合适的数据结构在时间和内存大小的限制下将其解决是算法课的永恒主题。如果你受够了老师的照本宣科，那么我强烈推荐伯克利的 UCB CS61B: Data Structures and Algorithms 和普林斯顿的 Coursera: Algorithms I & II，这两门课的都讲得深入浅出并且会有丰富且有趣的编程实验将理论与知识结合起来。此外，对一些更高级的算法以及 NP 问题感兴趣的同学可以学习伯克利的算法设计与分析课程 UCB CS170: Efficient Algorithms and Intractable Problems。

软件工程

入门课

一份“能跑”的代码，和一份高质量的工业级代码是有本质区别的。因此我非常推荐低年级的同学学习一下 MIT 6.031: Software Construction 这门课，它会以 Java 语言为基础，以丰富细致的阅读材料和精心设计的编程练习传授如何编写不易出 bug、简明易懂、易于维护修改的高质量代码。大到宏观数据结构设计，小到如何写注释，遵循这些前人总结的细节和经验，对于你此后的编程生涯大有裨益。

专业课

当然，如果你想系统性地上一门软件工程的课程，那我推荐的是伯克利的 UCB CS169: software engineering。但需要提醒的是，和大多学校（包括贵校）的软件工程课程不同，这门课不会涉及传统的 design and document 模式，即强调各种类图、流程图及文档设计，而是采用近些年流行起来的小团队快速迭代 Agile Develepment 开发模式以及利用云平台的 Software as a service 服务模式。

体系结构

入门课

从小我就一直听说，计算机的世界是由 01 构成的，我不理解但大受震撼。如果你的内心也怀有这份好奇，不妨花一到两个月的时间学习 Coursera: Nand2Tetris 这门无门槛的计算机课程。这门麻雀虽小五脏俱全的课程会从 01 开始让你亲手造出一台计算机，并在上面运行俄罗斯方块小游戏。一门课里涵盖了编译、虚拟机、汇编、体系结构、数字电路、逻辑门等等从上至下、从软至硬的各类知识，非常全面。难度上也是通过精心的设计，略去了众多现代计算机复杂的细节，提取出了最核心本质的东西，力图让每个人都能理解。在低年级，如果就能从宏观上建立对整个计算机体系的鸟瞰图，是大有裨益的。

专业课

当然，如果想深入现代计算机体系结构的复杂细节，还得上一门大学本科难度的课程 UCB CS61C: Great Ideas in Computer Architecture。UC Berkeley 作为 RISC-V 架构的发源地，在体系结构领域算得上首屈一指。其课程非常注重实践，你会在 Project 中手写汇编构造神经网络，从零开始搭建一个 CPU，这些实践都会让你对计算机体系结构有更为深入的理解，而不是仅停留于“取指译码执行访存写回”的单调背诵里。

系统入门

计算机系统是一个庞杂而深刻的主题，在深入学习某个细分领域之前，对各个领域有一个宏观概念性的理解，对一些通用性的设计原则有所知晓，会让你在之后的深入学习中不断强化一些最为核心乃至哲学的概念，而不会桎梏于复杂的内部细节和各种 trick。因为在我看来，学习系统最关键的还是想让你领悟到这些最核心的东西，从而能够设计和实现出属于自己的系统。

MIT6.033: System Engineering 是 MIT 的系统入门课，主题涉及了操作系统、网络、分布式和系统安全，除了知识点的传授外，这门课还会讲授一些写作和表达上的技巧，让你学会如何设计并向别人介绍和分析自己的系统。这本书配套的教材 Principles of Computer System Design: An Introduction 也写得非常好，推荐大家阅读。

CMU 15-213: Introduction to Computer System 是 CMU 的系统入门课，内容覆盖了体系结构、操作系统、链接、并行、网络等等，兼具广度和深度，配套的教材 Computer Systems: A Programmer's Perspective 也是质量极高，强烈建议阅读。

操作系统

没有什么能比自己写个内核更能加深对操作系统的理解了。

操作系统作为各类纷繁复杂的底层硬件虚拟化出一套规范优雅的抽象，给所有应用软件提供丰富的功能支持。了解操作系统的设计原则和内部原理对于一个不满足于当调包侠的程序员来说是大有裨益的。出于对操作系统的热爱，我上过国内外很多操作系统课程，它们各有侧重和优劣，大家可以根据兴趣各取所需。

MIT 6.S081: Operating System Engineering，MIT 著名 PDOS 实验室出品，11 个 Project 让你在一个实现非常优雅的类Unix操作系统xv6上增加各类功能模块。这门课也让我深刻认识到，做系统不是靠 PPT 念出来的，是得几万行代码一点点累起来的。

UCB CS162: Operating System，伯克利的操作系统课，采用和 Stanford 同样的 Project —— 一个教学用操作系统 Pintos。我作为北京大学2022年春季学期操作系统实验班的助教，引入并改善了这个 Project，课程资源也会全部开源，具体参见课程网站。

NJU: Operating System Design and Implementation，南京大学的蒋炎岩老师开设的操作系统课程。蒋老师以其独到的系统视角结合丰富的代码示例将众多操作系统的概念讲得深入浅出，此外这门课的全部课程内容都是中文的，非常方便大家学习。

并行与分布式系统

想必这两年各类 CS 讲座里最常听到的话就是“摩尔定律正在走向终结”，此话不假，当单核能力达到上限时，多核乃至众核架构如日中天。硬件的变化带来的是上层编程逻辑的适应与改变，要想充分利用硬件性能，编写并行程序几乎成了程序员的必备技能。与此同时，深度学习的兴起对计算机算力与存储的要求都达到了前所未有的高度，大规模集群的部署和优化也成为热门技术话题。

并行计算

CMU 15-418/Stanford CS149: Parallel Computing

分布式系统

MIT 6.824: Distributed System

系统安全

不知道你当年选择计算机是不是因为怀着一个中二的黑客梦想，但现实却是成为黑客道阻且长。

理论课程

UCB CS161: Computer Security 是伯克利的系统安全课程，会涵盖栈攻击、密码学、网站安全、网络安全等等内容。

实践课程

掌握这些理论知识之后，还需要在实践中培养和锻炼这些“黑客素养”。CTF 夺旗赛是一项比较热门的系统安全比赛，赛题中会融会贯通地考察你对计算机各个领域知识的理解和运用。北大今年也成功举办了第 0 届和第 1 届，鼓励大家后期踊跃参与，在实践中提高自己。下面列举一些我平时学习（摸鱼）用到的资源：

计算机网络

没有什么能比自己写个 TCP/IP 协议栈更能加深对计算机网络的理解了。

大名鼎鼎的 Stanford CS144: Computer Network，8 个 Project 带你实现整个 TCP/IP 协议栈。

如果你只是想在理论上对计算机网络有所了解，那么推荐计网著名教材《自顶向下方法》的配套学习资源 Computer Networking: A Top-Down Approach。

数据库系统

没有什么能比自己写个关系型数据库更能加深对数据库系统的理解了。

CMU 的著名数据库神课 CMU 15-445: Introduction to Database System 会通过 4 个 Project 带你为一个用于教学的关系型数据库 bustub 添加各种功能。实验的评测框架也免费开源了，非常适合大家自学。此外课程实验会用到 C++11 的众多新特性，也是一个锻炼 C++ 代码能力的好机会。

Berkeley 作为著名开源数据库 postgres 的发源地也不遑多让，UCB CS186: Introduction to Database System 会让你用 Java 语言实现一个支持 SQL 并发查询、B+ 树索引和故障恢复的关系型数据库。

编译原理

没有什么能比自己写个编译器更能加深对编译器的理解了。

Stanford CS143: Compilers 带你手写编译器。

Web开发

前后端开发很少在计算机的培养方案里被重视，但其实掌握这项技能还是好处多多的，例如搭建自己的个人主页，抑或是给自己的课程项目做一个精彩的展示网页。

两周速成版

MIT web development course

系统学习版

Stanford CS142: Web Applications

计算机图形学

数据科学

UCB Data100: Principles and Techniques of Data Science

人工智能

入门课

Harvard CS50’s Introduction to AI with Python

专业课

UCB CS188: Introduction to Artificial Intelligence

机器学习

入门课

Coursera: Machine Learning

专业课

深度学习

入门课

计算机视觉

Stanford CS231n: CNN for Visual Recognition

自然语言处理

Stanford CS224n: Natural Language Processing

图神经网络

Stanford CS224w: Machine Learning with Graphs

强化学习

UCB CS285: Deep Reinforcement Learning

定制属于你的课程地图

授人以鱼不如授人以渔。

以上的课程规划难免带有强烈的个人偏好，不一定适合所有人，更多是起到抛砖引玉的作用。如果你想挑选自己感兴趣的方向和内容加以学习，可以参考我在下面列出来的资源。

Last update: October 8, 2022
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前言

最近更新：英文版正在建设中，增加陈天奇机器学习编译，增加 CMU 机器学习系统， ～

这是一本计算机的自学指南，也是对自己大学三年自学生涯的一个纪念。

这同时也是一份献给北大信科学弟学妹们的礼物。如果这本书能对你们的信科生涯有哪怕一丝一毫的帮助，都是对我极大的鼓励和慰藉。

本书目前包括了以下部分(如果你有其他好的建议，或者想加入贡献者的行列，欢迎邮件 zhongyinmin@pku.edu.cn 或者在 issue 里提问)：

  • 必学工具：IDE, 翻墙, StackOverflow, Git, GitHub, Vim, LaTeX, GNU Make, 实用工具 ...
  • 环境配置：PC端以及服务器端开发环境配置、各类运维相关教材及资料 ...
  • 经典书籍推荐：看过 CSAPP 这本书的同学一定感叹好书的重要，我将列举推荐自己看过的计算机领域的必看好书与资源链接。
  • 国外高质量 CS 课程汇总：我将把我上过的所有高质量的国外 CS 课程分门别类进行汇总，并给出相关的自学建议，大部分课程都会有一个独立的仓库维护相关的资源以及我的作业实现。

梦开始的地方 —— CS61A

大一入学时我是一个对计算机一无所知的小白，装了几十个 G 的 Visual Studio 天天和 OJ 你死我活。凭着高中的数学底子我数学课学得还不错，但在专业课上对竞赛大佬只有仰望。提到编程我只会打开那笨重的 IDE，新建一个我也不知道具体是干啥的命令行项目，然后就是 cin, cout, for 循环，然后 CE, RE, WA 循环。当时的我就处在一种拼命想学好但不知道怎么学，课上认真听讲但题还不会做，课后做作业完全是用时间和它硬耗的痛苦状态。我至今电脑里还存着自己大一上学期计算概论大作业的源代码 —— 一个 1200 行的 C++ 文件，没有头文件、没有类、没有封装、没有 unit test、没有 Makefile、没有 Git，唯一的优点是它确实能跑，缺点是“能跑”的补集。我一度怀疑我是不是不适合学计算机，因为童年对于极客的所有想象，已经被我第一个学期的体验彻底粉碎了。

这一切的转机发生在我大一的寒假，我心血来潮想学习 Python。无意间看到知乎有人推荐了 CS61A 这门课，说是 UC Berkeley 的大一入门课程，讲的就是 Python。我永远不会忘记那一天，打开 CS61A 课程网站的那个瞬间，就像哥伦布发现了新大陆一样，我开启了新世界的大门。

我一口气 3 个星期上完了这门课，它让我第一次感觉到原来 CS 可以学得如此充实而有趣，原来这世上竟有如此精华的课程。

为避免有崇洋媚外之嫌，我单纯从一个学生的视角来讲讲自学 CS61A 的体验：

  • 独立搭建的课程网站: 一个网站将所有课程资源整合一体，条理分明的课程 schedule、所有 slides, hw, discussion 的文件链接、详细明确的课程给分说明、历年的考试题与答案。这样一个网站抛开美观程度不谈，既方便学生，也让资源公正透明。

  • 课程教授亲自编写的教材：CS61A 这门课的开课老师将MIT的经典教材 Structure and Interpretation of Computer Programs (SICP) 用Python这门语言进行改编（原教材基于 Scheme 语言），保证了课堂内容与教材内容的一致性，同时补充了更多细节，可以说诚意满满。而且全书开源，可以直接线上阅读。

  • 丰富到让人眼花缭乱的课程作业：14 个 lab 巩固随堂知识点，10 个 homework，还有 4 个代码量均上千行的 project。与大家熟悉的 OJ 和 Word 文档式的作业不同，所有作业均有完善的代码框架，保姆级的作业说明。每个 Project 都有详尽的 handout 文档、全自动的评分脚本。CS61A 甚至专门开发了一个自动化的作业提交评分系统（据说还发了论文）。当然，有人会说“一个 project 几千行代码大部分都是助教帮你写好的，你还能学到啥？”。此言差矣，作为一个刚刚接触计算机，连安装 Python 都磕磕绊绊的小白来说，这样完善的代码框架既可以让你专注于巩固课堂上学习到的核心知识点，又能有“我才学了一个月就能做一个小游戏了！”的成就感，还能有机会阅读学习别人高质量的代码，从而为自己所用。我觉得在低年级，这种代码框架可以说百利而无一害。唯一的害也许是苦了老师和助教，因为开发这样的作业可想而知需要相当的时间投入。

  • 每周 Discussion 讨论课，助教会讲解知识难点和考试例题：类似于北京大学 ICS 的小班研讨，但习题全部用 LaTeX 撰写，相当规范且会明确给出 solution。

这样的课程，你完全不需要任何计算机的基础，你只需要努力、认真、花时间就够了。此前那种有劲没处使的感觉，那种付出再多时间却得不到回报的感觉，从此烟消云散。这太适合我了，我从此爱上了自学。

试想如果有人能把艰深的知识点嚼碎嚼烂，用生动直白的方式呈现给你，还有那么多听起来就很 fancy，种类繁多的 project 来巩固你的理论知识，你会觉得他们真的是在倾尽全力想方设法地让你完全掌握这门课，你会觉得不学好它简直是对这些课程建设者的侮辱。

如果你觉得我在夸大其词，那么不妨从 CS61A 开始，因为它是我的梦开始的地方。

为什么写这本书

在我2020年秋季学期担任《深入理解计算机系统》（CSAPP）这门课的助教时，我已经自学一年多了。这一年多来我无比享受这种自学模式，为了分享这种快乐，我为自己的小班同学做过一个 CS自学资料整理仓库。当时纯粹是心血来潮，因为我也不敢公然鼓励大家翘课自学。

但随着又一年时间的维护，这个仓库的内容已经相当丰富，基本覆盖了计科、智能系、软工系的绝大多数课程，我也为每个课程都建了各自的 GitHub 仓库，汇总我用到的自学资料以及作业实现。

直到大四开始凑学分毕业的时候，我打开自己的培养方案，我发现它已经是我这个自学仓库的子集了，而这距离我开始自学也才两年半而已。于是，一个大胆的想法在我脑海中浮现：也许，我可以打造一个自学式的培养方案，把我这三年自学经历中遇到的坑、走过的路记录下来，以期能为后来的学弟学妹们贡献自己的一份微薄之力。

如果大家可以在三年不到的时间里就能建立起整座CS的基础大厦，能有相对扎实的数学功底和代码能力，经历过数十个千行代码量的 Project 的洗礼，掌握至少 C/C++/Java/JS/Python/Go/Rust 等主流语言，对算法、电路、体系、网络、操统、编译、人工智能、机器学习、计算机视觉、自然语言处理、强化学习、密码学、信息论、博弈论、数值分析、统计学、分布式、数据库、图形学、Web开发、云服务、超算等等方面均有涉猎。我想，你将有足够的底气和自信选择自己感兴趣的方向，无论是就业还是科研，你都将有相当的竞争力。

因为我坚信，既然你能坚持听我 BB 到这里，你一定不缺学好 CS 的能力，你只是没有一个好的老师，给你讲一门好的课程。而我，将力图根据我三年的体验，为你挑选这样的课程。

自学的好处

对我来说，自学最大的好处就在于可以完全根据自己的进度来调整学习速度。对于一些疑难知识点，我可以反复回看视频，在网上谷歌相关的内容，上 StackOverflow 提问题，直到完全将它弄明白。而对于自己掌握得相对较快的内容，则可以两倍速甚至三倍速略过。

自学的另一大好处就是博采众长。计算机系的几大核心课程：体系、网络、操统、编译，每一门我基本都上过不同大学的课程，不同的教材、不同的知识点侧重、不同的 project 将会极大丰富你的视野，也会让你理解错误的一些内容得到及时纠正。

自学的第三个好处是时间自由，具体原因省略。

自学的坏处

当然，作为 CS 自学主义的忠实拥趸，我不得不承认自学也有它的坏处。

第一就是交流沟通的不便。我其实是一个很热衷于提问的人，对于所有没有弄明白的点，我都喜欢穷追到底。但当你面对着屏幕听到老师讲了一个你没明白的知识点的时候，你无法顺着网线到另一端向老师问个明白。我努力通过独立思考和善用 Google 来缓解这一点，但是，如果能有几个志同道合的伙伴结伴自学，那将是极好的。关于交流群的建立，大家可以参考仓库 README 中的教程。

第二就是这些自学的课程基本都是英文的。从视频到slides到作业全是英文，所以有一定的门槛。不过我觉得这个挑战如果你克服了的话对你是极为有利的。因为在当下，虽然我很不情愿，但也不得不承认，在计算机领域，很多优质的文档、论坛、网站都是全英文的。养成英文阅读的习惯，在赤旗插遍世界之前，还是有一定好处的（狗头保命）。

第三，也是我觉得最困难的一点，就是自律。因为没有 DDL 有时候真的是一件可怕的事情，特别是随着学习的深入，国外的很多课程是相当虐的。你得有足够的驱动力强迫自己静下心来，阅读几十页的 Project Handout，理解上千行的代码框架，忍受数个小时的 debug 时光。而这一切，没有学分，没有绩点，没有老师，没有同学，只有一个信念 —— 你在变强。

这本书适合谁

正如我在前言里说的，任何有志于自学计算机的朋友都可以参考这本书。如果你已经有了一定的计算机基础，只是对某个特定的领域感兴趣，可以选择性地挑选你感兴趣的内容进行学习。当然，如果你是一个像我当年一样对计算机一无所知的小白，初入大学的校门，我希望这本书能成为你的攻略，让你花最少的时间掌握你所需要的知识和能力。某种程度上，这本书更像是一个根据我的体验来排序的课程搜索引擎，帮助大家足不出户，体验世界顶级名校的计算机优质课程。

当然，作为一个还未毕业的本科生，我深感自己没有能力也没有权利去宣扬一种学习方式，我只是希望这份资料能让那些同样有自学之心和毅力朋友可以少走些弯路，收获更丰富、更多样、更满足的学习体验。

特别鸣谢

在这里，我怀着崇敬之心真诚地感谢所有将课程资源无偿开源的各位教授们。这些课程倾注了他们数十年教学生涯的积淀和心血，他们却选择无私地让所有人享受到如此高质量的CS教育。没有他们，我的大学生活不会这样充实而快乐。很多教授在我给他们发了感谢邮件之后，甚至会回复上百字的长文，真的让我无比感动。他们也时刻激励着我，做一件事，就得用心做好，无论是科研，还是为人。

你也想加入到贡献者的行列

一个人的力量终究是有限的，这本书也是我在繁重的科研之余熬夜抽空写出来的，难免有不够完善之处。另外，由于个人做的是系统方向，很多课程侧重系统领域，对于数学、理论计算机、高级算法相关的内容则相对少些。如果有大佬想在其他领域分享自己的自学经历与资源，可以直接在项目中发起 Pull Request，也欢迎和我邮件联系（zhongyinmin@pku.edu.cn）。

关于交流群的建立

方法参见仓库的 README.md。

最后更新: 2022年9月15日

Image title

前言

最近更新：英文版正在建设中，增加陈天奇机器学习编译，增加 CMU 机器学习系统

这是一本计算机的自学指南，也是对自己大学三年自学生涯的一个纪念。

这同时也是一份献给北大信科学弟学妹们的礼物。如果这本书能对你们的信科生涯有哪怕一丝一毫的帮助，都是对我极大的鼓励和慰藉。

本书目前包括了以下部分(如果你有其他好的建议，或者想加入贡献者的行列，欢迎邮件 zhongyinmin@pku.edu.cn 或者在 issue 里提问)：

  • 必学工具：IDE, 翻墙, StackOverflow, Git, GitHub, Vim, LaTeX, GNU Make, 实用工具 ...
  • 环境配置：PC端以及服务器端开发环境配置、各类运维相关教材及资料 ...
  • 经典书籍推荐：看过 CSAPP 这本书的同学一定感叹好书的重要，我将列举推荐自己看过的计算机领域的必看好书与资源链接。
  • 国外高质量 CS 课程汇总：我将把我上过的所有高质量的国外 CS 课程分门别类进行汇总，并给出相关的自学建议，大部分课程都会有一个独立的仓库维护相关的资源以及我的作业实现。

梦开始的地方 —— CS61A

大一入学时我是一个对计算机一无所知的小白，装了几十个 G 的 Visual Studio 天天和 OJ 你死我活。凭着高中的数学底子我数学课学得还不错，但在专业课上对竞赛大佬只有仰望。提到编程我只会打开那笨重的 IDE，新建一个我也不知道具体是干啥的命令行项目，然后就是 cin, cout, for 循环，然后 CE, RE, WA 循环。当时的我就处在一种拼命想学好但不知道怎么学，课上认真听讲但题还不会做，课后做作业完全是用时间和它硬耗的痛苦状态。我至今电脑里还存着自己大一上学期计算概论大作业的源代码 —— 一个 1200 行的 C++ 文件，没有头文件、没有类、没有封装、没有 unit test、没有 Makefile、没有 Git，唯一的优点是它确实能跑，缺点是“能跑”的补集。我一度怀疑我是不是不适合学计算机，因为童年对于极客的所有想象，已经被我第一个学期的体验彻底粉碎了。

这一切的转机发生在我大一的寒假，我心血来潮想学习 Python。无意间看到知乎有人推荐了 CS61A 这门课，说是 UC Berkeley 的大一入门课程，讲的就是 Python。我永远不会忘记那一天，打开 CS61A 课程网站的那个瞬间，就像哥伦布发现了新大陆一样，我开启了新世界的大门。

我一口气 3 个星期上完了这门课，它让我第一次感觉到原来 CS 可以学得如此充实而有趣，原来这世上竟有如此精华的课程。

为避免有崇洋媚外之嫌，我单纯从一个学生的视角来讲讲自学 CS61A 的体验：

  • 独立搭建的课程网站: 一个网站将所有课程资源整合一体，条理分明的课程 schedule、所有 slides, hw, discussion 的文件链接、详细明确的课程给分说明、历年的考试题与答案。这样一个网站抛开美观程度不谈，既方便学生，也让资源公正透明。

  • 课程教授亲自编写的教材：CS61A 这门课的开课老师将MIT的经典教材 Structure and Interpretation of Computer Programs (SICP) 用Python这门语言进行改编（原教材基于 Scheme 语言），保证了课堂内容与教材内容的一致性，同时补充了更多细节，可以说诚意满满。而且全书开源，可以直接线上阅读。

  • 丰富到让人眼花缭乱的课程作业：14 个 lab 巩固随堂知识点，10 个 homework，还有 4 个代码量均上千行的 project。与大家熟悉的 OJ 和 Word 文档式的作业不同，所有作业均有完善的代码框架，保姆级的作业说明。每个 Project 都有详尽的 handout 文档、全自动的评分脚本。CS61A 甚至专门开发了一个自动化的作业提交评分系统（据说还发了论文）。当然，有人会说“一个 project 几千行代码大部分都是助教帮你写好的，你还能学到啥？”。此言差矣，作为一个刚刚接触计算机，连安装 Python 都磕磕绊绊的小白来说，这样完善的代码框架既可以让你专注于巩固课堂上学习到的核心知识点，又能有“我才学了一个月就能做一个小游戏了！”的成就感，还能有机会阅读学习别人高质量的代码，从而为自己所用。我觉得在低年级，这种代码框架可以说百利而无一害。唯一的害也许是苦了老师和助教，因为开发这样的作业可想而知需要相当的时间投入。

  • 每周 Discussion 讨论课，助教会讲解知识难点和考试例题：类似于北京大学 ICS 的小班研讨，但习题全部用 LaTeX 撰写，相当规范且会明确给出 solution。

这样的课程，你完全不需要任何计算机的基础，你只需要努力、认真、花时间就够了。此前那种有劲没处使的感觉，那种付出再多时间却得不到回报的感觉，从此烟消云散。这太适合我了，我从此爱上了自学。

试想如果有人能把艰深的知识点嚼碎嚼烂，用生动直白的方式呈现给你，还有那么多听起来就很 fancy，种类繁多的 project 来巩固你的理论知识，你会觉得他们真的是在倾尽全力想方设法地让你完全掌握这门课，你会觉得不学好它简直是对这些课程建设者的侮辱。

如果你觉得我在夸大其词，那么不妨从 CS61A 开始，因为它是我的梦开始的地方。

为什么写这本书

在我2020年秋季学期担任《深入理解计算机系统》（CSAPP）这门课的助教时，我已经自学一年多了。这一年多来我无比享受这种自学模式，为了分享这种快乐，我为自己的小班同学做过一个 CS自学资料整理仓库。当时纯粹是心血来潮，因为我也不敢公然鼓励大家翘课自学。

但随着又一年时间的维护，这个仓库的内容已经相当丰富，基本覆盖了计科、智能系、软工系的绝大多数课程，我也为每个课程都建了各自的 GitHub 仓库，汇总我用到的自学资料以及作业实现。

直到大四开始凑学分毕业的时候，我打开自己的培养方案，我发现它已经是我这个自学仓库的子集了，而这距离我开始自学也才两年半而已。于是，一个大胆的想法在我脑海中浮现：也许，我可以打造一个自学式的培养方案，把我这三年自学经历中遇到的坑、走过的路记录下来，以期能为后来的学弟学妹们贡献自己的一份微薄之力。

如果大家可以在三年不到的时间里就能建立起整座CS的基础大厦，能有相对扎实的数学功底和代码能力，经历过数十个千行代码量的 Project 的洗礼，掌握至少 C/C++/Java/JS/Python/Go/Rust 等主流语言，对算法、电路、体系、网络、操统、编译、人工智能、机器学习、计算机视觉、自然语言处理、强化学习、密码学、信息论、博弈论、数值分析、统计学、分布式、数据库、图形学、Web开发、云服务、超算等等方面均有涉猎。我想，你将有足够的底气和自信选择自己感兴趣的方向，无论是就业还是科研，你都将有相当的竞争力。

因为我坚信，既然你能坚持听我 BB 到这里，你一定不缺学好 CS 的能力，你只是没有一个好的老师，给你讲一门好的课程。而我，将力图根据我三年的体验，为你挑选这样的课程。

自学的好处

对我来说，自学最大的好处就在于可以完全根据自己的进度来调整学习速度。对于一些疑难知识点，我可以反复回看视频，在网上谷歌相关的内容，上 StackOverflow 提问题，直到完全将它弄明白。而对于自己掌握得相对较快的内容，则可以两倍速甚至三倍速略过。

自学的另一大好处就是博采众长。计算机系的几大核心课程：体系、网络、操统、编译，每一门我基本都上过不同大学的课程，不同的教材、不同的知识点侧重、不同的 project 将会极大丰富你的视野，也会让你理解错误的一些内容得到及时纠正。

自学的第三个好处是时间自由，具体原因省略。

自学的坏处

当然，作为 CS 自学主义的忠实拥趸，我不得不承认自学也有它的坏处。

第一就是交流沟通的不便。我其实是一个很热衷于提问的人，对于所有没有弄明白的点，我都喜欢穷追到底。但当你面对着屏幕听到老师讲了一个你没明白的知识点的时候，你无法顺着网线到另一端向老师问个明白。我努力通过独立思考和善用 Google 来缓解这一点，但是，如果能有几个志同道合的伙伴结伴自学，那将是极好的。关于交流群的建立，大家可以参考仓库 README 中的教程。

第二就是这些自学的课程基本都是英文的。从视频到slides到作业全是英文，所以有一定的门槛。不过我觉得这个挑战如果你克服了的话对你是极为有利的。因为在当下，虽然我很不情愿，但也不得不承认，在计算机领域，很多优质的文档、论坛、网站都是全英文的。养成英文阅读的习惯，在赤旗插遍世界之前，还是有一定好处的（狗头保命）。

第三，也是我觉得最困难的一点，就是自律。因为没有 DDL 有时候真的是一件可怕的事情，特别是随着学习的深入，国外的很多课程是相当虐的。你得有足够的驱动力强迫自己静下心来，阅读几十页的 Project Handout，理解上千行的代码框架，忍受数个小时的 debug 时光。而这一切，没有学分，没有绩点，没有老师，没有同学，只有一个信念 —— 你在变强。

这本书适合谁

正如我在前言里说的，任何有志于自学计算机的朋友都可以参考这本书。如果你已经有了一定的计算机基础，只是对某个特定的领域感兴趣，可以选择性地挑选你感兴趣的内容进行学习。当然，如果你是一个像我当年一样对计算机一无所知的小白，初入大学的校门，我希望这本书能成为你的攻略，让你花最少的时间掌握你所需要的知识和能力。某种程度上，这本书更像是一个根据我的体验来排序的课程搜索引擎，帮助大家足不出户，体验世界顶级名校的计算机优质课程。

当然，作为一个还未毕业的本科生，我深感自己没有能力也没有权利去宣扬一种学习方式，我只是希望这份资料能让那些同样有自学之心和毅力朋友可以少走些弯路，收获更丰富、更多样、更满足的学习体验。

特别鸣谢

在这里，我怀着崇敬之心真诚地感谢所有将课程资源无偿开源的各位教授们。这些课程倾注了他们数十年教学生涯的积淀和心血，他们却选择无私地让所有人享受到如此高质量的CS教育。没有他们，我的大学生活不会这样充实而快乐。很多教授在我给他们发了感谢邮件之后，甚至会回复上百字的长文，真的让我无比感动。他们也时刻激励着我，做一件事，就得用心做好，无论是科研，还是为人。

你也想加入到贡献者的行列

一个人的力量终究是有限的，这本书也是我在繁重的科研之余熬夜抽空写出来的，难免有不够完善之处。另外，由于个人做的是系统方向，很多课程侧重系统领域，对于数学、理论计算机、高级算法相关的内容则相对少些。如果有大佬想在其他领域分享自己的自学经历与资源，可以直接在项目中发起 Pull Request，也欢迎和我邮件联系（zhongyinmin@pku.edu.cn）。

关于交流群的建立

方法参见仓库的 README.md。

最后更新: 2022年10月8日
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Pull Request\uff0c\u4e5f\u6b22\u8fce\u548c\u6211\u90ae\u4ef6\u8054\u7cfb\uff08 zhongyinmin@pku.edu.cn \uff09\u3002","title":"\u4f60\u4e5f\u60f3\u52a0\u5165\u5230\u8d21\u732e\u8005\u7684\u884c\u5217"},{"location":"#_8","text":"\u65b9\u6cd5\u53c2\u89c1\u4ed3\u5e93\u7684 README.md \u3002","title":"\u5173\u4e8e\u4ea4\u6d41\u7fa4\u7684\u5efa\u7acb"},{"location":"CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/","text":"\u4e00\u4e2a\u4ec5\u4f9b\u53c2\u8003\u7684 CS \u5b66\u4e60\u89c4\u5212 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Calculus Course \u548c 18.06: Linear Algebra \u7684\u8bfe\u7a0b notes\uff0c\u81f3\u5c11\u4e8e\u6211\u800c\u8a00\uff0c\u5b83\u5e2e\u52a9\u6211\u6df1\u523b\u7406\u89e3\u4e86\u5fae\u79ef\u5206\u548c\u7ebf\u6027\u4ee3\u6570\u7684\u8bb8\u591a\u672c\u8d28\u3002\u987a\u9053\u518d\u5b89\u5229\u4e00\u4e2a\u6cb9\u7ba1\u6570\u5b66\u7f51\u7ea2 3Blue1Brown \uff0c\u4ed6\u7684\u9891\u9053\u6709\u5f88\u591a\u7528\u751f\u52a8\u5f62\u8c61\u7684\u52a8\u753b\u9610\u91ca\u6570\u5b66\u672c\u8d28\u5185\u6838\u7684\u89c6\u9891\uff0c\u517c\u5177\u6df1\u5ea6\u548c\u5e7f\u5ea6\uff0c\u8d28\u91cf\u975e\u5e38\u9ad8\u3002 \u4fe1\u606f\u8bba\u5165\u95e8 \u4f5c\u4e3a\u8ba1\u7b97\u673a\u7cfb\u7684\u5b66\u751f\uff0c\u53ca\u65e9\u4e86\u89e3\u4e00\u4e9b\u4fe1\u606f\u8bba\u7684\u57fa\u7840\u77e5\u8bc6\uff0c\u6211\u89c9\u5f97\u662f\u5927\u6709\u88e8\u76ca\u7684\u3002\u4f46\u5927\u591a\u4fe1\u606f\u8bba\u8bfe\u7a0b\u90fd\u9762\u5411\u9ad8\u5e74\u7ea7\u672c\u79d1\u751f\u751a\u81f3\u7814\u7a76\u751f\uff0c\u5bf9\u65b0\u624b\u6781\u4e0d\u53cb\u597d\u3002\u800c MIT \u7684 6.050J: Information theory and Entropy \u8fd9\u95e8\u8bfe\u6b63\u662f\u4e3a\u5927\u4e00\u65b0\u751f\u91cf\u8eab\u5b9a\u5236\u7684\uff0c\u51e0\u4e4e\u6ca1\u6709\u5148\u4fee\u8981\u6c42\uff0c\u6db5\u76d6\u4e86\u7f16\u7801\u3001\u538b\u7f29\u3001\u901a\u4fe1\u3001\u4fe1\u606f\u71b5\u7b49\u7b49\u5185\u5bb9\uff0c\u975e\u5e38\u6709\u8da3\u3002 \u6570\u5b66\u8fdb\u9636 \u79bb\u6563\u6570\u5b66\u4e0e\u6982\u7387\u8bba \u96c6\u5408\u8bba\u3001\u56fe\u8bba\u3001\u6982\u7387\u8bba\u7b49\u7b49\u662f\u7b97\u6cd5\u63a8\u5bfc\u4e0e\u8bc1\u660e\u7684\u91cd\u8981\u5de5\u5177\uff0c\u4e5f\u662f\u540e\u7eed\u9ad8\u9636\u6570\u5b66\u8bfe\u7a0b\u7684\u57fa\u7840\u3002\u4f46\u6211\u89c9\u5f97\u8fd9\u7c7b\u8bfe\u7a0b\u7684\u8bb2\u6388\u5f88\u5bb9\u6613\u843d\u5165\u7406\u8bba\u5316\u4e0e\u5f62\u5f0f\u5316\u7684\u7aa0\u81fc\uff0c\u8ba9\u8bfe\u5802\u6210\u4e3a\u5b9a\u7406\u7ed3\u8bba\u7684\u5806\u780c\uff0c\u800c\u65e0\u6cd5\u4f7f\u5b66\u751f\u6df1\u523b\u628a\u63e1\u7406\u8bba\u7684\u672c\u8d28\uff0c\u8fdb\u800c\u9020\u6210\u5b66\u4e86\u5c31\u80cc\uff0c\u8003\u4e86\u5c31\u5fd8\u7684\u602a\u5708\u3002\u5982\u679c\u80fd\u5728\u7406\u8bba\u6559\u5b66\u4e2d\u7a7f\u63d2\u7b97\u6cd5\u8fd0\u7528\u5b9e\u4f8b\uff0c\u5b66\u751f\u5728\u62d3\u5c55\u7b97\u6cd5\u77e5\u8bc6\u7684\u540c\u65f6\u4e5f\u80fd\u7aa5\u89c1\u7406\u8bba\u7684\u529b\u91cf\u548c\u9b45\u529b\u3002 UCB CS70 : discrete Math and probability theory \u548c UCB CS126 : Probability theory \u662f UC Berkeley \u7684\u6982\u7387\u8bba\u8bfe\u7a0b\uff0c\u524d\u8005\u8986\u76d6\u4e86\u79bb\u6563\u6570\u5b66\u548c\u6982\u7387\u8bba\u57fa\u7840\uff0c\u540e\u8005\u5219\u6d89\u53ca\u968f\u673a\u8fc7\u7a0b\u4ee5\u53ca\u6df1\u5165\u7684\u7406\u8bba\u5185\u5bb9\u3002\u4e24\u8005\u90fd\u975e\u5e38\u6ce8\u91cd\u7406\u8bba\u548c\u5b9e\u8df5\u7684\u7ed3\u5408\uff0c\u6709\u4e30\u5bcc\u7684\u7b97\u6cd5\u5b9e\u9645\u8fd0\u7528\u5b9e\u4f8b\uff0c\u540e\u8005\u8fd8\u6709\u5927\u91cf\u7684 Python \u7f16\u7a0b\u4f5c\u4e1a\u6765\u8ba9\u5b66\u751f\u8fd0\u7528\u6982\u7387\u8bba\u7684\u77e5\u8bc6\u89e3\u51b3\u5b9e\u9645\u95ee\u9898\u3002 \u6570\u503c\u5206\u6790 \u4f5c\u4e3a\u8ba1\u7b97\u673a\u7cfb\u7684\u5b66\u751f\uff0c\u57f9\u517b\u8ba1\u7b97\u601d\u7ef4\u662f\u5f88\u91cd\u8981\u7684\uff0c\u5b9e\u9645\u95ee\u9898\u7684\u5efa\u6a21\u3001\u79bb\u6563\u5316\uff0c\u8ba1\u7b97\u673a\u7684\u6a21\u62df\u3001\u5206\u6790\uff0c\u662f\u4e00\u9879\u5f88\u91cd\u8981\u7684\u80fd\u529b\u3002\u800c\u8fd9\u4e24\u5e74\u5f00\u59cb\u98ce\u9761\u7684\uff0c\u7531 MIT \u6253\u9020\u7684 Julia \u7f16\u7a0b\u8bed\u8a00\u4ee5\u5176 C \u4e00\u6837\u7684\u901f\u5ea6\u548c Python \u4e00\u6837\u53cb\u597d\u7684\u8bed\u6cd5\u5728\u6570\u503c\u8ba1\u7b97\u9886\u57df\u6709\u4e00\u7edf\u5929\u4e0b\u4e4b\u52bf\uff0cMIT \u7684\u8bb8\u591a\u6570\u5b66\u8bfe\u7a0b\u4e5f\u5f00\u59cb\u7528 Julia \u4f5c\u4e3a\u6559\u5b66\u5de5\u5177\uff0c\u628a\u8270\u6df1\u7684\u6570\u5b66\u7406\u8bba\u7528\u76f4\u89c2\u6e05\u6670\u7684\u4ee3\u7801\u5c55\u793a\u51fa\u6765\u3002 ComputationalThinking \u662f MIT \u5f00\u8bbe\u7684\u4e00\u95e8\u8ba1\u7b97\u601d\u7ef4\u5165\u95e8\u8bfe\uff0c\u6240\u6709\u8bfe\u7a0b\u5185\u5bb9\u5168\u90e8\u5f00\u6e90\uff0c\u53ef\u4ee5\u5728\u8bfe\u7a0b\u7f51\u7ad9\u76f4\u63a5\u8bbf\u95ee\u3002\u8fd9\u95e8\u8bfe\u5229\u7528 Julia \u7f16\u7a0b\u8bed\u8a00\uff0c\u5728\u56fe\u50cf\u5904\u7406\u3001\u793e\u4f1a\u79d1\u5b66\u4e0e\u6570\u636e\u79d1\u5b66\u3001\u6c14\u5019\u5b66\u5efa\u6a21\u4e09\u4e2a topic \u4e0b\u5e26\u9886\u5b66\u751f\u7406\u89e3\u7b97\u6cd5\u3001\u6570\u5b66\u5efa\u6a21\u3001\u6570\u636e\u5206\u6790\u3001\u4ea4\u4e92\u8bbe\u8ba1\u3001\u56fe\u4f8b\u5c55\u793a\uff0c\u8ba9\u5b66\u751f\u4f53\u9a8c\u8ba1\u7b97\u4e0e\u79d1\u5b66\u7684\u7f8e\u5999\u7ed3\u5408\u3002\u5185\u5bb9\u867d\u7136\u4e0d\u96be\uff0c\u4f46\u7ed9\u6211\u6700\u6df1\u523b\u7684\u611f\u53d7\u5c31\u662f\uff0c\u79d1\u5b66\u7684\u9b45\u529b\u5e76\u4e0d\u662f\u6545\u5f04\u7384\u865a\u7684\u8270\u6df1\u7406\u8bba\uff0c\u4e0d\u662f\u8bd8\u5c48\u8071\u7259\u7684\u672f\u8bed\u884c\u8bdd\uff0c\u800c\u662f\u7528\u76f4\u89c2\u751f\u52a8\u7684\u6848\u4f8b\uff0c\u7528\u7b80\u7ec3\u6df1\u523b\u7684\u8bed\u8a00\uff0c\u8ba9\u6bcf\u4e2a\u666e\u901a\u4eba\u90fd\u80fd\u7406\u89e3\u3002 \u4e0a\u5b8c\u4e0a\u9762\u7684\u4f53\u9a8c\u8bfe\u4e4b\u540e\uff0c\u5982\u679c\u610f\u72b9\u672a\u5c3d\u7684\u8bdd\uff0c\u4e0d\u59a8\u8bd5\u8bd5 MIT \u7684 18.330 : Introduction to numerical analysis \uff0c\u8fd9\u95e8\u8bfe\u7684\u7f16\u7a0b\u4f5c\u4e1a\u540c\u6837\u4f1a\u7528 Julia \u7f16\u7a0b\u8bed\u8a00\uff0c\u4e0d\u8fc7\u96be\u5ea6\u548c\u6df1\u5ea6\u4e0a\u90fd\u4e0a\u4e86\u4e00\u4e2a\u53f0\u9636\u3002\u5185\u5bb9\u6d89\u53ca\u4e86\u6d6e\u70b9\u7f16\u7801\u3001Root finding\u3001\u7ebf\u6027\u7cfb\u7edf\u3001\u5fae\u5206\u65b9\u7a0b\u7b49\u7b49\u65b9\u9762\uff0c\u6574\u95e8\u8bfe\u7684\u4e3b\u65e8\u5c31\u662f\u8ba9\u4f60\u5229\u7528\u79bb\u6563\u5316\u7684\u8ba1\u7b97\u673a\u8868\u793a\u53bb\u4f30\u8ba1\u548c\u903c\u8fd1\u4e00\u4e2a\u6570\u5b66\u4e0a\u8fde\u7eed\u7684\u6982\u5ff5\u3002\u8fd9\u95e8\u8bfe\u7684\u6559\u6388\u8fd8\u4e13\u95e8\u64b0\u5199\u4e86\u4e00\u672c\u914d\u5957\u7684\u5f00\u6e90\u6559\u6750 Fundamentals of Numerical Computation \uff0c\u91cc\u9762\u9644\u6709\u4e30\u5bcc\u7684 Julia \u4ee3\u7801\u5b9e\u4f8b\u548c\u4e25\u8c28\u7684\u516c\u5f0f\u63a8\u5bfc\u3002 \u5982\u679c\u4f60\u8fd8\u610f\u72b9\u672a\u5c3d\u7684\u8bdd\uff0c\u8fd8\u6709 MIT \u7684\u6570\u503c\u5206\u6790\u7814\u7a76\u751f\u8bfe\u7a0b 18.335: Introduction to numerical method \u4f9b\u4f60\u53c2\u8003\u3002 \u5fae\u5206\u65b9\u7a0b \u5982\u679c\u4e16\u95f4\u4e07\u7269\u7684\u8fd0\u52a8\u53d1\u5c55\u90fd\u80fd\u7528\u65b9\u7a0b\u6765\u523b\u753b\u548c\u63cf\u8ff0\uff0c\u8fd9\u662f\u4e00\u4ef6\u591a\u4e48\u9177\u7684\u4e8b\u60c5\u5440\uff01\u867d\u7136\u51e0\u4e4e\u4efb\u4f55\u4e00\u6240\u5b66\u6821\u7684 CS \u57f9\u517b\u65b9\u6848\u4e2d\u90fd\u6ca1\u6709\u5fae\u5206\u65b9\u7a0b\u76f8\u5173\u7684\u5fc5\u4fee\u8bfe\u7a0b\uff0c\u4f46\u6211\u8fd8\u662f\u89c9\u5f97\u638c\u63e1\u5b83\u4f1a\u8d4b\u4e88\u4f60\u4e00\u4e2a\u65b0\u7684\u89c6\u89d2\u6765\u5ba1\u89c6\u8fd9\u4e2a\u4e16\u754c\u3002 \u7531\u4e8e\u5fae\u5206\u65b9\u7a0b\u4e2d\u5f80\u5f80\u4f1a\u7528\u5230\u5f88\u591a\u590d\u53d8\u51fd\u6570\u7684\u77e5\u8bc6\uff0c\u6240\u4ee5\u5927\u5bb6\u53ef\u4ee5\u53c2\u8003 MIT18.04: Complex variables functions \u7684\u8bfe\u7a0b notes \u6765\u8865\u9f50\u5148\u4fee\u77e5\u8bc6\u3002 MIT18.03: differential equations ) \u4e3b\u8981\u8986\u76d6\u4e86\u5e38\u5fae\u5206\u65b9\u7a0b\u7684\u6c42\u89e3\uff0c\u5728\u6b64\u57fa\u7840\u4e4b\u4e0a MIT18.152: Partial differential equations ) \u5219\u4f1a\u6df1\u5165\u504f\u5fae\u5206\u65b9\u7a0b\u7684\u5efa\u6a21\u4e0e\u6c42\u89e3\u3002\u638c\u63e1\u4e86\u5fae\u5206\u65b9\u7a0b\u8fd9\u4e00\u6709\u5229\u5de5\u5177\uff0c\u76f8\u4fe1\u5bf9\u4e8e\u4f60\u7684\u5b9e\u9645\u95ee\u9898\u7684\u5efa\u6a21\u80fd\u529b\u4ee5\u53ca\u4ece\u4f17\u591a\u566a\u58f0\u53d8\u91cf\u4e2d\u628a\u63e1\u672c\u8d28\u7684\u76f4\u89c9\u90fd\u4f1a\u6709\u5f88\u5927\u5e2e\u52a9\u3002 \u6570\u5b66\u9ad8\u9636 \u4f5c\u4e3a\u8ba1\u7b97\u673a\u7cfb\u7684\u5b66\u751f\uff0c\u6211\u7ecf\u5e38\u542c\u5230\u6570\u5b66\u65e0\u7528\u8bba\u7684\u8bba\u65ad\uff0c\u5bf9\u6b64\u6211\u4e0d\u6562\u82df\u540c\u4f46\u4e5f\u65e0\u6743\u53cd\u5bf9\uff0c\u4f46\u82e5\u51e1\u4e8b\u90fd\u786c\u8981\u4e89\u51fa\u4e2a\u6709\u7528\u548c\u65e0\u7528\u7684\u533a\u522b\u6765\uff0c\u5012\u4e5f\u7740\u5b9e\u65e0\u8da3\uff0c\u56e0\u6b64\u4e0b\u9762\u8fd9\u4e9b\u9762\u5411\u9ad8\u5e74\u7ea7\u751a\u81f3\u7814\u7a76\u751f\u7684\u6570\u5b66\u8bfe\u7a0b\uff0c\u5927\u5bb6\u6309\u5174\u8da3\u81ea\u53d6\u6240\u9700\u3002 \u51f8\u4f18\u5316 Standford EE364A: Convex Optimization \u4fe1\u606f\u8bba MIT6.441: Information Theory \u5e94\u7528\u7edf\u8ba1\u5b66 MIT18.650: Statistics for Applications \u521d\u7b49\u6570\u8bba MIT18.781: Theory of Numbers \u5bc6\u7801\u5b66 Standford CS255: Cryptography \u7f16\u7a0b\u5165\u95e8 Languages are tools, you choose the right tool to do the right thing. Since there's no universally perfect tool, there's no universally perfect language. Shell MIT-Missing-Semester Python Harvard CS50: This is CS50x UCB CS61A: Structure and Interpretation of Computer Programs C++ Stanford CS106B/X: Programming Abstractions Stanford CS106L: Standard C++ Programming Rust Stanford CS110L: Safety in Systems Programming OCaml Cornell CS3110 textbook: Functional Programming in OCaml \u7535\u5b50\u57fa\u7840 \u7535\u8def\u57fa\u7840 \u4f5c\u4e3a\u8ba1\u7b97\u673a\u7cfb\u7684\u5b66\u751f\uff0c\u4e86\u89e3\u4e00\u4e9b\u57fa\u7840\u7684\u7535\u8def\u77e5\u8bc6\uff0c\u611f\u53d7\u4ece\u4f20\u611f\u5668\u6536\u96c6\u6570\u636e\u5230\u6570\u636e\u5206\u6790\u518d\u5230\u7b97\u6cd5\u9884\u6d4b\u6574\u6761\u6d41\u6c34\u7ebf\uff0c\u5bf9\u4e8e\u540e\u7eed\u77e5\u8bc6\u7684\u5b66\u4e60\u4ee5\u53ca\u8ba1\u7b97\u601d\u7ef4\u7684\u57f9\u517b\u8fd8\u662f\u5f88\u6709\u5e2e\u52a9\u7684\u3002 EE16A&B: Designing Information Devices and Systems I&II \u662f\u4f2f\u514b\u5229 EE \u5b66\u751f\u7684\u5927\u4e00\u5165\u95e8\u8bfe\uff0c\u5176\u4e2d EE16A \u6ce8\u91cd\u901a\u8fc7\u7535\u8def\u4ece\u5b9e\u9645\u73af\u5883\u4e2d\u6536\u96c6\u548c\u5206\u6790\u6570\u636e\uff0c\u800c EE16B \u5219\u4fa7\u91cd\u4ece\u8fd9\u4e9b\u6536\u96c6\u5230\u7684\u6570\u636e\u8fdb\u884c\u5206\u6790\u5e76\u505a\u51fa\u9884\u6d4b\u884c\u4e3a\u3002 \u4fe1\u53f7\u4e0e\u7cfb\u7edf \u4fe1\u53f7\u4e0e\u7cfb\u7edf\u662f\u4e00\u95e8\u6211\u89c9\u5f97\u975e\u5e38\u503c\u5f97\u4e00\u4e0a\u7684\u8bfe\uff0c\u6700\u521d\u5b66\u5b83\u53ea\u662f\u4e3a\u4e86\u6ee1\u8db3\u6211\u5bf9\u5085\u91cc\u53f6\u53d8\u6362\u7684\u597d\u5947\uff0c\u4f46\u5b66\u5b8c\u4e4b\u540e\u6211\u624d\u4e0d\u7981\u611f\u53f9\uff0c\u5085\u7acb\u53f6\u53d8\u6362\u7ed9\u6211\u63d0\u4f9b\u4e86\u4e00\u4e2a\u5168\u65b0\u7684\u89c6\u89d2\u53bb\u770b\u5f85\u8fd9\u4e2a\u4e16\u754c\uff0c\u5c31\u5982\u540c\u5fae\u5206\u65b9\u7a0b\u4e00\u6837\uff0c\u8ba9\u4f60\u6c89\u6d78\u5728\u7528\u6570\u5b66\u53bb\u7cbe\u786e\u63cf\u7ed8\u548c\u523b\u753b\u8fd9\u4e2a\u4e16\u754c\u7684\u4f18\u96c5\u4e0e\u795e\u5947\u4e4b\u4e2d\u3002 MIT 6.003: signal and systems \u63d0\u4f9b\u4e86\u5168\u90e8\u7684\u8bfe\u7a0b\u5f55\u5f71\u3001\u4e66\u9762\u4f5c\u4e1a\u4ee5\u53ca\u7b54\u6848\u3002\u4e5f\u53ef\u4ee5\u53bb\u770b\u8fd9\u95e8\u8bfe\u7684 \u8fdc\u53e4\u7248\u672c \u800c UCB EE120: Signal and Systems \u5173\u4e8e\u5085\u7acb\u53f6\u53d8\u6362\u7684 notes \u5199\u5f97\u975e\u5e38\u597d\uff0c\u5e76\u4e14\u63d0\u4f9b\u4e866 \u4e2a\u975e\u5e38\u6709\u8da3\u7684 Python \u7f16\u7a0b\u4f5c\u4e1a\uff0c\u8ba9\u4f60\u5b9e\u8df5\u4e2d\u8fd0\u7528\u4fe1\u53f7\u4e0e\u7cfb\u7edf\u7684\u7406\u8bba\u4e0e\u7b97\u6cd5\u3002 \u6570\u636e\u7ed3\u6784\u4e0e\u7b97\u6cd5 \u6570\u636e\u7ed3\u6784\u4e0e\u7b97\u6cd5 UCB CS61B: Data Structures and Algorithms Coursera: Algorithms I & II \u7b97\u6cd5\u8bbe\u8ba1\u4e0e\u5206\u6790 UCB CS170: Efficient Algorithms and Intractable Problems \u8f6f\u4ef6\u5de5\u7a0b \u5165\u95e8\u8bfe \u4e00\u4efd\u201c\u80fd\u8dd1\u201d\u7684\u4ee3\u7801\uff0c\u548c\u4e00\u4efd\u9ad8\u8d28\u91cf\u7684\u5de5\u4e1a\u7ea7\u4ee3\u7801\u662f\u6709\u672c\u8d28\u533a\u522b\u7684\u3002\u56e0\u6b64\u6211\u975e\u5e38\u63a8\u8350\u4f4e\u5e74\u7ea7\u7684\u540c\u5b66\u5b66\u4e60\u4e00\u4e0b MIT 6.031: Software Construction \u8fd9\u95e8\u8bfe\uff0c\u5b83\u4f1a\u4ee5 Java \u8bed\u8a00\u4e3a\u57fa\u7840\uff0c\u4ee5\u4e30\u5bcc\u7ec6\u81f4\u7684\u9605\u8bfb\u6750\u6599\u548c\u7cbe\u5fc3\u8bbe\u8ba1\u7684\u7f16\u7a0b\u7ec3\u4e60\u4f20\u6388\u5982\u4f55\u7f16\u5199 \u4e0d\u6613\u51fa bug\u3001\u7b80\u660e\u6613\u61c2\u3001\u6613\u4e8e\u7ef4\u62a4\u4fee\u6539 \u7684\u9ad8\u8d28\u91cf\u4ee3\u7801\u3002\u5927\u5230\u5b8f\u89c2\u6570\u636e\u7ed3\u6784\u8bbe\u8ba1\uff0c\u5c0f\u5230\u5982\u4f55\u5199\u6ce8\u91ca\uff0c\u9075\u5faa\u8fd9\u4e9b\u524d\u4eba\u603b\u7ed3\u7684\u7ec6\u8282\u548c\u7ecf\u9a8c\uff0c\u5bf9\u4e8e\u4f60\u6b64\u540e\u7684\u7f16\u7a0b\u751f\u6daf\u5927\u6709\u88e8\u76ca\u3002 \u4e13\u4e1a\u8bfe \u5f53\u7136\uff0c\u5982\u679c\u4f60\u60f3\u7cfb\u7edf\u6027\u5730\u4e0a\u4e00\u95e8\u8f6f\u4ef6\u5de5\u7a0b\u7684\u8bfe\u7a0b\uff0c\u90a3\u6211\u63a8\u8350\u7684\u662f\u4f2f\u514b\u5229\u7684 UCB CS169: software engineering \u3002\u4f46\u9700\u8981\u63d0\u9192\u7684\u662f\uff0c\u548c\u5927\u591a\u5b66\u6821\uff08\u5305\u62ec\u8d35\u6821\uff09\u7684\u8f6f\u4ef6\u5de5\u7a0b\u8bfe\u7a0b\u4e0d\u540c\uff0c\u8fd9\u95e8\u8bfe\u4e0d\u4f1a\u6d89\u53ca\u4f20\u7edf\u7684 design and document \u6a21\u5f0f\uff0c\u5373\u5f3a\u8c03\u5404\u79cd\u7c7b\u56fe\u3001\u6d41\u7a0b\u56fe\u53ca\u6587\u6863\u8bbe\u8ba1\uff0c\u800c\u662f\u91c7\u7528\u8fd1\u4e9b\u5e74\u6d41\u884c\u8d77\u6765\u7684\u5c0f\u56e2\u961f\u5feb\u901f\u8fed\u4ee3 Agile Develepment \u5f00\u53d1\u6a21\u5f0f\u4ee5\u53ca\u5229\u7528\u4e91\u5e73\u53f0\u7684 Software as a service \u670d\u52a1\u6a21\u5f0f\u3002 \u4f53\u7cfb\u7ed3\u6784 \u5165\u95e8\u8bfe \u4ece\u5c0f\u6211\u5c31\u4e00\u76f4\u542c\u8bf4\uff0c\u8ba1\u7b97\u673a\u7684\u4e16\u754c\u662f\u7531 01 \u6784\u6210\u7684\uff0c\u6211\u4e0d\u7406\u89e3\u4f46\u5927\u53d7\u9707\u64bc\u3002\u5982\u679c\u4f60\u7684\u5185\u5fc3\u4e5f\u6000\u6709\u8fd9\u4efd\u597d\u5947\uff0c\u4e0d\u59a8\u82b1\u4e00\u5230\u4e24\u4e2a\u6708\u7684\u65f6\u95f4\u5b66\u4e60 Coursera: Nand2Tetris \u8fd9\u95e8\u65e0\u95e8\u69db\u7684\u8ba1\u7b97\u673a\u8bfe\u7a0b\u3002\u8fd9\u95e8\u9ebb\u96c0\u867d\u5c0f\u4e94\u810f\u4ff1\u5168\u7684\u8bfe\u7a0b\u4f1a\u4ece 01 \u5f00\u59cb\u8ba9\u4f60\u4eb2\u624b\u9020\u51fa\u4e00\u53f0\u8ba1\u7b97\u673a\uff0c\u5e76\u5728\u4e0a\u9762\u8fd0\u884c\u4fc4\u7f57\u65af\u65b9\u5757\u5c0f\u6e38\u620f\u3002\u4e00\u95e8\u8bfe\u91cc\u6db5\u76d6\u4e86\u7f16\u8bd1\u3001\u865a\u62df\u673a\u3001\u6c47\u7f16\u3001\u4f53\u7cfb\u7ed3\u6784\u3001\u6570\u5b57\u7535\u8def\u3001\u903b\u8f91\u95e8\u7b49\u7b49\u4ece\u4e0a\u81f3\u4e0b\u3001\u4ece\u8f6f\u81f3\u786c\u7684\u5404\u7c7b\u77e5\u8bc6\uff0c\u975e\u5e38\u5168\u9762\u3002\u96be\u5ea6\u4e0a\u4e5f\u662f\u901a\u8fc7\u7cbe\u5fc3\u7684\u8bbe\u8ba1\uff0c\u7565\u53bb\u4e86\u4f17\u591a\u73b0\u4ee3\u8ba1\u7b97\u673a\u590d\u6742\u7684\u7ec6\u8282\uff0c\u63d0\u53d6\u51fa\u4e86\u6700\u6838\u5fc3\u672c\u8d28\u7684\u4e1c\u897f\uff0c\u529b\u56fe\u8ba9\u6bcf\u4e2a\u4eba\u90fd\u80fd\u7406\u89e3\u3002\u5728\u4f4e\u5e74\u7ea7\uff0c\u5982\u679c\u5c31\u80fd\u4ece\u5b8f\u89c2\u4e0a\u5efa\u7acb\u5bf9\u6574\u4e2a\u8ba1\u7b97\u673a\u4f53\u7cfb\u7684\u9e1f\u77b0\u56fe\uff0c\u662f\u5927\u6709\u88e8\u76ca\u7684\u3002 \u4e13\u4e1a\u8bfe \u5f53\u7136\uff0c\u5982\u679c\u60f3\u6df1\u5165\u73b0\u4ee3\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784\u7684\u590d\u6742\u7ec6\u8282\uff0c\u8fd8\u5f97\u4e0a\u4e00\u95e8\u5927\u5b66\u672c\u79d1\u96be\u5ea6\u7684\u8bfe\u7a0b UCB CS61C: Great Ideas in Computer Architecture \u3002UC Berkeley \u4f5c\u4e3a RISC-V \u67b6\u6784\u7684\u53d1\u6e90\u5730\uff0c\u5728\u4f53\u7cfb\u7ed3\u6784\u9886\u57df\u7b97\u5f97\u4e0a\u9996\u5c48\u4e00\u6307\u3002\u5176\u8bfe\u7a0b\u975e\u5e38\u6ce8\u91cd\u5b9e\u8df5\uff0c\u4f60\u4f1a\u5728 Project \u4e2d\u624b\u5199\u6c47\u7f16\u6784\u9020\u795e\u7ecf\u7f51\u7edc\uff0c\u4ece\u96f6\u5f00\u59cb\u642d\u5efa\u4e00\u4e2a CPU\uff0c\u8fd9\u4e9b\u5b9e\u8df5\u90fd\u4f1a\u8ba9\u4f60\u5bf9\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784\u6709\u66f4\u4e3a\u6df1\u5165\u7684\u7406\u89e3\uff0c\u800c\u4e0d\u662f\u4ec5\u505c\u7559\u4e8e\u201c\u53d6\u6307\u8bd1\u7801\u6267\u884c\u8bbf\u5b58\u5199\u56de\u201d\u7684\u5355\u8c03\u80cc\u8bf5\u91cc\u3002 \u7cfb\u7edf\u5165\u95e8 \u8ba1\u7b97\u673a\u7cfb\u7edf\u662f\u4e00\u4e2a\u5e9e\u6742\u800c\u6df1\u523b\u7684\u4e3b\u9898\uff0c\u5728\u6df1\u5165\u5b66\u4e60\u67d0\u4e2a\u7ec6\u5206\u9886\u57df\u4e4b\u524d\uff0c\u5bf9\u5404\u4e2a\u9886\u57df\u6709\u4e00\u4e2a\u5b8f\u89c2\u6982\u5ff5\u6027\u7684\u7406\u89e3\uff0c\u5bf9\u4e00\u4e9b\u901a\u7528\u6027\u7684\u8bbe\u8ba1\u539f\u5219\u6709\u6240\u77e5\u6653\uff0c\u4f1a\u8ba9\u4f60\u5728\u4e4b\u540e\u7684\u6df1\u5165\u5b66\u4e60\u4e2d\u4e0d\u65ad\u5f3a\u5316\u4e00\u4e9b\u6700\u4e3a\u6838\u5fc3\u4e43\u81f3\u54f2\u5b66\u7684\u6982\u5ff5\uff0c\u800c\u4e0d\u4f1a\u684e\u688f\u4e8e\u590d\u6742\u7684\u5185\u90e8\u7ec6\u8282\u548c\u5404\u79cd trick\u3002\u56e0\u4e3a\u5728\u6211\u770b\u6765\uff0c\u5b66\u4e60\u7cfb\u7edf\u6700\u5173\u952e\u7684\u8fd8\u662f\u60f3\u8ba9\u4f60\u9886\u609f\u5230\u8fd9\u4e9b\u6700\u6838\u5fc3\u7684\u4e1c\u897f\uff0c\u4ece\u800c\u80fd\u591f\u8bbe\u8ba1\u548c\u5b9e\u73b0\u51fa\u5c5e\u4e8e\u81ea\u5df1\u7684\u7cfb\u7edf\u3002 MIT6.033: System Engineering \u662f MIT \u7684\u7cfb\u7edf\u5165\u95e8\u8bfe\uff0c\u4e3b\u9898\u6d89\u53ca\u4e86\u64cd\u4f5c\u7cfb\u7edf\u3001\u7f51\u7edc\u3001\u5206\u5e03\u5f0f\u548c\u7cfb\u7edf\u5b89\u5168\uff0c\u9664\u4e86\u77e5\u8bc6\u70b9\u7684\u4f20\u6388\u5916\uff0c\u8fd9\u95e8\u8bfe\u8fd8\u4f1a\u8bb2\u6388\u4e00\u4e9b\u5199\u4f5c\u548c\u8868\u8fbe\u4e0a\u7684\u6280\u5de7\uff0c\u8ba9\u4f60\u5b66\u4f1a\u5982\u4f55\u8bbe\u8ba1\u5e76\u5411\u522b\u4eba\u4ecb\u7ecd\u548c\u5206\u6790\u81ea\u5df1\u7684\u7cfb\u7edf\u3002\u8fd9\u672c\u4e66\u914d\u5957\u7684\u6559\u6750 Principles of Computer System Design: An Introduction \u4e5f\u5199\u5f97\u975e\u5e38\u597d\uff0c\u63a8\u8350\u5927\u5bb6\u9605\u8bfb\u3002 CMU 15-213: Introduction to Computer System \u662f CMU 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OCaml","title":"OCaml"},{"location":"CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_20","text":"","title":"\u7535\u5b50\u57fa\u7840"},{"location":"CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_21","text":"\u4f5c\u4e3a\u8ba1\u7b97\u673a\u7cfb\u7684\u5b66\u751f\uff0c\u4e86\u89e3\u4e00\u4e9b\u57fa\u7840\u7684\u7535\u8def\u77e5\u8bc6\uff0c\u611f\u53d7\u4ece\u4f20\u611f\u5668\u6536\u96c6\u6570\u636e\u5230\u6570\u636e\u5206\u6790\u518d\u5230\u7b97\u6cd5\u9884\u6d4b\u6574\u6761\u6d41\u6c34\u7ebf\uff0c\u5bf9\u4e8e\u540e\u7eed\u77e5\u8bc6\u7684\u5b66\u4e60\u4ee5\u53ca\u8ba1\u7b97\u601d\u7ef4\u7684\u57f9\u517b\u8fd8\u662f\u5f88\u6709\u5e2e\u52a9\u7684\u3002 EE16A&B: Designing Information Devices and Systems I&II \u662f\u4f2f\u514b\u5229 EE \u5b66\u751f\u7684\u5927\u4e00\u5165\u95e8\u8bfe\uff0c\u5176\u4e2d EE16A \u6ce8\u91cd\u901a\u8fc7\u7535\u8def\u4ece\u5b9e\u9645\u73af\u5883\u4e2d\u6536\u96c6\u548c\u5206\u6790\u6570\u636e\uff0c\u800c EE16B 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construction.","title":"\u57f9\u517b\u65b9\u6848Pro"},{"location":"%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/","text":"\u597d\u4e66\u63a8\u8350 \u7531\u4e8e\u7248\u6743\u539f\u56e0\uff0c\u4e0b\u9762\u5217\u4e3e\u7684\u56fe\u4e66\u4e2d\u9664\u4e86\u5f00\u6e90\u8d44\u6e90\u63d0\u4f9b\u4e86\u94fe\u63a5\uff0c\u5176\u4ed6\u7684\u8d44\u6e90\u8bf7\u5927\u5bb6\u81ea\u884c\u901a\u8fc7 libgen \u6216 z-lib \u67e5\u627e\u3002 \u8d44\u6e90\u6c47\u603b Free Programming Books : \u5f00\u6e90\u7f16\u7a0b\u4e66\u7c4d\u8d44\u6e90\u6c47\u603b CS Textbook Recommendations : \u8ba1\u7b97\u673a\u79d1\u5b66\u65b9\u5411\u63a8\u8350\u6559\u6750\u5217\u8868 C Book Guide and List : C\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868 C++ Book Guide and List : C++\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868 Python Book Guide and List : Python\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868 \u7cfb\u7edf\u5165\u95e8 Computer Systems: A Programmer's Perspective [ \u8c46\u74e3 ] Principles of Computer System Design: An Introduction [ \u8c46\u74e3 ] \u64cd\u4f5c\u7cfb\u7edf \u73b0\u4ee3\u64cd\u4f5c\u7cfb\u7edf: \u539f\u7406\u4e0e\u5b9e\u73b0 [ \u8c46\u74e3 ] Operating Systems: Three Easy Pieces [ \u8c46\u74e3 ] Modern Operating Systems [ \u8c46\u74e3 ] Operating Systems: Principles and Practice [ \u8c46\u74e3 ] \u8ba1\u7b97\u673a\u7f51\u7edc Computer Networks: A Systems Approach [ \u8c46\u74e3 ] Computer Networking: A Top-Down Approach [ \u8c46\u74e3 ] \u5206\u5e03\u5f0f\u7cfb\u7edf Patterns of Distributed System (Blog) Distributed Systems for Fun and Profit (Blog) Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems [ \u8c46\u74e3 ] \u6570\u636e\u5e93\u7cfb\u7edf Architecture of a Database System [ \u8c46\u74e3 ] Readings in Database Systems [ \u8c46\u74e3 ] Database System Concepts [ \u8c46\u74e3 ] \u7f16\u8bd1\u539f\u7406 Engineering a Compiler [ \u8c46\u74e3 ] Compilers: Principles, Techniques, and Tools [ \u8c46\u74e3 ] \u8ba1\u7b97\u673a\u7f16\u7a0b\u8bed\u8a00 \u8ba1\u7b97\u673a\u7a0b\u5e8f\u7684\u6784\u9020\u548c\u89e3\u91ca [ \u8c46\u74e3 ] Essentials of Programming Languages [ \u8c46\u74e3 ] Practical Foundations for Programming Languages [ \u8c46\u74e3 ] Software Foundations [ \u8c46\u74e3 ] [ \u5317\u5927\u76f8\u5173\u8bfe\u7a0b ] Types and Programming Languages [ \u8c46\u74e3 ] [ \u5317\u5927\u76f8\u5173\u8bfe\u7a0b ] \u4f53\u7cfb\u7ed3\u6784 \u8d85\u6807\u91cf\u5904\u7406\u5668\u8bbe\u8ba1: Superscalar RISC Processor Design [ \u8c46\u74e3 ] Computer Organization and Design RISC-V Edition [ \u8c46\u74e3 ] Computer Organization and Design: The Hardware/Software Interface [ \u8c46\u74e3 ] Computer Architecture: A Quantitative Approach [ \u8c46\u74e3 ] \u7406\u8bba\u8ba1\u7b97\u673a\u79d1\u5b66 Introduction to the Theory of Computation [ \u8c46\u74e3 ] \u5bc6\u7801\u5b66 Cryptography Engineering: Design Principles and Practical Applications [ \u8c46\u74e3 ] Introduction to Modern Cryptography [ \u8c46\u74e3 ] \u9006\u5411\u5de5\u7a0b \u9006\u5411\u5de5\u7a0b\u6838\u5fc3\u539f\u7406 [ \u8c46\u74e3 ] \u52a0\u5bc6\u4e0e\u89e3\u5bc6 [ \u8c46\u74e3 ] \u8ba1\u7b97\u673a\u56fe\u5f62\u5b66 Monte Carlo theory, methods and examples Advanced Global Illumination [ \u8c46\u74e3 ] Fundamentals of Computer Graphics [ \u8c46\u74e3 ] Fluid Simulation for Computer Graphics [ \u8c46\u74e3 ] Physically Based Rendering: From Theory To Implementation [ \u8c46\u74e3 ] Real-Time Rendering [ \u8c46\u74e3 ] \u6e38\u620f\u5f15\u64ce \u6e38\u620f\u7f16\u7a0b\u6a21\u5f0f: Game Programming Patterns [ \u8c46\u74e3 ] \u5b9e\u65f6\u78b0\u649e\u68c0\u6d4b\u7b97\u6cd5\u6280\u672f [ \u8c46\u74e3 ] Game AI Pro Series [ \u8c46\u74e3 ] Artificial Intelligence for Games [ \u8c46\u74e3 ] Game Engine Architecture [ \u8c46\u74e3 ] Game Programming Gems Series [ \u8c46\u74e3 ] \u8f6f\u4ef6\u5de5\u7a0b Software Engineering at Google [ \u8c46\u74e3 ] \u8bbe\u8ba1\u6a21\u5f0f \u8bbe\u8ba1\u6a21\u5f0f: \u53ef\u590d\u7528\u9762\u5411\u5bf9\u8c61\u8f6f\u4ef6\u7684\u57fa\u7840 [ \u8c46\u74e3 ] \u5927\u8bdd\u8bbe\u8ba1\u6a21\u5f0f [ \u8c46\u74e3 ] Head First \u8bbe\u8ba1\u6a21\u5f0f [ \u8c46\u74e3 ] \u6df1\u5ea6\u5b66\u4e60 \u52a8\u624b\u5b66\u6df1\u5ea6\u5b66\u4e60 [ \u8c46\u74e3 ] \u795e\u7ecf\u7f51\u7edc\u4e0e\u6df1\u5ea6\u5b66\u4e60 [ \u8c46\u74e3 ] \u6df1\u5ea6\u5b66\u4e60\u5165\u95e8 [ \u8c46\u74e3 ] \u7b80\u5355\u7c97\u66b4 TensorFlow 2 (Tutorial) Speech and Language Processing [ \u8c46\u74e3 ] \u8ba1\u7b97\u673a\u89c6\u89c9 Multiple View Geometry in Computer Vision [ \u8c46\u74e3 ] \u673a\u5668\u4eba Probabilistic Robotics [ \u8c46\u74e3 ] \u9762\u8bd5 \u5251\u6307 Offer\uff1a\u540d\u4f01\u9762\u8bd5\u5b98\u7cbe\u8bb2\u5178\u578b\u7f16\u7a0b\u9898 [ \u8c46\u74e3 ] Cracking The Coding Interview [ \u8c46\u74e3 ]","title":"\u597d\u4e66\u63a8\u8350"},{"location":"%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_1","text":"\u7531\u4e8e\u7248\u6743\u539f\u56e0\uff0c\u4e0b\u9762\u5217\u4e3e\u7684\u56fe\u4e66\u4e2d\u9664\u4e86\u5f00\u6e90\u8d44\u6e90\u63d0\u4f9b\u4e86\u94fe\u63a5\uff0c\u5176\u4ed6\u7684\u8d44\u6e90\u8bf7\u5927\u5bb6\u81ea\u884c\u901a\u8fc7 libgen \u6216 z-lib \u67e5\u627e\u3002","title":"\u597d\u4e66\u63a8\u8350"},{"location":"%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_2","text":"Free Programming Books : \u5f00\u6e90\u7f16\u7a0b\u4e66\u7c4d\u8d44\u6e90\u6c47\u603b CS Textbook Recommendations : \u8ba1\u7b97\u673a\u79d1\u5b66\u65b9\u5411\u63a8\u8350\u6559\u6750\u5217\u8868 C Book Guide and List : C\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868 C++ Book Guide and List : C++\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868 Python Book Guide and List : 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Distributed System (Blog) Distributed Systems for Fun and Profit (Blog) Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems [ \u8c46\u74e3 ]","title":"\u5206\u5e03\u5f0f\u7cfb\u7edf"},{"location":"%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_7","text":"Architecture of a Database System [ \u8c46\u74e3 ] Readings in Database Systems [ \u8c46\u74e3 ] Database System Concepts [ \u8c46\u74e3 ]","title":"\u6570\u636e\u5e93\u7cfb\u7edf"},{"location":"%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_8","text":"Engineering a Compiler [ \u8c46\u74e3 ] Compilers: Principles, Techniques, and Tools [ \u8c46\u74e3 ]","title":"\u7f16\u8bd1\u539f\u7406"},{"location":"%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_9","text":"\u8ba1\u7b97\u673a\u7a0b\u5e8f\u7684\u6784\u9020\u548c\u89e3\u91ca [ \u8c46\u74e3 ] Essentials of Programming Languages [ \u8c46\u74e3 ] Practical Foundations for Programming Languages [ \u8c46\u74e3 ] Software Foundations [ \u8c46\u74e3 ] [ 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Bryant \u6559\u6388\u6267\u7b14\uff0c\u4e5f\u5373\u6240\u8c13\u7684 CSAPP\u3002\u8fd9\u4e5f\u662f\u6211\u7b2c\u4e00\u672c\u8ba4\u8ba4\u771f\u771f\u4e00\u9875\u4e00\u9875\u8bfb\u8fc7\u53bb\u7684\u8ba1\u7b97\u673a\u6559\u6750\uff0c\u867d\u7136\u5f88\u96be\u5543\uff0c\u4f46\u7740\u5b9e\u6536\u83b7\u826f\u591a\u3002 \u5317\u5927\u8d2d\u4e70\u4e86\u8fd9\u95e8\u8bfe\u7684\u7248\u6743\u5e76\u5f00\u8bbe\u4e86 Introduction to Computer System \u8fd9\u95e8\u8bfe\uff0c\u4f46\u5176\u5b9e CSAPP \u6240\u6709\u7684\u8bfe\u7a0b\u8d44\u6e90\u548c\u5b9e\u9a8c\u4ee3\u7801\u90fd\u80fd\u5728\u5b83\u7684\u5b98\u65b9\u4e3b\u9875\u4e0a\u8bbf\u95ee\u5230\uff08\u5177\u4f53\u53c2\u89c1\u4e0b\u65b9\u94fe\u63a5\uff09\u3002 \u8fd9\u95e8\u8bfe\u7531\u4e8e\u8fc7\u4e8e\u51fa\u540d\uff0c\u5168\u4e16\u754c\u7684\u7801\u519c\u4e89\u76f8\u5b66\u4e60\uff0c\u5bfc\u81f4\u5176 Project 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\u5199\u4f5c\u4e1a\u4e5f\u662f\u4e00\u4e2a\u4e0d\u9519\u7684\u9009\u62e9\u3002","title":"LaTeX"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/LaTeX/#latex","text":"","title":"LaTeX"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/LaTeX/#latex_1","text":"\u5982\u679c\u4f60\u9700\u8981\u5199\u8bba\u6587\uff0c\u90a3\u4e48\u8bf7\u76f4\u63a5\u8df3\u5230\u4e0b\u4e00\u8282\uff0c\u56e0\u4e3a\u4f60\u4e0d\u5b66\u4e5f\u5f97\u5b66\u3002 LaTeX \u662f\u4e00\u79cd\u57fa\u4e8e TeX \u7684\u6392\u7248\u7cfb\u7edf\uff0c\u7531\u56fe\u7075\u5956\u5f97\u4e3b Lamport \u5f00\u53d1\uff0c\u800c Tex \u5219\u662f\u7531 Knuth \u6700\u521d\u5f00\u53d1\uff0c\u8fd9\u4e24\u4f4d\u90fd\u662f\u8ba1\u7b97\u673a\u754c\u7684\u5de8\u64d8\u3002\u5f53\u7136\u5f00\u53d1\u8005\u5f3a\u5e76\u4e0d\u662f\u6211\u4eec\u5b66\u4e60 LaTeX \u7684\u7406\u7531\uff0cLaTeX \u548c\u5e38\u89c1\u7684\u6240\u89c1\u5373\u6240\u5f97\u7684 Word 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\u73af\u5883\u51fa\u73b0\u4e86\u95ee\u9898\uff0c\u53ef\u4ee5\u8003\u8651\u4f7f\u7528 Overleaf \u8fd9\u4e2a\u5728\u7ebf LaTeX \u7f16\u8f91\u7f51\u7ad9\u3002\u7ad9\u5185\u4e0d\u4ec5\u6709\u5404\u79cd\u5404\u6837\u7684 LaTeX \u6a21\u7248\u4f9b\u4f60\u9009\u62e9\uff0c\u8fd8\u514d\u53bb\u4e86\u73af\u5883\u914d\u7f6e\u7684\u96be\u9898\u3002 \u9605\u8bfb\u4e0b\u9762\u4e09\u7bc7 Tutorial: Part-1 , Part-2 , Part-3 \u3002 \u5b66\u4e60 LaTeX \u6700\u597d\u7684\u65b9\u5f0f\u5f53\u7136\u662f\u5199\u8bba\u6587\uff0c\u4e0d\u8fc7\u4ece\u4e00\u95e8\u6570\u5b66\u8bfe\u5165\u624b\u7528 LaTeX \u5199\u4f5c\u4e1a\u4e5f\u662f\u4e00\u4e2a\u4e0d\u9519\u7684\u9009\u62e9\u3002","title":"\u5982\u4f55\u5b66\u4e60 LaTeX"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Vim/","text":"Vim \u4e3a\u4ec0\u4e48\u5b66\u4e60 Vim \u5728\u6211\u770b\u6765 Vim \u7f16\u8f91\u5668\u6709\u5982\u4e0b\u7684\u597d\u5904\uff1a \u8ba9\u4f60\u7684\u6574\u4e2a\u5f00\u53d1\u8fc7\u7a0b\u624b\u6307\u4e0d\u9700\u8981\u79bb\u5f00\u952e\u76d8\uff0c\u800c\u4e14\u5149\u6807\u7684\u79fb\u52a8\u4e0d\u9700\u8981\u65b9\u5411\u952e\u4f7f\u5f97\u4f60\u7684\u624b\u6307\u4e00\u76f4\u5904\u5728\u6253\u5b57\u7684\u6700\u4f73\u4f4d\u7f6e\u3002 \u65b9\u4fbf\u7684\u6587\u4ef6\u5207\u6362\u4ee5\u53ca\u9762\u677f\u63a7\u5236\u53ef\u4ee5\u8ba9\u4f60\u540c\u65f6\u5f00\u53d1\u591a\u4efd\u6587\u4ef6\u751a\u81f3\u540c\u4e00\u4e2a\u6587\u4ef6\u7684\u4e0d\u540c\u4f4d\u7f6e\u3002 Vim \u7684\u5b8f\u64cd\u4f5c\u53ef\u4ee5\u6279\u91cf\u5316\u5904\u7406\u91cd\u590d\u64cd\u4f5c\uff08\u4f8b\u5982\u591a\u884c tab\uff0c\u6279\u91cf\u52a0\u53cc\u5f15\u53f7\u7b49\u7b49\uff09 Vim \u662f\u5f88\u591a\u670d\u52a1\u5668\u81ea\u5e26\u7684\u547d\u4ee4\u884c\u7f16\u8f91\u5668\uff0c\u5f53\u4f60\u901a\u8fc7 ssh \u8fde\u63a5\u8fdc\u7a0b\u670d\u52a1\u5668\u4e4b\u540e\uff0c\u7531\u4e8e\u6ca1\u6709\u56fe\u5f62\u754c\u9762\uff0c\u53ea\u80fd\u5728\u547d\u4ee4\u884c\u91cc\u8fdb\u884c\u5f00\u53d1\uff08\u5f53\u7136\u73b0\u5728\u5f88\u591a IDE \u5982 VS Code \u63d0\u4f9b\u4e86 ssh \u63d2\u4ef6\u53ef\u4ee5\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\uff09\u3002 \u5f02\u5e38\u4e30\u5bcc\u7684\u63d2\u4ef6\u751f\u6001\uff0c\u8ba9\u4f60\u62e5\u6709\u4e16\u754c\u4e0a\u6700\u82b1\u91cc\u80e1\u54e8\u7684\u547d\u4ee4\u884c\u7f16\u8f91\u5668\u3002 \u5982\u4f55\u5b66\u4e60 Vim \u4e0d\u5e78\u7684\u662f Vim \u7684\u5b66\u4e60\u66f2\u7ebf\u786e\u5b9e\u76f8\u5f53\u9661\u5ced\uff0c\u6211\u82b1\u4e86\u597d\u51e0\u4e2a\u661f\u671f\u624d\u6162\u6162\u9002\u5e94\u4e86\u7528 Vim \u8fdb\u884c\u5f00\u53d1\u7684\u8fc7\u7a0b\u3002\u6700\u5f00\u59cb\u4f60\u4f1a\u89c9\u5f97\u975e\u5e38\u4e0d\u9002\u5e94\uff0c\u4f46\u4e00\u65e6\u71ac\u8fc7\u4e86\u521d\u59cb\u9636\u6bb5\uff0c\u76f8\u4fe1\u6211\uff0c\u4f60\u4f1a\u7231\u4e0a Vim\u3002 Vim \u7684\u5b66\u4e60\u8d44\u6599\u6d69\u5982\u70df\u6d77\uff0c\u4f46\u638c\u63e1\u5b83\u6700\u597d\u7684\u65b9\u5f0f\u8fd8\u662f\u5c06\u5b83\u7528\u5728\u65e5\u5e38\u7684\u5f00\u53d1\u8fc7\u7a0b\u4e2d\uff0c\u800c\u4e0d\u662f\u4e00\u4e0a\u6765\u5c31\u53bb\u5b66\u5404\u79cd\u82b1\u91cc\u80e1\u54e8\u7684\u9ad8\u7ea7 Vim \u6280\u5de7\u3002\u4e2a\u4eba\u63a8\u8350\u7684\u5b66\u4e60\u8def\u7ebf\u5982\u4e0b\uff1a \u5148\u9605\u8bfb \u8fd9\u7bc7 tutorial \uff0c\u638c\u63e1\u57fa\u672c\u7684 Vim \u6982\u5ff5\u548c\u4f7f\u7528\u65b9\u5f0f\u3002 \u7528 Vim \u81ea\u5e26\u7684 vimtutor \u8fdb\u884c\u7ec3\u4e60\uff0c\u5b89\u88c5\u5b8c Vim \u4e4b\u540e\u76f4\u63a5\u5728\u547d\u4ee4\u884c\u91cc\u8f93\u5165 vimtutor \u5373\u53ef\u8fdb\u5165\u7ec3\u4e60\u7a0b\u5e8f\u3002 \u6700\u540e\u5c31\u662f\u5f3a\u8feb\u81ea\u5df1\u4f7f\u7528 Vim \u8fdb\u884c\u5f00\u53d1\uff0cIDE \u91cc\u53ef\u4ee5\u5b89\u88c5 Vim \u63d2\u4ef6\u3002 \u7b49\u4f60\u5b8c\u5168\u9002\u5e94 Vim \u4e4b\u540e\u65b0\u7684\u4e16\u754c\u4fbf\u5411\u4f60\u655e\u5f00\u4e86\u5927\u95e8\uff0c\u4f60\u53ef\u4ee5\u6309\u9700\u914d\u7f6e\u81ea\u5df1\u7684 Vim\uff08\u4fee\u6539 .vimrc \u6587\u4ef6\uff09\uff0c\u7f51\u4e0a\u6709\u6570\u4e0d\u80dc\u6570\u7684\u8d44\u6e90\u53ef\u4ee5\u501f\u9274\u3002 \u63a8\u8350\u53c2\u8003\u8d44\u6599 Neil, Drew. Practical Vim: Edit Text at the Speed of Thought. N.p., Pragmatic Bookshelf, 2015. Neil, Drew. Modern Vim: Craft Your Development Environment with Vim 8 and Neovim. United States, Pragmatic Bookshelf.","title":"Vim"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Vim/#vim","text":"","title":"Vim"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Vim/#vim_1","text":"\u5728\u6211\u770b\u6765 Vim \u7f16\u8f91\u5668\u6709\u5982\u4e0b\u7684\u597d\u5904\uff1a \u8ba9\u4f60\u7684\u6574\u4e2a\u5f00\u53d1\u8fc7\u7a0b\u624b\u6307\u4e0d\u9700\u8981\u79bb\u5f00\u952e\u76d8\uff0c\u800c\u4e14\u5149\u6807\u7684\u79fb\u52a8\u4e0d\u9700\u8981\u65b9\u5411\u952e\u4f7f\u5f97\u4f60\u7684\u624b\u6307\u4e00\u76f4\u5904\u5728\u6253\u5b57\u7684\u6700\u4f73\u4f4d\u7f6e\u3002 \u65b9\u4fbf\u7684\u6587\u4ef6\u5207\u6362\u4ee5\u53ca\u9762\u677f\u63a7\u5236\u53ef\u4ee5\u8ba9\u4f60\u540c\u65f6\u5f00\u53d1\u591a\u4efd\u6587\u4ef6\u751a\u81f3\u540c\u4e00\u4e2a\u6587\u4ef6\u7684\u4e0d\u540c\u4f4d\u7f6e\u3002 Vim \u7684\u5b8f\u64cd\u4f5c\u53ef\u4ee5\u6279\u91cf\u5316\u5904\u7406\u91cd\u590d\u64cd\u4f5c\uff08\u4f8b\u5982\u591a\u884c tab\uff0c\u6279\u91cf\u52a0\u53cc\u5f15\u53f7\u7b49\u7b49\uff09 Vim \u662f\u5f88\u591a\u670d\u52a1\u5668\u81ea\u5e26\u7684\u547d\u4ee4\u884c\u7f16\u8f91\u5668\uff0c\u5f53\u4f60\u901a\u8fc7 ssh \u8fde\u63a5\u8fdc\u7a0b\u670d\u52a1\u5668\u4e4b\u540e\uff0c\u7531\u4e8e\u6ca1\u6709\u56fe\u5f62\u754c\u9762\uff0c\u53ea\u80fd\u5728\u547d\u4ee4\u884c\u91cc\u8fdb\u884c\u5f00\u53d1\uff08\u5f53\u7136\u73b0\u5728\u5f88\u591a IDE \u5982 VS Code \u63d0\u4f9b\u4e86 ssh \u63d2\u4ef6\u53ef\u4ee5\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\uff09\u3002 \u5f02\u5e38\u4e30\u5bcc\u7684\u63d2\u4ef6\u751f\u6001\uff0c\u8ba9\u4f60\u62e5\u6709\u4e16\u754c\u4e0a\u6700\u82b1\u91cc\u80e1\u54e8\u7684\u547d\u4ee4\u884c\u7f16\u8f91\u5668\u3002","title":"\u4e3a\u4ec0\u4e48\u5b66\u4e60 Vim"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Vim/#vim_2","text":"\u4e0d\u5e78\u7684\u662f Vim \u7684\u5b66\u4e60\u66f2\u7ebf\u786e\u5b9e\u76f8\u5f53\u9661\u5ced\uff0c\u6211\u82b1\u4e86\u597d\u51e0\u4e2a\u661f\u671f\u624d\u6162\u6162\u9002\u5e94\u4e86\u7528 Vim \u8fdb\u884c\u5f00\u53d1\u7684\u8fc7\u7a0b\u3002\u6700\u5f00\u59cb\u4f60\u4f1a\u89c9\u5f97\u975e\u5e38\u4e0d\u9002\u5e94\uff0c\u4f46\u4e00\u65e6\u71ac\u8fc7\u4e86\u521d\u59cb\u9636\u6bb5\uff0c\u76f8\u4fe1\u6211\uff0c\u4f60\u4f1a\u7231\u4e0a Vim\u3002 Vim \u7684\u5b66\u4e60\u8d44\u6599\u6d69\u5982\u70df\u6d77\uff0c\u4f46\u638c\u63e1\u5b83\u6700\u597d\u7684\u65b9\u5f0f\u8fd8\u662f\u5c06\u5b83\u7528\u5728\u65e5\u5e38\u7684\u5f00\u53d1\u8fc7\u7a0b\u4e2d\uff0c\u800c\u4e0d\u662f\u4e00\u4e0a\u6765\u5c31\u53bb\u5b66\u5404\u79cd\u82b1\u91cc\u80e1\u54e8\u7684\u9ad8\u7ea7 Vim \u6280\u5de7\u3002\u4e2a\u4eba\u63a8\u8350\u7684\u5b66\u4e60\u8def\u7ebf\u5982\u4e0b\uff1a \u5148\u9605\u8bfb \u8fd9\u7bc7 tutorial \uff0c\u638c\u63e1\u57fa\u672c\u7684 Vim \u6982\u5ff5\u548c\u4f7f\u7528\u65b9\u5f0f\u3002 \u7528 Vim \u81ea\u5e26\u7684 vimtutor \u8fdb\u884c\u7ec3\u4e60\uff0c\u5b89\u88c5\u5b8c Vim \u4e4b\u540e\u76f4\u63a5\u5728\u547d\u4ee4\u884c\u91cc\u8f93\u5165 vimtutor \u5373\u53ef\u8fdb\u5165\u7ec3\u4e60\u7a0b\u5e8f\u3002 \u6700\u540e\u5c31\u662f\u5f3a\u8feb\u81ea\u5df1\u4f7f\u7528 Vim \u8fdb\u884c\u5f00\u53d1\uff0cIDE \u91cc\u53ef\u4ee5\u5b89\u88c5 Vim \u63d2\u4ef6\u3002 \u7b49\u4f60\u5b8c\u5168\u9002\u5e94 Vim \u4e4b\u540e\u65b0\u7684\u4e16\u754c\u4fbf\u5411\u4f60\u655e\u5f00\u4e86\u5927\u95e8\uff0c\u4f60\u53ef\u4ee5\u6309\u9700\u914d\u7f6e\u81ea\u5df1\u7684 Vim\uff08\u4fee\u6539 .vimrc \u6587\u4ef6\uff09\uff0c\u7f51\u4e0a\u6709\u6570\u4e0d\u80dc\u6570\u7684\u8d44\u6e90\u53ef\u4ee5\u501f\u9274\u3002","title":"\u5982\u4f55\u5b66\u4e60 Vim"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Vim/#_1","text":"Neil, Drew. Practical Vim: Edit Text at the Speed of Thought. N.p., Pragmatic Bookshelf, 2015. Neil, Drew. Modern Vim: Craft Your Development Environment with Vim 8 and Neovim. United States, Pragmatic Bookshelf.","title":"\u63a8\u8350\u53c2\u8003\u8d44\u6599"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/thesis/","text":"\u6bd5\u4e1a\u8bba\u6587 \u4e3a\u4ec0\u4e48\u5199\u8fd9\u4efd\u6559\u7a0b 2022\u5e74\uff0c\u6211\u672c\u79d1\u6bd5\u4e1a\u4e86\u3002\u5728\u5f00\u59cb\u52a8\u624b\u5199\u6bd5\u4e1a\u8bba\u6587\u7684\u65f6\u5019\uff0c\u6211\u5c34\u5c2c\u5730\u53d1\u73b0\uff0c\u6211\u5bf9 Word \u7684\u638c\u63e1\u7a0b\u5ea6\u4ec5\u9650\u4e8e\u8c03\u8282\u5b57\u4f53\u3001\u4fdd\u5b58\u5bfc\u51fa\u8fd9\u4e9b\u50bb\u74dc\u529f\u80fd\u3002\u66fe\u60f3\u8f6c\u6218 Latex\uff0c\u4f46\u8bba\u6587\u7684\u6bb5\u843d\u683c\u5f0f\u8981\u6c42\u8c03\u6574\u8d77\u6765\u8fd8\u662f\u7528 Word 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\uff0c\u5e76\u6309\u7167\u5176\u8981\u6c42\u5236\u4f5c\u4e86 \u4e00\u4efd\u6a21\u7248 \uff0c\u5927\u5bb6\u9700\u8981\u7684\u8bdd\u81ea\u53d6\uff0c\u672c\u4eba\u4e0d\u627f\u62c5\u65e0\u6cd5\u6bd5\u4e1a\u7b49\u4efb\u4f55\u8d23\u4efb\u3002 \u5b66\u4e60 Word \u6392\u7248\uff1a\u5230\u8fbe\u8fd9\u4e00\u6b65\u7684\u7ae5\u978b\u5206\u4e3a\u4e24\u7c7b\uff0c\u4e00\u662f\u5df2\u7ecf\u62e5\u6709\u4e86\u5b66\u9662\u63d0\u4f9b\u7684\u6807\u51c6\u6a21\u7248\uff0c\u4e8c\u662f\u53ea\u6709\u4e00\u4efd\u865a\u65e0\u7f25\u7f08\u7684\u683c\u5f0f\u8981\u6c42\u3002\u90a3\u73b0\u5728\u5f53\u52a1\u4e4b\u6025\u5c31\u662f\u5b66\u4e60\u57fa\u7840\u7684 Word \u6392\u7248\u6280\u672f\uff0c\u5bf9\u4e8e\u524d\u8005\u53ef\u4ee5\u5b66\u4f1a\u4f7f\u7528\u6a21\u7248\uff0c\u5bf9\u4e8e\u540e\u8005\u5219\u53ef\u4ee5\u5b66\u4f1a\u5236\u4f5c\u6a21\u7248\u3002\u6b64\u65f6\u5207\u8bb0\u4e0d\u8981\u96c4\u5fc3\u52c3\u52c3\u5730\u9009\u62e9\u4e00\u4e2a\u5341\u51e0\u4e2a\u5c0f\u65f6\u7684 Word \u6559\u5b66\u89c6\u9891\u5f00\u59cb\u5934\u60ac\u6881\u9525\u523a\u80a1\uff0c\u56e0\u4e3a\u751f\u4ea7\u4e00\u4efd\u5e94\u4ed8\u6bd5\u4e1a\u7684\u5b66\u672f\u5783\u573e\u53ea\u8981\u5b66\u534a\u5c0f\u65f6\u80fd\u4e0a\u624b\u5c31\u591f\u4e86\u3002\u6211\u5f53\u65f6\u770b\u7684 \u4e00\u4e2a B \u7ad9\u7684\u6559\u5b66\u89c6\u9891 \uff0c\u77ed\u5c0f\u7cbe\u608d\u975e\u5e38\u5b9e\u7528\uff0c\u5168\u957f\u534a\u5c0f\u65f6\u6781\u901f\u5165\u95e8\u3002 \u751f\u4ea7\u5b66\u672f\u5783\u573e\uff1a\u6700\u5bb9\u6613\u7684\u4e00\u6b65\uff0c\u5927\u5bb6\u516b\u4ed9\u8fc7\u6d77\uff0c\u5404\u663e\u795e\u901a\u5427\uff0c\u795d\u5927\u5bb6\u6bd5\u4e1a\u987a\u5229\uff5e\uff5e","title":"\u5982\u4f55\u7528 Word \u5199\u6bd5\u4e1a\u8bba\u6587"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/","text":"\u5b9e\u7528\u5de5\u5177\u7bb1 \u4e0b\u8f7d\u5de5\u5177 Libgen : PDF\u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9\u3002 z-epub : ePub\u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9\u3002 bitdownloader : \u6cb9\u7ba1\u89c6\u9891\u4e0b\u8f7d\u5668\u3002 zlibrary : \u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9(\u53ef\u80fd\u9700\u8981\u7ffb\u5899)\u3002 \u8bbe\u8ba1\u5de5\u5177 excalidraw : \u4e00\u6b3e\u624b\u7ed8\u98ce\u683c\u7684\u7ed8\u56fe\u5de5\u5177\uff0c\u975e\u5e38\u9002\u5408\u7ed8\u5236\u8bfe\u7a0b\u62a5\u544a\u6216\u8005PPT\u5185\u7684\u793a\u610f\u56fe\u3002 origamiway : \u624b\u628a\u624b\u6559\u4f60\u600e\u4e48\u6298\u7eb8\u3002 thingiverse : \u56ca\u62ec\u5404\u7c7b 2D/3D \u8bbe\u8ba1\u8d44\u6e90\uff0c\u5176 STL \u6587\u4ef6\u4e0b\u8f7d\u53ef\u76f4\u63a5 3D \u6253\u5370\u3002 iconfont : \u56fd\u5185\u6700\u5927\u7684\u56fe\u6807\u548c\u63d2\u753b\u8d44\u6e90\u5e93\uff0c\u53ef\u7528\u4e8e\u5f00\u53d1\u6216\u7ed8\u5236\u7cfb\u7edf\u67b6\u6784\u56fe\u3002 turbosquid : \u53ef\u4ee5\u8d2d\u4e70\u5404\u5f0f\u5404\u6837\u7684\u6a21\u578b\u3002 \u7f16\u7a0b\u76f8\u5173 sqlfiddle : \u4e00\u4e2a\u7b80\u6613\u7684\u5728\u7ebf SQL Playground\u3002 godbolt : \u975e\u5e38\u65b9\u4fbf\u7684\u7f16\u8bd1\u5668\u63a2\u7d22\u5de5\u5177\u3002\u4f60\u53ef\u4ee5\u5199\u4e00\u6bb5 C/C++ \u4ee3\u7801\uff0c\u9009\u62e9\u4e00\u6b3e\u7f16\u8bd1\u5668\uff0c\u7136\u540e\u4fbf\u53ef\u4ee5\u89c2\u5bdf\u751f\u6210\u7684\u5177\u4f53\u6c47\u7f16\u4ee3\u7801\u3002 explainshell : \u4f60\u662f\u5426\u66fe\u4e3a\u4e00\u6bb5 shell \u4ee3\u7801\u7684\u5177\u4f53\u542b\u4e49\u611f\u5230\u56f0\u6270\uff1fmanpage \u770b\u534a\u5929\u8fd8\u662f\u4e0d\u660e\u6240\u4ee5\uff1f\u8bd5\u8bd5\u8fd9\u4e2a\u7f51\u7ad9\uff01 regex101 : \u6b63\u5219\u8868\u8fbe\u5f0f\u8c03\u8bd5\u7f51\u7ad9\uff0c\u652f\u6301\u5404\u79cd\u7f16\u7a0b\u8bed\u8a00\u7684\u5339\u914d\u6807\u51c6\u3002 typingtom : \u9488\u5bf9\u7a0b\u5e8f\u5458\u7684\u6253\u5b57\u7ec3\u4e60/\u6d4b\u901f\u7f51\u7ad9\u3002 \u5b66\u4e60\u7f51\u7ad9 HFS : \u5404\u7c7b\u8f6f\u4ef6\u6559\u7a0b\u3002 os-wiki : \u64cd\u4f5c\u7cfb\u7edf\u6280\u672f\u8d44\u6e90\u767e\u79d1\u5168\u4e66\u3002 Shadertoy : \u7f16\u5199\u5404\u5f0f\u5404\u6837\u7684 shader\u3002 \u6742\u9879 tophub : \u65b0\u95fb\u70ed\u699c\u5408\u96c6\uff08\u7efc\u5408\u4e86\u77e5\u4e4e\u3001\u5fae\u535a\u3001\u767e\u5ea6\u3001\u5fae\u4fe1\u7b49\uff09\u3002 speedtest : \u5728\u7ebf\u7f51\u7edc\u6d4b\u901f\u7f51\u7ad9\u3002 public-apis : \u516c\u5171 API \u5408\u96c6\u5217\u8868\u3002","title":"\u5b9e\u7528\u5de5\u5177\u7bb1"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_1","text":"","title":"\u5b9e\u7528\u5de5\u5177\u7bb1"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_2","text":"Libgen : PDF\u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9\u3002 z-epub : ePub\u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9\u3002 bitdownloader : \u6cb9\u7ba1\u89c6\u9891\u4e0b\u8f7d\u5668\u3002 zlibrary : \u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9(\u53ef\u80fd\u9700\u8981\u7ffb\u5899)\u3002","title":"\u4e0b\u8f7d\u5de5\u5177"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_3","text":"excalidraw : \u4e00\u6b3e\u624b\u7ed8\u98ce\u683c\u7684\u7ed8\u56fe\u5de5\u5177\uff0c\u975e\u5e38\u9002\u5408\u7ed8\u5236\u8bfe\u7a0b\u62a5\u544a\u6216\u8005PPT\u5185\u7684\u793a\u610f\u56fe\u3002 origamiway : \u624b\u628a\u624b\u6559\u4f60\u600e\u4e48\u6298\u7eb8\u3002 thingiverse : \u56ca\u62ec\u5404\u7c7b 2D/3D \u8bbe\u8ba1\u8d44\u6e90\uff0c\u5176 STL \u6587\u4ef6\u4e0b\u8f7d\u53ef\u76f4\u63a5 3D \u6253\u5370\u3002 iconfont : \u56fd\u5185\u6700\u5927\u7684\u56fe\u6807\u548c\u63d2\u753b\u8d44\u6e90\u5e93\uff0c\u53ef\u7528\u4e8e\u5f00\u53d1\u6216\u7ed8\u5236\u7cfb\u7edf\u67b6\u6784\u56fe\u3002 turbosquid : \u53ef\u4ee5\u8d2d\u4e70\u5404\u5f0f\u5404\u6837\u7684\u6a21\u578b\u3002","title":"\u8bbe\u8ba1\u5de5\u5177"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_4","text":"sqlfiddle : \u4e00\u4e2a\u7b80\u6613\u7684\u5728\u7ebf SQL Playground\u3002 godbolt : \u975e\u5e38\u65b9\u4fbf\u7684\u7f16\u8bd1\u5668\u63a2\u7d22\u5de5\u5177\u3002\u4f60\u53ef\u4ee5\u5199\u4e00\u6bb5 C/C++ \u4ee3\u7801\uff0c\u9009\u62e9\u4e00\u6b3e\u7f16\u8bd1\u5668\uff0c\u7136\u540e\u4fbf\u53ef\u4ee5\u89c2\u5bdf\u751f\u6210\u7684\u5177\u4f53\u6c47\u7f16\u4ee3\u7801\u3002 explainshell : \u4f60\u662f\u5426\u66fe\u4e3a\u4e00\u6bb5 shell \u4ee3\u7801\u7684\u5177\u4f53\u542b\u4e49\u611f\u5230\u56f0\u6270\uff1fmanpage \u770b\u534a\u5929\u8fd8\u662f\u4e0d\u660e\u6240\u4ee5\uff1f\u8bd5\u8bd5\u8fd9\u4e2a\u7f51\u7ad9\uff01 regex101 : \u6b63\u5219\u8868\u8fbe\u5f0f\u8c03\u8bd5\u7f51\u7ad9\uff0c\u652f\u6301\u5404\u79cd\u7f16\u7a0b\u8bed\u8a00\u7684\u5339\u914d\u6807\u51c6\u3002 typingtom : \u9488\u5bf9\u7a0b\u5e8f\u5458\u7684\u6253\u5b57\u7ec3\u4e60/\u6d4b\u901f\u7f51\u7ad9\u3002","title":"\u7f16\u7a0b\u76f8\u5173"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_5","text":"HFS : \u5404\u7c7b\u8f6f\u4ef6\u6559\u7a0b\u3002 os-wiki : \u64cd\u4f5c\u7cfb\u7edf\u6280\u672f\u8d44\u6e90\u767e\u79d1\u5168\u4e66\u3002 Shadertoy : \u7f16\u5199\u5404\u5f0f\u5404\u6837\u7684 shader\u3002","title":"\u5b66\u4e60\u7f51\u7ad9"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_6","text":"tophub : \u65b0\u95fb\u70ed\u699c\u5408\u96c6\uff08\u7efc\u5408\u4e86\u77e5\u4e4e\u3001\u5fae\u535a\u3001\u767e\u5ea6\u3001\u5fae\u4fe1\u7b49\uff09\u3002 speedtest : \u5728\u7ebf\u7f51\u7edc\u6d4b\u901f\u7f51\u7ad9\u3002 public-apis : \u516c\u5171 API \u5408\u96c6\u5217\u8868\u3002","title":"\u6742\u9879"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/%E7%BF%BB%E5%A2%99/","text":"\u7ffb\u5899 \u6b64\u94fe\u63a5 \u51fa\u73b0\u5728\u8fd9\u91cc\u7eaf\u5c5e\u4e8c\u8fdb\u5236 bit \u7684\u968f\u610f\u7ec4\u5408\uff0c\u4e0e\u672c\u4eba\u6beb\u65e0\u5173\u7cfb\u3002","title":"\u7ffb\u5899"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/%E7%BF%BB%E5%A2%99/#_1","text":"\u6b64\u94fe\u63a5 \u51fa\u73b0\u5728\u8fd9\u91cc\u7eaf\u5c5e\u4e8c\u8fdb\u5236 bit \u7684\u968f\u610f\u7ec4\u5408\uff0c\u4e0e\u672c\u4eba\u6beb\u65e0\u5173\u7cfb\u3002","title":"\u7ffb\u5899"},{"location":"%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/CS162/","text":"CS162: Operating System \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS61A, CS61B, CS61C \u7f16\u7a0b\u8bed\u8a00\uff1aC, x86\u6c47\u7f16 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a200 \u5c0f\u65f6+\uff0c\u4e0a\u4e0d\u5c01\u9876 \u8fd9\u95e8\u8bfe\u8ba9\u6211\u8bb0\u5fc6\u72b9\u65b0\u7684\u6709\u4e24\u4e2a\u90e8\u5206\uff1a \u9996\u5148\u662f\u6559\u6750\uff0c\u8fd9\u672c\u4e66\u7528\u7684\u6559\u6750 Operating Systems: Principles and Practice (2nd Edition) \u4e00\u5171\u56db\u5377\uff0c\u5199\u5f97\u975e\u5e38\u6df1\u5165\u6d45\u51fa\uff0c\u5f88\u597d\u5730\u5f25\u8865\u4e86 MIT6.S081 \u5728\u7406\u8bba\u77e5\u8bc6\u4e0a\u7684\u4e9b\u8bb8\u7a7a\u767d\uff0c\u975e\u5e38\u5efa\u8bae\u5927\u5bb6\u9605\u8bfb\u3002\u76f8\u5173\u8d44\u6e90\u4f1a\u5206\u4eab\u5728\u672c\u4e66\u7684\u7ecf\u5178\u4e66\u7c4d\u63a8\u8350\u6a21\u5757\u3002 \u5176\u6b21\u662f\u8fd9\u95e8\u8bfe\u7684 Project \u2014\u2014 Pintos\u3002Pintos \u662f\u7531 Ben Pfaff \u7b49\u4eba\u5728 x86 \u5e73\u53f0\u4e0a\u7f16\u5199\u7684\u6559\u5b66\u7528\u64cd\u4f5c\u7cfb\u7edf\uff0cBen Pfaff \u751a\u81f3\u4e13\u95e8\u53d1\u4e86\u7bc7 paper \u6765\u9610\u8ff0 Pintos \u7684\u8bbe\u8ba1\u601d\u60f3\u3002 \u548c MIT \u7684 xv6 \u5c0f\u800c\u7cbe\u7684 lab \u8bbe\u8ba1\u7406\u5ff5\u4e0d\u540c\uff0cPintos \u66f4\u6ce8\u91cd\u7cfb\u7edf\u7684 Design and Implementation\u3002Pintos \u672c\u8eab\u4ec5\u4e00\u4e07\u884c\u5de6\u53f3\uff0c\u53ea\u63d0\u4f9b\u4e86\u64cd\u4f5c\u7cfb\u7edf\u6700\u57fa\u672c\u7684\u529f\u80fd\u3002\u800c 4 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\u4e4b\u540e\u6df1\u611f\u6127\u759a\uff0c\u542b\u6cea\u82b1\u4e86\u4e24\u767e\u591a\u4e70\u4e86\u4e00\u672c\u82f1\u6587\u6b63\u7248\u6536\u85cf\u3002\u4e0b\u9762\u9644\u4e0a\u6b64\u4e66\u5c01\u9762\uff0c\u5982\u679c\u4f60\u80fd\u5b8c\u5168\u7406\u89e3\u5c01\u9762\u56fe\u7684\u6570\u5b66\u542b\u4e49\uff0c\u90a3\u4f60\u5bf9\u7ebf\u6027\u4ee3\u6570\u7684\u7406\u89e3\u4e00\u5b9a\u4f1a\u8fbe\u5230\u65b0\u7684\u9ad8\u5ea6\u3002 \u914d\u5408\u6cb9\u7ba1\u6570\u5b66\u7f51\u7ea2 3Blue1Brown \u7684 \u7ebf\u6027\u4ee3\u6570\u7684\u672c\u8d28 \u7cfb\u5217\u89c6\u9891\u98df\u7528\u66f4\u4f73\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/syllabus/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1aIntroduction to Linear Algebra. Gilbert Strang \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9","title":"MIT18.06: Linear Algebra"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITLA/#mit1806-linear-algebra","text":"","title":"MIT18.06: Linear Algebra"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITLA/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u82f1\u6587 \u7f16\u7a0b\u8bed\u8a00\uff1a\u65e0 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a\u56e0\u4eba\u800c\u5f02 \u6570\u5b66\u5927\u725b Gilbert Strang \u8001\u5148\u751f\u5e74\u903e\u53e4\u7a00\u4ecd\u575a\u6301\u6388\u8bfe\uff0c\u5176\u7ecf\u5178\u6559\u6750 Introduction to Linear Algebra \u5df2\u88ab\u6e05\u534e\u91c7\u7528\u4e3a\u5b98\u65b9\u6559\u6750\u3002\u6211\u5f53\u65f6\u770b\u5b8c\u76d7\u7248 PDF \u4e4b\u540e\u6df1\u611f\u6127\u759a\uff0c\u542b\u6cea\u82b1\u4e86\u4e24\u767e\u591a\u4e70\u4e86\u4e00\u672c\u82f1\u6587\u6b63\u7248\u6536\u85cf\u3002\u4e0b\u9762\u9644\u4e0a\u6b64\u4e66\u5c01\u9762\uff0c\u5982\u679c\u4f60\u80fd\u5b8c\u5168\u7406\u89e3\u5c01\u9762\u56fe\u7684\u6570\u5b66\u542b\u4e49\uff0c\u90a3\u4f60\u5bf9\u7ebf\u6027\u4ee3\u6570\u7684\u7406\u89e3\u4e00\u5b9a\u4f1a\u8fbe\u5230\u65b0\u7684\u9ad8\u5ea6\u3002 \u914d\u5408\u6cb9\u7ba1\u6570\u5b66\u7f51\u7ea2 3Blue1Brown \u7684 \u7ebf\u6027\u4ee3\u6570\u7684\u672c\u8d28 \u7cfb\u5217\u89c6\u9891\u98df\u7528\u66f4\u4f73\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITLA/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a https://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/syllabus/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1aIntroduction to Linear Algebra. Gilbert Strang \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITmaths/","text":"MIT Calculus Course \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u82f1\u8bed \u7f16\u7a0b\u8bed\u8a00\uff1a\u65e0 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a\u56e0\u4eba\u800c\u5f02 MIT \u7684\u5fae\u79ef\u5206\u8bfe\u7531 MIT18.01: Single Variable Calculus \u548c MIT18.02: Multivariable Calculus \u4e24\u95e8\u8bfe\u7ec4\u6210\u3002\u5bf9\u81ea\u5df1\u6570\u5b66\u57fa\u7840\u6bd4\u8f83\u81ea\u4fe1\u7684\u540c\u5b66\u53ef\u4ee5\u53ea\u770b\u8bfe\u7a0b notes\uff0c\u5199\u5f97\u975e\u5e38\u6d45\u663e\u751f\u52a8\u5e76\u4e14\u6293\u4f4f\u672c\u8d28\uff0c\u8ba9\u4f60\u4e0d\u518d\u75b2\u4e8e\u505a\u9898\u800c\u662f\u80fd\u591f\u771f\u6b63\u7aa5\u89c1\u5fae\u79ef\u5206\u7684\u672c\u8d28\u9b45\u529b\u3002 \u914d\u5408\u6cb9\u7ba1\u6570\u5b66\u7f51\u7ea2 3Blue1Brown \u7684 \u5fae\u79ef\u5206\u7684\u672c\u8d28 \u7cfb\u5217\u89c6\u9891\u98df\u7528\u66f4\u4f73\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a 18.01 , 18.02 \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a\u53c2\u89c1\u8bfe\u7a0b notes \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u4e66\u9762\u4f5c\u4e1a\u53ca\u7b54\u6848\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9","title":"MIT18.01/18.02: Calculus"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITmaths/#mit-calculus-course","text":"","title":"MIT Calculus Course"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITmaths/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u82f1\u8bed \u7f16\u7a0b\u8bed\u8a00\uff1a\u65e0 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a\u56e0\u4eba\u800c\u5f02 MIT \u7684\u5fae\u79ef\u5206\u8bfe\u7531 MIT18.01: Single Variable Calculus \u548c MIT18.02: Multivariable Calculus \u4e24\u95e8\u8bfe\u7ec4\u6210\u3002\u5bf9\u81ea\u5df1\u6570\u5b66\u57fa\u7840\u6bd4\u8f83\u81ea\u4fe1\u7684\u540c\u5b66\u53ef\u4ee5\u53ea\u770b\u8bfe\u7a0b notes\uff0c\u5199\u5f97\u975e\u5e38\u6d45\u663e\u751f\u52a8\u5e76\u4e14\u6293\u4f4f\u672c\u8d28\uff0c\u8ba9\u4f60\u4e0d\u518d\u75b2\u4e8e\u505a\u9898\u800c\u662f\u80fd\u591f\u771f\u6b63\u7aa5\u89c1\u5fae\u79ef\u5206\u7684\u672c\u8d28\u9b45\u529b\u3002 \u914d\u5408\u6cb9\u7ba1\u6570\u5b66\u7f51\u7ea2 3Blue1Brown \u7684 \u5fae\u79ef\u5206\u7684\u672c\u8d28 \u7cfb\u5217\u89c6\u9891\u98df\u7528\u66f4\u4f73\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITmaths/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a 18.01 , 18.02 \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a\u53c2\u89c1\u8bfe\u7a0b notes \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u4e66\u9762\u4f5c\u4e1a\u53ca\u7b54\u6848\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/information/","text":"MIT6.050J: Information theory and Entropy \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u65e0 \u7f16\u7a0b\u8bed\u8a00\uff1a\u65e0 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 MIT \u9762\u5411\u5927\u4e00\u65b0\u751f\u7684\u4fe1\u606f\u8bba\u5165\u95e8\u8bfe\u7a0b\uff0cPenfield \u6559\u6388\u4e13\u95e8\u4e3a\u8fd9\u95e8\u8bfe\u5199\u4e86\u4e00\u672c \u6559\u6750 \u4f5c\u4e3a\u8bfe\u7a0b notes\uff0c\u5185\u5bb9\u6df1\u5165\u6d45\u51fa\uff0c\u751f\u52a8\u6709\u8da3\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/index.htm \u8bfe\u7a0b\u6559\u6750\uff1a https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/syllabus/MIT6_050JS08_textbook.pdf \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u8be6\u89c1\u8bfe\u7a0b\u7f51\u7ad9\uff0c\u5305\u542b\u4e66\u9762\u4f5c\u4e1a\u4e0e Matlab \u7f16\u7a0b\u4f5c\u4e1a\u3002","title":"MIT6.050J: Information theory and Entropy"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/information/#mit6050j-information-theory-and-entropy","text":"","title":"MIT6.050J: Information theory and Entropy"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/information/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u65e0 \u7f16\u7a0b\u8bed\u8a00\uff1a\u65e0 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 MIT \u9762\u5411\u5927\u4e00\u65b0\u751f\u7684\u4fe1\u606f\u8bba\u5165\u95e8\u8bfe\u7a0b\uff0cPenfield \u6559\u6388\u4e13\u95e8\u4e3a\u8fd9\u95e8\u8bfe\u5199\u4e86\u4e00\u672c \u6559\u6750 \u4f5c\u4e3a\u8bfe\u7a0b notes\uff0c\u5185\u5bb9\u6df1\u5165\u6d45\u51fa\uff0c\u751f\u52a8\u6709\u8da3\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/information/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/index.htm \u8bfe\u7a0b\u6559\u6750\uff1a https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/syllabus/MIT6_050JS08_textbook.pdf \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u8be6\u89c1\u8bfe\u7a0b\u7f51\u7ad9\uff0c\u5305\u542b\u4e66\u9762\u4f5c\u4e1a\u4e0e Matlab \u7f16\u7a0b\u4f5c\u4e1a\u3002","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/6.042J/","text":"MIT 6.042J: Mathematics for Computer Science \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1aCalculus, Linear Algebra \u7f16\u7a0b\u8bed\u8a00\uff1aPython preferred \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a50-70 \u5c0f\u65f6 MIT \u7684\u79bb\u6563\u6570\u5b66\u4ee5\u53ca\u6982\u7387\u7efc\u5408\u8bfe\u7a0b\uff0c\u5bfc\u5e08\u662f\u5927\u540d\u9f0e\u9f0e\u7684 Tom Leighton ( Akamai \u7684\u8054\u5408\u521b\u59cb\u4eba\u4e4b\u4e00)\u3002\u5b66\u5b8c\u4e4b\u540e\u5bf9\u4e8e\u540e\u7eed\u7684\u7b97\u6cd5\u5b66\u4e60\u5927\u6709\u88e8\u76ca\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1L741147VX \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/assignments/","title":"MIT 6.042J: Mathematics for Computer Science"},{"location":"%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/6.042J/#mit-6042j-mathematics-for-computer-science","text":"","title":"MIT 6.042J: Mathematics for Computer Science"},{"location":"%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/6.042J/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1aCalculus, Linear Algebra \u7f16\u7a0b\u8bed\u8a00\uff1aPython preferred \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a50-70 \u5c0f\u65f6 MIT \u7684\u79bb\u6563\u6570\u5b66\u4ee5\u53ca\u6982\u7387\u7efc\u5408\u8bfe\u7a0b\uff0c\u5bfc\u5e08\u662f\u5927\u540d\u9f0e\u9f0e\u7684 Tom Leighton ( Akamai 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\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://www.inference.org.uk/mackay/itila/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1rs411T71e \u8bfe\u7a0b\u6559\u6750\uff1aInformation Theory, Inference, and Learning Algorithms \u5728\u8bfe\u7a0b\u7f51\u7ad9\u53ef\u4ee5\u4e0b\u8f7d\u5230\u514d\u8d39\u7684\u7535\u5b50\u7248 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u5728\u6bcf\u4e00\u8282\u8bfe\u89c6\u9891\u7684\u6700\u540e\u4f1a\u7559\u6559\u6750\u4e0a\u7684\u8bfe\u540e\u4e60\u9898 R.I.P Prof. David MacKay","title":"The Information Theory, Patter Recognition, and Neural Networks"},{"location":"%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/The_Information_Theory_Pattern_Recognition_and_Neural_Networks/#the-information-theory-patter-recognition-and-neural-networks","text":"","title":"The Information Theory, Patter Recognition, and Neural Networks"},{"location":"%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/The_Information_Theory_Pattern_Recognition_and_Neural_Networks/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aCambridge \u5148\u4fee\u8981\u6c42\uff1aCalculus, Linear Algebra, Probabilities and Statistics \u7f16\u7a0b\u8bed\u8a00\uff1aAnything would be OK, Python preferred \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a30-50 \u5c0f\u65f6 \u5251\u6865\u5927\u5b66 Sir David MacKay \u6559\u6388\u7684\u4fe1\u606f\u8bba\u8bfe\u7a0b\u3002\u6559\u6388\u662f\u4e00\u4f4d\u5341\u5206\u7cbe\u901a\u4fe1\u606f\u8bba\u4e0e\u795e\u7ecf\u7f51\u7edc\u7684\u5b66\u8005\uff0c\u8bfe\u7a0b\u5bf9\u5e94\u6559\u6750\u4e5f\u662f\u4fe1\u606f\u8bba\u9886\u57df\u7684\u4e00\u90e8\u7ecf\u5178\u8457\u4f5c\u3002\u53ef\u60dc\u5929\u5992\u82f1\u624d...","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/The_Information_Theory_Pattern_Recognition_and_Neural_Networks/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://www.inference.org.uk/mackay/itila/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1rs411T71e \u8bfe\u7a0b\u6559\u6750\uff1aInformation Theory, Inference, and Learning Algorithms \u5728\u8bfe\u7a0b\u7f51\u7ad9\u53ef\u4ee5\u4e0b\u8f7d\u5230\u514d\u8d39\u7684\u7535\u5b50\u7248 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\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://stanford.edu/class/ee364a/index.html \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1aD4y1Q7aW \u8bfe\u7a0b\u6559\u6750\uff1a Convex Optimization \u8bfe\u7a0b\u4f5c\u4e1a\uff1a9 \u4e2a Python \u7f16\u7a0b\u4f5c\u4e1a \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/Standford_CVX101 - GitHub \u4e2d\u3002","title":"Standford EE364A: Convex Optimization"},{"location":"%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/convex/#stanford-ee364a-convex-optimization","text":"","title":"Stanford EE364A: Convex Optimization"},{"location":"%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/convex/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1aPython\uff0c\u5fae\u79ef\u5206\uff0c\u7ebf\u6027\u4ee3\u6570\uff0c\u6982\u7387\u8bba\uff0c\u6570\u503c\u5206\u6790 \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 Stephen Boyd \u6559\u6388\u662f\u51f8\u4f18\u5316\u9886\u57df\u7684\u5927\u725b\uff0c\u5176\u7f16\u5199\u7684 Convex Optimization \u8fd9\u672c\u6559\u6750\u88ab\u4f17\u591a\u540d\u6821\u91c7\u7528\u3002\u53e6\u5916\u5176\u7814\u7a76\u56e2\u961f\u8fd8\u4e13\u95e8\u5f00\u53d1\u4e86\u4e00\u4e2a\u7528\u4e8e\u6c42\u89e3\u5e38\u89c1\u51f8\u4f18\u5316\u95ee\u9898\u7684\u7f16\u7a0b\u6846\u67b6\uff0c\u652f\u6301 Python, Julia \u7b49\u4e3b\u6d41\u7f16\u7a0b\u8bed\u8a00\uff0c\u5176\u8bfe\u7a0b\u4f5c\u4e1a\u4e5f\u662f\u91c7\u7528\u8fd9\u4e2a\u7f16\u7a0b\u6846\u67b6\u53bb\u89e3\u51b3\u5b9e\u9645\u751f\u6d3b\u5f53\u4e2d\u7684\u51f8\u4f18\u5316\u95ee\u9898\u3002 \u5728\u5b9e\u9645\u8fd0\u7528\u5f53\u4e2d\uff0c\u4f60\u4f1a\u6df1\u523b\u4f53\u4f1a\u5230\u5bf9\u4e8e\u540c\u4e00\u4e2a\u95ee\u9898\uff0c\u5efa\u6a21\u8fc7\u7a0b\u4e2d\u4e00\u4e2a\u7ec6\u5c0f\u7684\u6539\u53d8\uff0c\u5176\u65b9\u7a0b\u7684\u6c42\u89e3\u96be\u5ea6\u4f1a\u6709\u5929\u58e4\u4e4b\u522b\uff0c\u5982\u4f55\u8ba9\u4f60\u5efa\u6a21\u7684\u65b9\u7a0b\u662f\u201c\u51f8\u201d\u7684\uff0c\u662f\u4e00\u95e8\u827a\u672f\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/convex/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://stanford.edu/class/ee364a/index.html \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1aD4y1Q7aW \u8bfe\u7a0b\u6559\u6750\uff1a Convex Optimization \u8bfe\u7a0b\u4f5c\u4e1a\uff1a9 \u4e2a Python \u7f16\u7a0b\u4f5c\u4e1a","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/convex/#_3","text":"@PKUFlyingPig 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Join\uff0c\u7edf\u8ba1\u4fe1\u606f\u4ee5\u53ca\u4ee3\u4ef7\u4f30\u8ba1\uff0c\u5b50\u67e5\u8be2\u5b9e\u73b0\uff0cAgg\uff0cGroup By \u7684\u5b9e\u73b0\u7b49\u3002\u9664\u6b64\u4e4b\u5916\uff0c\u8fd8\u6709 B+\u6811\uff0cWAL \u76f8\u5173\u5b9e\u9a8c\u3002\u672c\u95e8\u8bfe\u7a0b\u9002\u5408\u5728\u5b66\u5b8c CMU15-445 \u8bfe\u7a0b\u4e4b\u540e\uff0c\u5bf9\u67e5\u8be2\u4f18\u5316\u76f8\u5173\u5185\u5bb9\u6709\u5174\u8da3\u7684\u540c\u5b66\u3002 \u4e0b\u9762\u4ecb\u7ecd\u4e00\u4e0b\u8fd9\u95e8\u8bfe\u7684\u524d 3 \u4e2a Assignment \u4e5f\u5c31\u662f\u5b9e\u9a8c Lab \u6240\u8981\u5b9e\u73b0\u7684\u529f\u80fd\uff1a Assignment1 \u4e3a NanoDB \u63d0\u4f9b delete\uff0cupdate \u8bed\u53e5\u7684\u652f\u6301\u3002 \u4e3a Buffer Pool Manager \u6dfb\u52a0\u5408\u9002\u7684 pin/unpin \u4ee3\u7801\u3002 \u63d0\u5347 insert \u8bed\u53e5\u7684\u6027\u80fd\uff0c \u540c\u65f6\u4e0d\u4f7f\u6570\u636e\u5e93\u6587\u4ef6\u5927\u5c0f\u8fc7\u5206\u81a8\u80c0\u3002 Assignment2 \u5b9e\u73b0\u4e00\u4e2a\u7b80\u5355\u7684\u8ba1\u5212\u751f\u6210\u5668\uff0c\u5c06\u5404\u79cd\u5df2\u7ecf Parser \u8fc7\u7684 SQL \u8bed\u53e5\u8f6c\u5316\u4e3a\u53ef\u6267\u884c\u7684\u6267\u884c\u8ba1\u5212\u3002 \u4f7f\u7528 nested-loop join \u7b97\u6cd5\uff0c\u5b9e\u73b0\u652f\u6301 inner- and outer-join \u7684 Join \u8ba1\u5212\u8282\u70b9\u3002 \u6dfb\u52a0\u4e00\u4e9b\u5355\u5143\u6d4b\u8bd5\uff0c \u4fdd\u8bc1 inner- and outer-join \u529f\u80fd\u5b9e\u73b0\u6b63\u786e\u3002 Assignment3 \u5b8c\u6210\u6536\u96c6\u8868\u7684\u7edf\u8ba1\u4fe1\u606f\u3002 \u5b8c\u6210\u5404\u79cd\u8ba1\u5212\u8282\u70b9\u7684\u8ba1\u5212\u6210\u672c\u8ba1\u7b97\u3002 \u8ba1\u7b97\u53ef\u51fa\u73b0\u5728\u6267\u884c\u8ba1\u5212\u4e2d\u7684\u5404\u79cd\u8c13\u8bcd\u7684\u9009\u62e9\u6027\u3002 \u6839\u636e\u8c13\u8bcd\u66f4\u65b0\u8ba1\u5212\u8282\u70b9\u8f93\u51fa\u7684\u5143\u7ec4\u7edf\u8ba1\u4fe1\u606f\u3002 \u5269\u4f59 Assignment \u548c Challenges \u53ef\u4ee5\u67e5\u770b\u8bfe\u7a0b\u4ecb\u7ecd\uff0c\u63a8\u8350\u4f7f\u7528 IDEA \u6253\u5f00\u5de5\u7a0b\uff0cMaven \u6784\u5efa\uff0c\u6ce8\u610f\u65e5\u5fd7\u76f8\u5173\u914d\u7f6e\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://courses.cms.caltech.edu/cs122/ \u8bfe\u7a0b\u4ee3\u7801\uff1a https://gitlab.caltech.edu/cs122-19wi \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a7 Assignments + 2 Challenges","title":"Caltech CS122: Database System Implementation"},{"location":"%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/CS122/#caltech-cs-122-database-system-implementation","text":"","title":"Caltech CS 122: Database System Implementation"},{"location":"%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/CS122/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aCaltech \u5148\u4fee\u8981\u6c42\uff1a\u65e0 \u7f16\u7a0b\u8bed\u8a00\uff1aJava \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f 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\u8fd9\u95e8\u8bfe\u7684\u6784\u6210\u5c31\u975e\u5e38\u597d\u5730\u5951\u5408\u4e86\u4e0a\u8ff0\u4e09\u4e2a\u6b65\u9aa4\u3002\u89c2\u770b\u8bfe\u7a0b\u89c6\u9891\u5e76\u4e14\u9605\u8bfb\u6559\u6388\u7684 \u5f00\u6e90\u8bfe\u672c \u6709\u52a9\u4e8e\u4f60\u7406\u89e3\u7b97\u6cd5\u7684\u672c\u8d28\uff0c\u8ba9\u4f60\u4e5f\u53ef\u4ee5\u7528\u975e\u5e38 \u751f\u52a8\u6d45\u663e\u7684\u8bdd\u8bed\u5411\u522b\u4eba\u8bb2\u8ff0\u4e3a\u4ec0\u4e48\u8fd9\u4e2a\u7b97\u6cd5\u5f97\u957f\u8fd9\u4e2a\u6837\u5b50\u3002 \u5728\u7406\u89e3\u7b97\u6cd5\u4e4b\u540e\uff0c\u4f60\u53ef\u4ee5\u9605\u8bfb\u6559\u6388\u5bf9\u4e8e\u8bfe\u7a0b\u4e2d\u8bb2\u6388\u7684\u6240\u6709\u6570\u636e\u7ed3\u6784\u4e0e\u7b97\u6cd5\u7684 \u4ee3\u7801\u5b9e\u73b0 \u3002 \u6ce8\u610f\uff0c\u8fd9\u4e9b\u5b9e\u73b0\u53ef\u4e0d\u662f demo \u6027\u8d28\u7684\uff0c\u800c\u662f\u5de5\u4e1a\u7ea7\u7684\u9ad8\u6548\u5b9e\u73b0\uff0c\u4ece\u6ce8\u91ca\u5230\u53d8\u91cf\u547d\u540d\u90fd\u975e\u5e38\u4e25\u8c28\uff0c\u6a21\u5757\u5316\u4e5f\u505a\u5f97\u76f8\u5f53\u597d\uff0c\u662f\u8d28\u91cf\u5f88\u9ad8\u7684\u4ee3\u7801\u3002\u6211\u4ece\u8fd9\u4e9b\u4ee3\u7801\u4e2d\u6536\u83b7\u826f\u591a\u3002 \u6700\u540e\uff0c\u5c31\u662f\u8fd9\u95e8\u8bfe\u6700\u6fc0\u52a8\u4eba\u5fc3\u7684\u90e8\u5206\u4e86\uff0c10 \u4e2a\u9ad8\u8d28\u91cf\u7684 Project\uff0c\u5e76\u4e14\u5168\u90fd\u6709\u5b9e\u9645\u95ee\u9898\u7684\u80cc\u666f\u63cf\u8ff0\uff0c\u4e30\u5bcc\u7684\u6d4b\u8bd5\u6837\u4f8b\uff0c\u81ea\u52a8\u7684\u8bc4\u5206\u7cfb\u7edf\uff08\u4ee3\u7801\u98ce\u683c\u4e5f\u662f\u8bc4\u5206\u7684\u4e00\u73af\uff09\u3002\u8ba9\u4f60\u5728\u5b9e\u9645\u751f\u6d3b\u4e2d \u9886\u7565\u7b97\u6cd5\u7684\u9b45\u529b\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a Algorithm I , Algorithm II \u8bfe\u7a0b\u89c6\u9891\uff1a\u8be6\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a https://algs4.cs.princeton.edu/home/ \u8bfe\u7a0b\u4f5c\u4e1a\uff1a10\u4e2aProject\uff0c\u5177\u4f53\u8981\u6c42\u8be6\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/Princeton-Algorithm - GitHub \u4e2d\u3002","title":"Coursera: Algorithms I & II"},{"location":"%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/Algo/#coursera-algorithms-i-ii","text":"","title":"Coursera: Algorithms I & II"},{"location":"%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/Algo/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aPrinceton \u5148\u4fee\u8981\u6c42\uff1aCS61A \u7f16\u7a0b\u8bed\u8a00\uff1aJava \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a60 \u5c0f\u65f6 \u8fd9\u662f Coursera \u4e0a\u8bc4\u5206\u6700\u9ad8\u7684\u7b97\u6cd5\u8bfe\u7a0b\u3002Robert Sedgewick \u6559\u6388\u6709\u4e00\u79cd\u9b54\u529b\uff0c\u53ef\u4ee5\u5c06\u65e0\u8bba\u591a\u4e48\u590d\u6742\u7684\u7b97\u6cd5\u8bb2\u5f97\u6781\u4e3a\u751f\u52a8\u6d45\u663e\u3002\u5b9e\u4e0d\u76f8\u7792\uff0c\u56f0\u6270\u6211\u591a\u5e74\u7684 KMP \u4ee5\u53ca\u7f51\u7edc\u6d41\u7b97\u6cd5\u90fd\u662f\u5728\u8fd9\u95e8\u8bfe\u4e0a\u8ba9\u6211\u8305\u585e\u987f\u5f00\u7684\uff0c\u65f6\u9694\u4e24\u5e74\u6211\u751a\u81f3\u8fd8\u80fd\u5199\u51fa\u8fd9\u4e24\u4e2a\u7b97\u6cd5\u7684\u63a8\u5bfc\u4e0e\u8bc1\u660e\u3002 \u4f60\u662f\u5426\u89c9\u5f97\u7b97\u6cd5\u5b66\u4e86\u5c31\u5fd8\u5462\uff1f\u6211\u89c9\u5f97\u8ba9\u4f60\u5b8c\u5168\u638c\u63e1\u4e00\u4e2a\u7b97\u6cd5\u7684\u6838\u5fc3\u5728\u4e8e\u7406\u89e3\u4e09\u70b9\uff1a \u4e3a\u4ec0\u4e48\u8fd9\u4e48\u505a\uff1f\uff08\u6b63\u786e\u6027\u63a8\u5bfc\uff0c\u6291\u6216\u662f\u6574\u4e2a\u7b97\u6cd5\u7684\u6838\u5fc3\u672c\u8d28\uff09 \u5982\u4f55\u5b9e\u73b0\u5b83\uff1f\uff08\u5149\u5b66\u4e0d\u7528\u5047\u628a\u5f0f\uff09 \u7528\u5b83\u89e3\u51b3\u5b9e\u9645\u95ee\u9898\uff08\u5b66\u4ee5\u81f4\u7528\u624d\u662f\u771f\u672c\u4e8b\uff09 \u8fd9\u95e8\u8bfe\u7684\u6784\u6210\u5c31\u975e\u5e38\u597d\u5730\u5951\u5408\u4e86\u4e0a\u8ff0\u4e09\u4e2a\u6b65\u9aa4\u3002\u89c2\u770b\u8bfe\u7a0b\u89c6\u9891\u5e76\u4e14\u9605\u8bfb\u6559\u6388\u7684 \u5f00\u6e90\u8bfe\u672c \u6709\u52a9\u4e8e\u4f60\u7406\u89e3\u7b97\u6cd5\u7684\u672c\u8d28\uff0c\u8ba9\u4f60\u4e5f\u53ef\u4ee5\u7528\u975e\u5e38 \u751f\u52a8\u6d45\u663e\u7684\u8bdd\u8bed\u5411\u522b\u4eba\u8bb2\u8ff0\u4e3a\u4ec0\u4e48\u8fd9\u4e2a\u7b97\u6cd5\u5f97\u957f\u8fd9\u4e2a\u6837\u5b50\u3002 \u5728\u7406\u89e3\u7b97\u6cd5\u4e4b\u540e\uff0c\u4f60\u53ef\u4ee5\u9605\u8bfb\u6559\u6388\u5bf9\u4e8e\u8bfe\u7a0b\u4e2d\u8bb2\u6388\u7684\u6240\u6709\u6570\u636e\u7ed3\u6784\u4e0e\u7b97\u6cd5\u7684 \u4ee3\u7801\u5b9e\u73b0 \u3002 \u6ce8\u610f\uff0c\u8fd9\u4e9b\u5b9e\u73b0\u53ef\u4e0d\u662f demo \u6027\u8d28\u7684\uff0c\u800c\u662f\u5de5\u4e1a\u7ea7\u7684\u9ad8\u6548\u5b9e\u73b0\uff0c\u4ece\u6ce8\u91ca\u5230\u53d8\u91cf\u547d\u540d\u90fd\u975e\u5e38\u4e25\u8c28\uff0c\u6a21\u5757\u5316\u4e5f\u505a\u5f97\u76f8\u5f53\u597d\uff0c\u662f\u8d28\u91cf\u5f88\u9ad8\u7684\u4ee3\u7801\u3002\u6211\u4ece\u8fd9\u4e9b\u4ee3\u7801\u4e2d\u6536\u83b7\u826f\u591a\u3002 \u6700\u540e\uff0c\u5c31\u662f\u8fd9\u95e8\u8bfe\u6700\u6fc0\u52a8\u4eba\u5fc3\u7684\u90e8\u5206\u4e86\uff0c10 \u4e2a\u9ad8\u8d28\u91cf\u7684 Project\uff0c\u5e76\u4e14\u5168\u90fd\u6709\u5b9e\u9645\u95ee\u9898\u7684\u80cc\u666f\u63cf\u8ff0\uff0c\u4e30\u5bcc\u7684\u6d4b\u8bd5\u6837\u4f8b\uff0c\u81ea\u52a8\u7684\u8bc4\u5206\u7cfb\u7edf\uff08\u4ee3\u7801\u98ce\u683c\u4e5f\u662f\u8bc4\u5206\u7684\u4e00\u73af\uff09\u3002\u8ba9\u4f60\u5728\u5b9e\u9645\u751f\u6d3b\u4e2d \u9886\u7565\u7b97\u6cd5\u7684\u9b45\u529b\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/Algo/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a Algorithm I , Algorithm II \u8bfe\u7a0b\u89c6\u9891\uff1a\u8be6\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a https://algs4.cs.princeton.edu/home/ \u8bfe\u7a0b\u4f5c\u4e1a\uff1a10\u4e2aProject\uff0c\u5177\u4f53\u8981\u6c42\u8be6\u89c1\u8bfe\u7a0b\u7f51\u7ad9","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/Algo/#_3","text":"@PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/Princeton-Algorithm - GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS170/","text":"CS170: Efficient Algorithms and Intractable Problems \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS61B, CS70 \u7f16\u7a0b\u8bed\u8a00\uff1aLaTeX \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a60 \u5c0f\u65f6 \u4f2f\u514b\u5229\u7684\u7b97\u6cd5\u8bbe\u8ba1\u8bfe\uff0c\u66f4\u6ce8\u91cd\u7b97\u6cd5\u7684\u7406\u8bba\u57fa\u7840\u4e0e\u590d\u6742\u5ea6\u5206\u6790\u3002\u8bfe\u7a0b\u5185\u5bb9\u6db5\u76d6\u4e86\u5206\u6cbb\u3001\u56fe\u7b97\u6cd5\u3001\u6700\u77ed\u8def\u3001\u751f\u6210\u6811\u3001\u8d2a\u5fc3\u3001\u52a8\u89c4\u3001\u5e76\u67e5\u96c6\u3001\u7ebf\u6027\u89c4\u5212\u3001\u7f51\u7edc\u6d41\u3001NP \u95ee\u9898\u3001\u968f\u673a\u7b97\u6cd5\u3001\u54c8\u5e0c\u7b97\u6cd5\u7b49\u7b49\u3002 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\u8fd9\u4e00\u4e3b\u6d41\u7684\u673a\u5668\u5b66\u4e60\u7f16\u8bd1\u6846\u67b6\u4e3a\u4f8b\uff08\u9648\u5929\u5947\u662f\u8fd9\u4e2a\u6846\u67b6\u7684\u521b\u59cb\u4eba\u4e4b\u4e00\uff09\uff0c\u805a\u7126\u4e8e\u5982\u4f55\u5c06\u5f00\u53d1\u6a21\u5f0f\u4e0b\uff08\u5982 Tensorflow, Pytorch, Jax\uff09\u7684\u5404\u7c7b\u673a\u5668\u5b66\u4e60\u6a21\u578b\uff0c\u901a\u8fc7\u4e00\u5957\u666e\u9002\u7684\u62bd\u8c61\u548c\u4f18\u5316\u7b97\u6cd5\uff0c\u53d8\u6362\u4e3a\u62e5\u6709\u66f4\u9ad8\u6027\u80fd\u5e76\u4e14\u9002\u914d\u5404\u7c7b\u5e95\u5c42\u786c\u4ef6\u7684\u90e8\u7f72\u6a21\u5f0f\u3002\u8bfe\u7a0b\u8bb2\u6388\u7684\u77e5\u8bc6\u70b9\u90fd\u662f\u76f8\u5bf9 High-Level \u7684\u5b8f\u89c2\u6982\u5ff5\uff0c\u540c\u65f6\u6bcf\u8282\u8bfe\u90fd\u4f1a\u6709\u4e00\u4e2a\u914d\u5957\u7684 Jupyter Notebook \u6765\u901a\u8fc7\u5177\u4f53\u7684\u4ee3\u7801\u8bb2\u89e3\u77e5\u8bc6\u70b9\uff0c\u56e0\u6b64\u5982\u679c\u4ece\u4e8b TVM \u76f8\u5173\u7684\u7f16\u7a0b\u5f00\u53d1\u7684\u8bdd\uff0c\u8fd9\u95e8\u8bfe\u6709\u4e30\u5bcc\u4e14\u89c4\u8303\u7684\u4ee3\u7801\u793a\u4f8b\u4ee5\u4f9b\u53c2\u8003\u3002 \u6240\u6709\u7684\u8bfe\u7a0b\u8d44\u6e90\u5168\u90e8\u5f00\u6e90\u5e76\u4e14\u6709\u4e2d\u6587\u548c\u82f1\u6587\u4e24\u4e2a\u7248\u672c\uff0cB\u7ad9\u548c\u6cb9\u7ba1\u5206\u522b\u6709\u4e2d\u6587\u548c\u82f1\u6587\u7684\u8bfe\u7a0b\u5f55\u5f71\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%B3%BB%E7%BB%9F/MLC/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a https://mlc.ai/summer22-zh/ \u8bfe\u7a0b\u89c6\u9891\uff1a Bilibili \u8bfe\u7a0b\u7b14\u8bb0\uff1a https://mlc.ai/zh/index.html \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://github.com/mlc-ai/notebooks/blob/main/assignment","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CMU10-708/","text":"CMU 10-708: Probabilistic Graphical Models \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aCMU \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Reinforcement Learning \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://sailinglab.github.io/pgm-spring-2019/ \u8fd9\u4e2a\u7f51\u7ad9\u5305\u542b\u4e86\u6240\u6709\u7684\u8d44\u6e90\uff1aslides, nots, video, homework, project \u8fd9\u95e8\u8bfe\u7a0b\u662f CMU \u7684\u56fe\u6a21\u578b\u57fa\u7840 + \u8fdb\u9636\u8bfe\uff0c\u6388\u8bfe\u8001\u5e08\u4e3a Eric P. Xing\uff0c\u6db5\u76d6\u4e86\u56fe\u6a21\u578b\u57fa\u7840\uff0c\u4e0e\u795e\u7ecf\u7f51\u7edc\u7684\u7ed3\u5408\uff0c\u5728\u5f3a\u5316\u5b66\u4e60\u4e2d\u7684\u5e94\u7528\uff0c\u4ee5\u53ca\u975e\u53c2\u6570\u65b9\u6cd5\u3002\u76f8\u5f53\u786c\u6838","title":"CMU 10-708: Probabilistic Graphical Models"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CMU10-708/#cmu-10-708-probabilistic-graphical-models","text":"","title":"CMU 10-708: Probabilistic Graphical Models"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CMU10-708/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aCMU \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Reinforcement Learning \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://sailinglab.github.io/pgm-spring-2019/ \u8fd9\u4e2a\u7f51\u7ad9\u5305\u542b\u4e86\u6240\u6709\u7684\u8d44\u6e90\uff1aslides, nots, video, homework, project \u8fd9\u95e8\u8bfe\u7a0b\u662f CMU \u7684\u56fe\u6a21\u578b\u57fa\u7840 + \u8fdb\u9636\u8bfe\uff0c\u6388\u8bfe\u8001\u5e08\u4e3a Eric P. Xing\uff0c\u6db5\u76d6\u4e86\u56fe\u6a21\u578b\u57fa\u7840\uff0c\u4e0e\u795e\u7ecf\u7f51\u7edc\u7684\u7ed3\u5408\uff0c\u5728\u5f3a\u5316\u5b66\u4e60\u4e2d\u7684\u5e94\u7528\uff0c\u4ee5\u53ca\u975e\u53c2\u6570\u65b9\u6cd5\u3002\u76f8\u5f53\u786c\u6838","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CS229M/","text":"STATS214 / CS229M: Machine Learning Theory \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Statistics \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/stats214/ \u7ecf\u5178\u5b66\u4e60\u7406\u8bba + \u6700\u65b0\u6df1\u5ea6\u5b66\u4e60\u7406\u8bba\uff0c\u975e\u5e38\u786c\u6838\u3002\u6388\u8bfe\u8001\u5e08\u4e4b\u524d\u662f Percy Liang\uff0c\u73b0\u5728\u662f Tengyu Ma","title":"Stanford STATS214 / CS229M: Machine Learning Theory"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CS229M/#stats214-cs229m-machine-learning-theory","text":"","title":"STATS214 / CS229M: Machine Learning Theory"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CS229M/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Statistics \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/stats214/ \u7ecf\u5178\u5b66\u4e60\u7406\u8bba + \u6700\u65b0\u6df1\u5ea6\u5b66\u4e60\u7406\u8bba\uff0c\u975e\u5e38\u786c\u6838\u3002\u6388\u8bfe\u8001\u5e08\u4e4b\u524d\u662f Percy Liang\uff0c\u73b0\u5728\u662f Tengyu Ma","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STA4273/","text":"STA 4273 Winter 2021: Minimizing Expectations \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aU Toronto \u5148\u4fee\u8981\u6c42\uff1aBayesian Inference, Reinforcement Learning \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://www.cs.toronto.edu/~cmaddis/courses/sta4273_w21/ \u8fd9\u662f\u4e00\u95e8\u8f83\u4e3a\u8fdb\u9636\u7684 Ph.D. \u7814\u7a76\u8bfe\u7a0b\uff0c\u6838\u5fc3\u5185\u5bb9\u662f inference \u548c control \u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u6388\u8bfe\u8001\u5e08\u4e3a Chris Maddison (AlphaGo founding member, NeurIPS 14 best paper)","title":"U Toronto STA 4273 Winter 2021: Minimizing Expectations"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STA4273/#sta-4273-winter-2021-minimizing-expectations","text":"","title":"STA 4273 Winter 2021: Minimizing Expectations"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STA4273/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aU Toronto \u5148\u4fee\u8981\u6c42\uff1aBayesian Inference, Reinforcement Learning \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://www.cs.toronto.edu/~cmaddis/courses/sta4273_w21/ \u8fd9\u662f\u4e00\u95e8\u8f83\u4e3a\u8fdb\u9636\u7684 Ph.D. \u7814\u7a76\u8bfe\u7a0b\uff0c\u6838\u5fc3\u5185\u5bb9\u662f inference \u548c control \u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u6388\u8bfe\u8001\u5e08\u4e3a Chris Maddison (AlphaGo founding member, NeurIPS 14 best paper)","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STAT8201/","text":"Columbia STAT 8201: Deep Generative Models \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aColumbia University \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Graphical Models \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://stat.columbia.edu/~cunningham/teaching/GR8201/ \u8fd9\u95e8\u8bfe\u662f\u4e00\u95e8 PhD \u8ba8\u8bba\u73ed\uff0c\u6bcf\u5468\u7684\u5185\u5bb9\u662f\u5c55\u793a + \u8ba8\u8bba\u8bba\u6587\uff0c\u6388\u8bfe\u8001\u5e08\u662f John Cunningham\u3002Deep Generative Models \uff08\u6df1\u5ea6\u751f\u6210\u6a21\u578b\uff09 \u662f\u56fe\u6a21\u578b\u4e0e\u795e\u7ecf\u7f51\u7edc\u7684\u7ed3\u5408\uff0c\u4e5f\u662f\u73b0\u4ee3\u673a\u5668\u5b66\u4e60\u6700\u91cd\u8981\u7684\u65b9\u5411\u4e4b\u4e00","title":"Columbia STAT 8201: Deep Generative Models"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STAT8201/#columbia-stat-8201-deep-generative-models","text":"","title":"Columbia STAT 8201: Deep Generative Models"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STAT8201/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aColumbia University \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Graphical Models \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://stat.columbia.edu/~cunningham/teaching/GR8201/ \u8fd9\u95e8\u8bfe\u662f\u4e00\u95e8 PhD \u8ba8\u8bba\u73ed\uff0c\u6bcf\u5468\u7684\u5185\u5bb9\u662f\u5c55\u793a + \u8ba8\u8bba\u8bba\u6587\uff0c\u6388\u8bfe\u8001\u5e08\u662f John Cunningham\u3002Deep Generative Models \uff08\u6df1\u5ea6\u751f\u6210\u6a21\u578b\uff09 \u662f\u56fe\u6a21\u578b\u4e0e\u795e\u7ecf\u7f51\u7edc\u7684\u7ed3\u5408\uff0c\u4e5f\u662f\u73b0\u4ee3\u673a\u5668\u5b66\u4e60\u6700\u91cd\u8981\u7684\u65b9\u5411\u4e4b\u4e00","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/","text":"\u673a\u5668\u5b66\u4e60\u8fdb\u9636 \u6b64\u8def\u7ebf\u56fe\u9002\u7528\u4e8e\u5df2\u7ecf\u5b66\u8fc7\u4e86\u57fa\u7840\u673a\u5668\u5b66\u4e60 (ML, NLP, CV, RL) \u7684\u540c\u5b66 (\u9ad8\u5e74\u7ea7\u672c\u79d1\u751f\u6216\u4f4e\u5e74\u7ea7\u7814\u7a76\u751f)\uff0c\u5df2\u7ecf\u53d1\u8868\u8fc7\u81f3\u5c11\u4e00\u7bc7\u9876\u4f1a\u8bba\u6587 (NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, CVPR, ICCV) \u60f3\u8981\u8d70\u673a\u5668\u5b66\u4e60\u79d1\u7814\u8def\u7ebf\u7684\u9009\u624b\u3002 \u6b64\u8def\u7ebf\u7684\u76ee\u6807\u662f\u4e3a\u8bfb\u61c2\u4e0e\u53d1\u8868\u673a\u5668\u5b66\u4e60\u9876\u4f1a\u8bba\u6587\u6253\u4e0b\u7406\u8bba\u57fa\u7840\uff0c\u7279\u522b\u662f Probabilistic Methods \u8fd9\u4e2a track \u4e0b\u7684\u6587\u7ae0 \u673a\u5668\u5b66\u4e60\u8fdb\u9636\u53ef\u80fd\u5b58\u5728\u591a\u79cd\u4e0d\u540c\u7684\u5b66\u4e60\u8def\u7ebf\uff0c\u6b64\u8def\u7ebf\u53ea\u80fd\u4ee3\u8868\u4f5c\u8005 Yao Fu \u6240\u7406\u89e3\u7684\u6700\u4f73\u8def\u5f84\uff0c\u4fa7\u91cd\u4e8e\u8d1d\u53f6\u65af\u5b66\u6d3e\u4e0b\u7684\u6982\u7387\u5efa\u6a21\u65b9\u6cd5\uff0c\u4e5f\u4f1a\u6d89\u53ca\u5230\u5404\u9879\u76f8\u5173\u5b66\u79d1\u7684\u4ea4\u53c9\u77e5\u8bc6\u3002 \u5fc5\u8bfb\u6559\u6750 PRML: Pattern Recognition and Machine Learning. Christopher Bishop \u7ecf\u5178\u8d1d\u53f6\u65af\u5b66\u6d3e\u6559\u6750 AoS: All of Statistics. Larry Wasserman \u7ecf\u5178\u9891\u7387\u5b66\u6d3e\u6559\u6750 \u6240\u4ee5\u8fd9\u4e24\u672c\u4e66\u521a\u597d\u76f8\u8f85\u76f8\u6210 \u5b57\u5178 MLAPP: Machine Learning: A Probabilistic Perspective. Kevin Murphy Convex Optimization. Stephen Boyd and Lieven Vandenberghe \u8fdb\u9636\u4e66\u7c4d W&J: Graphical Models, Exponential Families, and Variational Inference. Martin Wainwright and Michael Jordan Theory of Point Estimation. E. L. Lehmann and George Casella \u5982\u4f55\u9605\u8bfb Guidelines \u5fc5\u8bfb\u6559\u6750\u5c31\u662f\u4e00\u5b9a\u8981\u8bfb\u7684\u6559\u6750 \u5b57\u5178\u7684\u610f\u601d\u662f\uff0c\u4e00\u822c\u60c5\u51b5\u4e0b\u4e0d\u7ba1\u5b83\uff0c\u4f46\u5f53\u9047\u5230\u4e86\u4e0d\u61c2\u7684\u6982\u5ff5\u7684\u65f6\u5019\uff0c\u5c31\u53bb\u5b57\u5178\u91cc\u9762\u67e5\uff08\u800c\u4e0d\u662f\u7ef4\u57fa\u767e\u79d1\uff09 \u8fdb\u9636\u4e66\u7c4d\u5148\u4e0d\u8bfb\uff0c\u5148\u8bfb\u5b8c\u5fc5\u8bfb\u4e66\u7c4d\u3002\u5fc5\u8bfb\u4e66\u7c4d\u4e00\u822c\u90fd\u662f\u8981\u524d\u524d\u540e\u540e\u53cd\u590d\u770b\u8fc7 N \u904d\u624d\u7b97\u8bfb\u5b8c \u8bfb\u7684\u8fc7\u7a0b\u4e2d\uff0c\u6700\u91cd\u8981\u7684\u8bfb\u6cd5\u5c31\u662f\u5bf9\u6bd4\u9605\u8bfb (contrastive-comparative reading)\uff1a\u540c\u65f6\u6253\u5f00\u4e24\u672c\u4e66\u8bb2\u540c\u4e00\u4e3b\u9898\u7684\u7ae0\u8282\uff0c\u7136\u540e\u5bf9\u6bd4\u76f8\u540c\u70b9\u548c\u4e0d\u540c\u70b9\u548c\u8054\u7cfb \u8bfb\u7684\u8fc7\u7a0b\u4e2d\uff0c\u5c3d\u91cf\u53bb\u56de\u60f3\u4e4b\u524d\u8bfb\u8fc7\u7684\u8bba\u6587\uff0c\u6bd4\u8f83\u8bba\u6587\u548c\u6559\u6750\u7684\u76f8\u540c\u70b9\u4e0e\u4e0d\u540c\u70b9 \u57fa\u7840\u8def\u5f84 \u5148\u8bfb AoS \u7b2c\u516d\u7ae0: Models, Statistical Inference and Learning\uff0c\u8fd9\u4e00\u90e8\u5206\u662f\u6700\u57fa\u7840\u7684\u79d1\u666e \u7136\u540e\u8bfb PRML \u7b2c 10, 11 \u7ae0 \u7b2c 10 \u7ae0\u7684\u5185\u5bb9\u662f Variational Inference, \u7b2c 11 \u7ae0\u7684\u5185\u5bb9\u662f MCMC, \u8fd9\u4e24\u79cd\u65b9\u6cd5\u662f\u8d1d\u53f6\u65af\u63a8\u65ad\u7684\u4e24\u6761\u6700\u4e3b\u8981\u8def\u7ebf \u5982\u679c\u5728\u8bfb PRML \u7684\u8fc7\u7a0b\u4e2d\u53d1\u73b0\u6709\u4efb\u4f55\u4e0d\u61c2\u7684\u540d\u8bcd\uff0c\u5c31\u53bb\u7ffb\u524d\u9762\u7684\u7ae0\u8282\u3002\u5f88\u5927\u6982\u7387\u80fd\u591f\u5728\u7b2c 3\uff0c4 \u7ae0\u627e\u5230\u76f8\u5bf9\u5e94\u7684\u5b9a\u4e49\uff1b\u5982\u679c\u627e\u4e0d\u5230\u6216\u8005\u4e0d\u591f\u8be6\u7ec6\uff0c\u5c31\u53bb\u67e5 MLAPP AoS \u7b2c 8 \u7ae0 (Parametric Inference) \u548c\u7b2c 11 \u7ae0 (Bayesian Inference) \u4e5f\u53ef\u4ee5\u4f5c\u4e3a\u53c2\u8003\u3002\u6700\u597d\u7684\u65b9\u6cd5\u662f\u591a\u672c\u4e66\u5bf9\u6bd4\u9605\u8bfb\uff0c\u6d41\u7a0b\u5982\u4e0b \u5047\u8bbe\u6211\u5728\u8bfb PRML \u7b2c 10 \u7ae0\u7684\u65f6\u5019\u53d1\u73b0\u4e86\u4e00\u4e2a\u4e0d\u61c2\u7684\u8bcd\uff1aposterior inference \u4e8e\u662f\u6211\u5f80\u524d\u7ffb\uff0c\u7ffb\u5230\u4e86\u7b2c 3 \u7ae0 (Linear Model for Regression)\uff0c\u770b\u5230\u4e86\u6700\u7b80\u5355\u7684 posterior \u7136\u540e\u6211\u63a5\u7740\u7ffb AoS\uff0c\u7ffb\u5230\u4e86\u7b2c 11 \u7ae0\uff0c\u4e5f\u6709\u5bf9 posterior \u7684\u63cf\u8ff0 \u7136\u540e\u6211\u5bf9\u6bd4 PRML \u7b2c 10 \u7ae0\uff0c\u7b2c 3 \u7ae0\uff0cAoS \u7b2c 11 \u7ae0\uff0c\u4e09\u5904\u4e0d\u540c\u5730\u65b9\u5bf9 posterior \u7684\u89e3\u8bfb\uff0c\u6bd4\u8f83\u5176\u76f8\u540c\u70b9\u548c\u4e0d\u540c\u70b9\u548c\u8054\u7cfb \u8bfb\u5b8c PRML \u7b2c 10 \u548c 11 \u7ae0\u4e4b\u540e\uff0c\u63a5\u7740\u8bfb AoS \u7b2c 24 \u7ae0 (Simulation Methods)\uff0c\u7136\u540e\u628a\u5b83\u548c PRML \u7b2c 11 \u7ae0\u5bf9\u6bd4\u9605\u8bfb -- \u8fd9\u4fe9\u90fd\u662f\u8bb2 MCMC \u5982\u679c\u5230\u6b64\u5904\u53d1\u73b0\u8fd8\u6709\u57fa\u7840\u6982\u5ff5\u8bfb\u4e0d\u61c2\uff0c\u5c31\u56de\u5230 PRML \u7b2c 3 \u7ae0\uff0c\u628a\u5b83\u548c AoS \u7b2c 11 \u7ae0\u5bf9\u6bd4\u9605\u8bfb Again\uff0c\u5bf9\u6bd4\u9605\u8bfb\u975e\u5e38\u91cd\u8981\uff0c\u4e00\u5b9a\u8981\u628a\u4e0d\u540c\u672c\u4e66\u7684\u7c7b\u4f3c\u5185\u5bb9\u540c\u65f6\u6446\u5728\u9762\u524d\u76f8\u4e92\u5bf9\u6bd4\uff0c\u8fd9\u6837\u53ef\u4ee5\u663e\u8457\u589e\u5f3a\u8bb0\u5fc6 \u7136\u540e\u8bfb PRML \u7b2c 13 \u7ae0\uff08\u8df3\u8fc7\u7b2c 12 \u7ae0\uff09\uff0c\u8fd9\u4e00\u7ae0\u53ef\u4ee5\u548c MLAPP \u7684\u7b2c 17, 18 \u7ae0\u5bf9\u6bd4\u9605\u8bfb MLAPP \u7b2c 17 \u7ae0\u662f PRML \u7b2c 13.2 \u7ae0\u7684\u8be6\u7ec6\u7248\uff0c\u4e3b\u8981\u8bb2 HMM MLAPP \u7b2c 18 \u7ae0\u662f PRML \u7b2c 13.3 \u7ae0\u7684\u8be6\u7ec6\u7248\uff0c\u4e3b\u8981\u8bb2 LDS \u8bfb\u5b8c PRML \u7b2c 13 \u7ae0\u4e4b\u540e\uff0c\u518d\u53bb\u8bfb PRML \u7b2c 8 \u7ae0 (Graphical Models) -- \u6b64\u65f6\u8fd9\u90e8\u5206\u5e94\u8be5\u4f1a\u8bfb\u5f97\u5f88\u8f7b\u677e \u4ee5\u4e0a\u7684\u5185\u5bb9\u53ef\u4ee5\u8fdb\u4e00\u6b65\u5bf9\u7167 CMU 10-708 PGM \u8bfe\u7a0b\u6750\u6599 \u5230\u76ee\u524d\u4e3a\u6b62\uff0c\u5e94\u8be5\u80fd\u591f\u638c\u63e1 \u6982\u7387\u6a21\u578b\u7684\u57fa\u7840\u5b9a\u4e49 \u7cbe\u51c6\u63a8\u65ad - Sum-Product \u8fd1\u4f3c\u63a8\u65ad - MCMC \u8fd1\u4f3c\u63a8\u65ad - VI \u7136\u540e\u5c31\u53ef\u4ee5\u53bb\u505a\u66f4\u8fdb\u9636\u7684\u5185\u5bb9","title":"\u8fdb\u9636\u8def\u7ebf\u56fe"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_1","text":"\u6b64\u8def\u7ebf\u56fe\u9002\u7528\u4e8e\u5df2\u7ecf\u5b66\u8fc7\u4e86\u57fa\u7840\u673a\u5668\u5b66\u4e60 (ML, NLP, CV, RL) \u7684\u540c\u5b66 (\u9ad8\u5e74\u7ea7\u672c\u79d1\u751f\u6216\u4f4e\u5e74\u7ea7\u7814\u7a76\u751f)\uff0c\u5df2\u7ecf\u53d1\u8868\u8fc7\u81f3\u5c11\u4e00\u7bc7\u9876\u4f1a\u8bba\u6587 (NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, CVPR, ICCV) \u60f3\u8981\u8d70\u673a\u5668\u5b66\u4e60\u79d1\u7814\u8def\u7ebf\u7684\u9009\u624b\u3002 \u6b64\u8def\u7ebf\u7684\u76ee\u6807\u662f\u4e3a\u8bfb\u61c2\u4e0e\u53d1\u8868\u673a\u5668\u5b66\u4e60\u9876\u4f1a\u8bba\u6587\u6253\u4e0b\u7406\u8bba\u57fa\u7840\uff0c\u7279\u522b\u662f Probabilistic Methods \u8fd9\u4e2a track \u4e0b\u7684\u6587\u7ae0 \u673a\u5668\u5b66\u4e60\u8fdb\u9636\u53ef\u80fd\u5b58\u5728\u591a\u79cd\u4e0d\u540c\u7684\u5b66\u4e60\u8def\u7ebf\uff0c\u6b64\u8def\u7ebf\u53ea\u80fd\u4ee3\u8868\u4f5c\u8005 Yao Fu \u6240\u7406\u89e3\u7684\u6700\u4f73\u8def\u5f84\uff0c\u4fa7\u91cd\u4e8e\u8d1d\u53f6\u65af\u5b66\u6d3e\u4e0b\u7684\u6982\u7387\u5efa\u6a21\u65b9\u6cd5\uff0c\u4e5f\u4f1a\u6d89\u53ca\u5230\u5404\u9879\u76f8\u5173\u5b66\u79d1\u7684\u4ea4\u53c9\u77e5\u8bc6\u3002","title":"\u673a\u5668\u5b66\u4e60\u8fdb\u9636"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_2","text":"PRML: Pattern Recognition and Machine Learning. Christopher Bishop \u7ecf\u5178\u8d1d\u53f6\u65af\u5b66\u6d3e\u6559\u6750 AoS: All of Statistics. Larry Wasserman \u7ecf\u5178\u9891\u7387\u5b66\u6d3e\u6559\u6750 \u6240\u4ee5\u8fd9\u4e24\u672c\u4e66\u521a\u597d\u76f8\u8f85\u76f8\u6210","title":"\u5fc5\u8bfb\u6559\u6750"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_3","text":"MLAPP: Machine Learning: A Probabilistic Perspective. Kevin Murphy Convex Optimization. Stephen Boyd and Lieven Vandenberghe","title":"\u5b57\u5178"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_4","text":"W&J: Graphical Models, Exponential Families, and Variational Inference. Martin Wainwright and Michael Jordan Theory of Point Estimation. E. L. Lehmann and George Casella","title":"\u8fdb\u9636\u4e66\u7c4d"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_5","text":"","title":"\u5982\u4f55\u9605\u8bfb"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#guidelines","text":"\u5fc5\u8bfb\u6559\u6750\u5c31\u662f\u4e00\u5b9a\u8981\u8bfb\u7684\u6559\u6750 \u5b57\u5178\u7684\u610f\u601d\u662f\uff0c\u4e00\u822c\u60c5\u51b5\u4e0b\u4e0d\u7ba1\u5b83\uff0c\u4f46\u5f53\u9047\u5230\u4e86\u4e0d\u61c2\u7684\u6982\u5ff5\u7684\u65f6\u5019\uff0c\u5c31\u53bb\u5b57\u5178\u91cc\u9762\u67e5\uff08\u800c\u4e0d\u662f\u7ef4\u57fa\u767e\u79d1\uff09 \u8fdb\u9636\u4e66\u7c4d\u5148\u4e0d\u8bfb\uff0c\u5148\u8bfb\u5b8c\u5fc5\u8bfb\u4e66\u7c4d\u3002\u5fc5\u8bfb\u4e66\u7c4d\u4e00\u822c\u90fd\u662f\u8981\u524d\u524d\u540e\u540e\u53cd\u590d\u770b\u8fc7 N \u904d\u624d\u7b97\u8bfb\u5b8c \u8bfb\u7684\u8fc7\u7a0b\u4e2d\uff0c\u6700\u91cd\u8981\u7684\u8bfb\u6cd5\u5c31\u662f\u5bf9\u6bd4\u9605\u8bfb (contrastive-comparative reading)\uff1a\u540c\u65f6\u6253\u5f00\u4e24\u672c\u4e66\u8bb2\u540c\u4e00\u4e3b\u9898\u7684\u7ae0\u8282\uff0c\u7136\u540e\u5bf9\u6bd4\u76f8\u540c\u70b9\u548c\u4e0d\u540c\u70b9\u548c\u8054\u7cfb \u8bfb\u7684\u8fc7\u7a0b\u4e2d\uff0c\u5c3d\u91cf\u53bb\u56de\u60f3\u4e4b\u524d\u8bfb\u8fc7\u7684\u8bba\u6587\uff0c\u6bd4\u8f83\u8bba\u6587\u548c\u6559\u6750\u7684\u76f8\u540c\u70b9\u4e0e\u4e0d\u540c\u70b9","title":"Guidelines"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_6","text":"\u5148\u8bfb AoS \u7b2c\u516d\u7ae0: Models, Statistical Inference and Learning\uff0c\u8fd9\u4e00\u90e8\u5206\u662f\u6700\u57fa\u7840\u7684\u79d1\u666e \u7136\u540e\u8bfb PRML \u7b2c 10, 11 \u7ae0 \u7b2c 10 \u7ae0\u7684\u5185\u5bb9\u662f Variational Inference, \u7b2c 11 \u7ae0\u7684\u5185\u5bb9\u662f MCMC, \u8fd9\u4e24\u79cd\u65b9\u6cd5\u662f\u8d1d\u53f6\u65af\u63a8\u65ad\u7684\u4e24\u6761\u6700\u4e3b\u8981\u8def\u7ebf \u5982\u679c\u5728\u8bfb PRML \u7684\u8fc7\u7a0b\u4e2d\u53d1\u73b0\u6709\u4efb\u4f55\u4e0d\u61c2\u7684\u540d\u8bcd\uff0c\u5c31\u53bb\u7ffb\u524d\u9762\u7684\u7ae0\u8282\u3002\u5f88\u5927\u6982\u7387\u80fd\u591f\u5728\u7b2c 3\uff0c4 \u7ae0\u627e\u5230\u76f8\u5bf9\u5e94\u7684\u5b9a\u4e49\uff1b\u5982\u679c\u627e\u4e0d\u5230\u6216\u8005\u4e0d\u591f\u8be6\u7ec6\uff0c\u5c31\u53bb\u67e5 MLAPP AoS \u7b2c 8 \u7ae0 (Parametric Inference) \u548c\u7b2c 11 \u7ae0 (Bayesian Inference) \u4e5f\u53ef\u4ee5\u4f5c\u4e3a\u53c2\u8003\u3002\u6700\u597d\u7684\u65b9\u6cd5\u662f\u591a\u672c\u4e66\u5bf9\u6bd4\u9605\u8bfb\uff0c\u6d41\u7a0b\u5982\u4e0b \u5047\u8bbe\u6211\u5728\u8bfb PRML \u7b2c 10 \u7ae0\u7684\u65f6\u5019\u53d1\u73b0\u4e86\u4e00\u4e2a\u4e0d\u61c2\u7684\u8bcd\uff1aposterior inference \u4e8e\u662f\u6211\u5f80\u524d\u7ffb\uff0c\u7ffb\u5230\u4e86\u7b2c 3 \u7ae0 (Linear Model for Regression)\uff0c\u770b\u5230\u4e86\u6700\u7b80\u5355\u7684 posterior \u7136\u540e\u6211\u63a5\u7740\u7ffb AoS\uff0c\u7ffb\u5230\u4e86\u7b2c 11 \u7ae0\uff0c\u4e5f\u6709\u5bf9 posterior \u7684\u63cf\u8ff0 \u7136\u540e\u6211\u5bf9\u6bd4 PRML \u7b2c 10 \u7ae0\uff0c\u7b2c 3 \u7ae0\uff0cAoS \u7b2c 11 \u7ae0\uff0c\u4e09\u5904\u4e0d\u540c\u5730\u65b9\u5bf9 posterior \u7684\u89e3\u8bfb\uff0c\u6bd4\u8f83\u5176\u76f8\u540c\u70b9\u548c\u4e0d\u540c\u70b9\u548c\u8054\u7cfb \u8bfb\u5b8c PRML \u7b2c 10 \u548c 11 \u7ae0\u4e4b\u540e\uff0c\u63a5\u7740\u8bfb AoS \u7b2c 24 \u7ae0 (Simulation Methods)\uff0c\u7136\u540e\u628a\u5b83\u548c PRML \u7b2c 11 \u7ae0\u5bf9\u6bd4\u9605\u8bfb -- \u8fd9\u4fe9\u90fd\u662f\u8bb2 MCMC \u5982\u679c\u5230\u6b64\u5904\u53d1\u73b0\u8fd8\u6709\u57fa\u7840\u6982\u5ff5\u8bfb\u4e0d\u61c2\uff0c\u5c31\u56de\u5230 PRML \u7b2c 3 \u7ae0\uff0c\u628a\u5b83\u548c AoS \u7b2c 11 \u7ae0\u5bf9\u6bd4\u9605\u8bfb Again\uff0c\u5bf9\u6bd4\u9605\u8bfb\u975e\u5e38\u91cd\u8981\uff0c\u4e00\u5b9a\u8981\u628a\u4e0d\u540c\u672c\u4e66\u7684\u7c7b\u4f3c\u5185\u5bb9\u540c\u65f6\u6446\u5728\u9762\u524d\u76f8\u4e92\u5bf9\u6bd4\uff0c\u8fd9\u6837\u53ef\u4ee5\u663e\u8457\u589e\u5f3a\u8bb0\u5fc6 \u7136\u540e\u8bfb PRML \u7b2c 13 \u7ae0\uff08\u8df3\u8fc7\u7b2c 12 \u7ae0\uff09\uff0c\u8fd9\u4e00\u7ae0\u53ef\u4ee5\u548c MLAPP \u7684\u7b2c 17, 18 \u7ae0\u5bf9\u6bd4\u9605\u8bfb MLAPP \u7b2c 17 \u7ae0\u662f PRML \u7b2c 13.2 \u7ae0\u7684\u8be6\u7ec6\u7248\uff0c\u4e3b\u8981\u8bb2 HMM MLAPP \u7b2c 18 \u7ae0\u662f PRML \u7b2c 13.3 \u7ae0\u7684\u8be6\u7ec6\u7248\uff0c\u4e3b\u8981\u8bb2 LDS \u8bfb\u5b8c PRML \u7b2c 13 \u7ae0\u4e4b\u540e\uff0c\u518d\u53bb\u8bfb PRML \u7b2c 8 \u7ae0 (Graphical Models) -- \u6b64\u65f6\u8fd9\u90e8\u5206\u5e94\u8be5\u4f1a\u8bfb\u5f97\u5f88\u8f7b\u677e \u4ee5\u4e0a\u7684\u5185\u5bb9\u53ef\u4ee5\u8fdb\u4e00\u6b65\u5bf9\u7167 CMU 10-708 PGM \u8bfe\u7a0b\u6750\u6599 \u5230\u76ee\u524d\u4e3a\u6b62\uff0c\u5e94\u8be5\u80fd\u591f\u638c\u63e1 \u6982\u7387\u6a21\u578b\u7684\u57fa\u7840\u5b9a\u4e49 \u7cbe\u51c6\u63a8\u65ad - Sum-Product \u8fd1\u4f3c\u63a8\u65ad - MCMC \u8fd1\u4f3c\u63a8\u65ad - VI \u7136\u540e\u5c31\u53ef\u4ee5\u53bb\u505a\u66f4\u8fdb\u9636\u7684\u5185\u5bb9","title":"\u57fa\u7840\u8def\u5f84"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224n/","text":"CS224n: Natural Language Processing \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6df1\u5ea6\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 Stanford \u7684 NLP \u5165\u95e8\u8bfe\u7a0b\uff0c\u7531\u81ea\u7136\u8bed\u8a00\u5904\u7406\u9886\u57df\u7684\u5de8\u4f6c Chris Manning \u9886\u8854\u6559\u6388\uff08word2vec \u7b97\u6cd5\u7684\u5f00\u521b\u8005\uff09\u3002\u5185\u5bb9\u8986\u76d6\u4e86\u8bcd\u5411\u91cf\u3001RNN\u3001LSTM\u3001Seq2Seq \u6a21\u578b\u3001\u673a\u5668\u7ffb\u8bd1\u3001\u6ce8\u610f\u529b\u673a\u5236\u3001Transformer \u7b49\u7b49 NLP \u9886\u57df\u7684\u6838\u5fc3\u77e5\u8bc6\u70b9\u3002 5 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a\u96be\u5ea6\u5faa\u5e8f\u6e10\u8fdb\uff0c\u5206\u522b\u662f\u8bcd\u5411\u91cf\u3001word2vec \u7b97\u6cd5\u3001Dependency parsing\u3001\u673a\u5668\u7ffb\u8bd1\u4ee5\u53ca Transformer \u7684 fine-tune\u3002 \u6700\u7ec8\u7684\u5927\u4f5c\u4e1a\u662f\u5728 Stanford \u8457\u540d\u7684 SQuAD \u6570\u636e\u96c6\u4e0a\u8bad\u7ec3 QA \u6a21\u578b\uff0c\u6709\u5b66\u751f\u7684\u5927\u4f5c\u4e1a\u751a\u81f3\u76f4\u63a5\u53d1\u8868\u4e86\u9876\u4f1a\u8bba\u6587\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs224n/index.html \u8bfe\u7a0b\u89c6\u9891\uff1aB \u7ad9\u641c\u7d22 CS224n \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a http://web.stanford.edu/class/cs224n/index.html \uff0c5 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a + 1 \u4e2a Final Project \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS224n - GitHub \u4e2d\u3002","title":"Stanford CS224n: Natural Language Processing"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224n/#cs224n-natural-language-processing","text":"","title":"CS224n: Natural Language Processing"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224n/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6df1\u5ea6\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 Stanford \u7684 NLP \u5165\u95e8\u8bfe\u7a0b\uff0c\u7531\u81ea\u7136\u8bed\u8a00\u5904\u7406\u9886\u57df\u7684\u5de8\u4f6c Chris Manning \u9886\u8854\u6559\u6388\uff08word2vec \u7b97\u6cd5\u7684\u5f00\u521b\u8005\uff09\u3002\u5185\u5bb9\u8986\u76d6\u4e86\u8bcd\u5411\u91cf\u3001RNN\u3001LSTM\u3001Seq2Seq \u6a21\u578b\u3001\u673a\u5668\u7ffb\u8bd1\u3001\u6ce8\u610f\u529b\u673a\u5236\u3001Transformer \u7b49\u7b49 NLP \u9886\u57df\u7684\u6838\u5fc3\u77e5\u8bc6\u70b9\u3002 5 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a\u96be\u5ea6\u5faa\u5e8f\u6e10\u8fdb\uff0c\u5206\u522b\u662f\u8bcd\u5411\u91cf\u3001word2vec \u7b97\u6cd5\u3001Dependency parsing\u3001\u673a\u5668\u7ffb\u8bd1\u4ee5\u53ca Transformer \u7684 fine-tune\u3002 \u6700\u7ec8\u7684\u5927\u4f5c\u4e1a\u662f\u5728 Stanford \u8457\u540d\u7684 SQuAD \u6570\u636e\u96c6\u4e0a\u8bad\u7ec3 QA \u6a21\u578b\uff0c\u6709\u5b66\u751f\u7684\u5927\u4f5c\u4e1a\u751a\u81f3\u76f4\u63a5\u53d1\u8868\u4e86\u9876\u4f1a\u8bba\u6587\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224n/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs224n/index.html \u8bfe\u7a0b\u89c6\u9891\uff1aB \u7ad9\u641c\u7d22 CS224n \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a http://web.stanford.edu/class/cs224n/index.html \uff0c5 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a + 1 \u4e2a Final Project","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224n/#_3","text":"@PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS224n - GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224w/","text":"CS224w: Machine Learning with Graphs \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6df1\u5ea6\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython, LaTeX \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 Stanford \u7684\u56fe\u795e\u7ecf\u7f51\u7edc\u5165\u95e8\u8bfe\uff0c\u8fd9\u95e8\u8bfe\u6211\u6ca1\u6709\u4e0a\u8fc7\uff0c\u4f46\u4f17\u591a\u505a GNN \u7684\u670b\u53cb\u90fd\u5411\u6211\u529b\u8350\u8fc7\u8fd9\u95e8\u8bfe\uff0c\u60f3\u5fc5 Stanford \u7684\u8bfe\u8d28\u91cf\u8fd8\u662f\u4e00\u5982\u65e2\u5f80\u5730\u6709\u4fdd\u8bc1\u7684\u3002\u53e6\u5916\u5c31\u662f\u8fd9\u95e8\u8bfe\u7684\u6388\u8bfe\u8001\u5e08\u975e\u5e38\u5e74\u8f7b\u5e05\u6c14:) \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs224w/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.youtube.com/watch?v=JAB_plj2rbA \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a http://web.stanford.edu/class/cs224w/ \uff0c6 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a\uff0c3 \u4e2a LaTeX \u4e66\u9762\u4f5c\u4e1a","title":"Stanford CS224w: Machine Learning with Graphs"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224w/#cs224w-machine-learning-with-graphs","text":"","title":"CS224w: Machine Learning with Graphs"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224w/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6df1\u5ea6\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython, LaTeX \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 Stanford \u7684\u56fe\u795e\u7ecf\u7f51\u7edc\u5165\u95e8\u8bfe\uff0c\u8fd9\u95e8\u8bfe\u6211\u6ca1\u6709\u4e0a\u8fc7\uff0c\u4f46\u4f17\u591a\u505a GNN \u7684\u670b\u53cb\u90fd\u5411\u6211\u529b\u8350\u8fc7\u8fd9\u95e8\u8bfe\uff0c\u60f3\u5fc5 Stanford \u7684\u8bfe\u8d28\u91cf\u8fd8\u662f\u4e00\u5982\u65e2\u5f80\u5730\u6709\u4fdd\u8bc1\u7684\u3002\u53e6\u5916\u5c31\u662f\u8fd9\u95e8\u8bfe\u7684\u6388\u8bfe\u8001\u5e08\u975e\u5e38\u5e74\u8f7b\u5e05\u6c14:)","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224w/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs224w/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.youtube.com/watch?v=JAB_plj2rbA \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a http://web.stanford.edu/class/cs224w/ \uff0c6 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a\uff0c3 \u4e2a LaTeX \u4e66\u9762\u4f5c\u4e1a","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS230/","text":"Coursera: Deep Learning \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u673a\u5668\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 \u5434\u6069\u8fbe\u5728 Coursera \u5f00\u8bbe\u7684\u53e6\u4e00\u95e8\u7f51\u7ea2\u8bfe\u7a0b\uff0c\u5b66\u4e60\u8005\u65e0\u6570\uff0c\u582a\u79f0\u5723\u7ecf\u7ea7\u7684\u6df1\u5ea6\u5b66\u4e60\u5165\u95e8\u8bfe\u3002\u6df1\u5165\u6d45\u51fa\u7684\u8bb2\u89e3\uff0c\u773c\u82b1\u7f2d\u4e71\u7684 Project\u3002\u4ece\u6700\u57fa\u7840\u7684\u795e\u7ecf\u7f51\u7edc\uff0c\u5230 CNN, RNN\uff0c\u518d\u5230\u6700\u8fd1\u5927\u70ed\u7684 Transformer\u3002\u5b66\u5b8c\u8fd9\u95e8\u8bfe\uff0c\u4f60\u5c06\u521d\u6b65\u638c\u63e1\u6df1\u5ea6\u5b66\u4e60\u9886\u57df\u5fc5\u5907\u7684\u77e5\u8bc6\u548c\u6280\u80fd\uff0c\u5e76\u4e14\u53ef\u4ee5\u5728 Kaggle \u4e2d\u53c2\u52a0\u81ea\u5df1\u611f\u5174\u8da3\u7684\u6bd4\u8d5b\uff0c\u5728\u5b9e\u8df5\u4e2d\u953b\u70bc\u81ea\u5df1\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://www.coursera.org/specializations/deep-learning \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.coursera.org/specializations/deep-learning \uff0cB\u7ad9\u6709\u642c\u8fd0 \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://www.coursera.org/specializations/deep-learning","title":"Coursera: Deep Learning"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS230/#coursera-deep-learning","text":"","title":"Coursera: Deep Learning"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS230/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u673a\u5668\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 \u5434\u6069\u8fbe\u5728 Coursera 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https://www.coursera.org/specializations/deep-learning \uff0cB\u7ad9\u6709\u642c\u8fd0 \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://www.coursera.org/specializations/deep-learning","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS231/","text":"CS231n: CNN for Visual Recognition \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u673a\u5668\u5b66\u4e60\u57fa\u7840 \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 Stanford \u7684 CV \u5165\u95e8\u8bfe\uff0c\u7531\u8ba1\u7b97\u673a\u9886\u57df\u7684\u5de8\u4f6c\u674e\u98de\u98de\u9662\u58eb\u9886\u8854\u6559\u6388\uff08CV \u9886\u57df\u5212\u65f6\u4ee3\u7684\u8457\u540d\u6570\u636e\u96c6 ImageNet 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Regression\u3001Classification\u3001CNN\u3001Self-Attention\u3001Transformer\u3001GAN\u3001BERT\u3001Anomaly Detection\u3001Explainable AI\u3001Attack\u3001Adaptation\u3001 RL\u3001Compression\u3001Life-Long Learning \u4ee5\u53ca Meta Learning\u3002\u53ef\u8c13\u662f\u5305\u7f57\u4e07\u8c61\uff0c\u80fd\u8ba9\u5b66\u751f\u5bf9\u4e8e\u6df1\u5ea6\u5b66\u4e60\u7684\u7edd\u5927\u591a\u6570\u9886\u57df\u90fd\u6709\u4e00\u5b9a\u4e86\u89e3\uff0c\u4ece\u800c\u53ef\u4ee5\u8fdb\u4e00\u6b65\u9009\u62e9\u60f3\u8981\u6df1\u5165\u7684\u65b9\u5411\u8fdb\u884c\u5b66\u4e60\u3002 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Oppenheim \u597d\u7684\uff0c\u4e0a\u8fd9\u95e8\u8bfe\u7684\u7406\u7531\u5df2\u7ecf\u8db3\u591f\u4e86\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/Signals_and_Systems_AVO/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a https://ocw.mit.edu/resources/res-6-007-signals-and-systems-spring-2011/index.htm \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1CZ4y1j7hs \u8bfe\u7a0b\u6559\u6750\uff1aSignals and Systems, 2nd Edition \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://ocw.mit.edu/resources/res-6-007-signals-and-systems-spring-2011/assignments","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/signal/","text":"UCB EE120: Signal and Systems \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS61A, CS70\uff0c\u5fae\u79ef\u5206\uff0c\u7ebf\u6027\u4ee3\u6570 \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 \u8fd9\u95e8\u8bfe\u6700\u7cbe\u534e\u7684\u90e8\u5206\u5c31\u662f 6 \u4e2a\u8d85\u6709\u8da3\u7684\u7f16\u7a0b\u4f5c\u4e1a\u4e86\uff0c\u4f1a\u8ba9\u4f60\u7528 Python \u901a\u8fc7\u5b66\u4e60\u5230\u7684\u4fe1\u53f7\u4e0e\u7cfb\u7edf\u7684\u7406\u8bba\u77e5\u8bc6\uff0c\u89e3\u51b3\u5404\u7c7b\u5b9e\u9645\u95ee\u9898\u3002\u4f8b\u5982 lab3 \u4f1a\u8ba9\u4f60\u5b9e\u73b0 FFT \u7b97\u6cd5\uff0c\u5e76\u548c Numpy \u7684\u5b98\u65b9\u5b9e\u73b0\u8fdb\u884c\u6027\u80fd\u5bf9\u6bd4\uff1blab4 \u4f1a\u901a\u8fc7\u5206\u6790\u624b\u6307\u5934\u7684\u5f71\u50cf\u6570\u636e\u63a8\u65ad\u5fc3\u7387\uff1blab5 \u5c31\u66f4\u725b\u4e86\uff0c\u4f1a\u8ba9\u4f60\u7ed9\u54c8\u52c3\u671b\u8fdc\u955c\u62cd\u5230\u7684\u7167\u7247\u8fdb\u884c\u964d\u566a\u5904\u7406\uff0c\u6062\u590d\u7eda\u70c2\u6e05\u6670\u7684\u661f\u7a7a\uff1blab6 \u4f1a\u8ba9\u4f60\u6784\u9020\u4e00\u4e2a\u53cd\u9988\u7cfb\u7edf\uff0c\u5e73\u8861\u5c0f\u8f66\u4e0a\u7684\u7ec6\u6746\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://inst.eecs.berkeley.edu/~ee120/fa19/ \u8bfe\u7a0b\u6559\u6750\uff1a\u53c2\u89c1\u8bfe\u7a0b notes \u8bfe\u7a0b\u4f5c\u4e1a\uff1a5 \u4e2a\u4e66\u9762\u4f5c\u4e1a + 6 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/UCB-EE120 - 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GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"%E7%A8%8B%E5%BA%8F%E8%AF%AD%E8%A8%80%E8%AE%BE%E8%AE%A1/CS242/","text":"","title":"CS242"},{"location":"%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/CS161/","text":"UCB CS161: Computer Security \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS61A, CS61B, CS61C \u7f16\u7a0b\u8bed\u8a00\uff1aC, Go \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 \u4f2f\u514b\u5229\u7684\u8ba1\u7b97\u673a\u7cfb\u7edf\u5b89\u5168\u8bfe\u7a0b\uff0c\u8bfe\u7a0b\u5185\u5bb9\u5206\u4e3a5\u4e2a\u90e8\u5206\uff1a Security principles: how to design a secure system Memory safety: buffer overflow attack Cryptography: symmetric encryption, asymmetric encryption, MAC, digital signature ......... Web: SQL-injection, XSS, XSRF ....... Networking: attacks for each layer \u8fd9\u95e8\u8bfe\u8ba9\u6211\u5370\u8c61\u6700\u4e3a\u6df1\u523b\u7684\u90e8\u5206\u662f Project2\uff0c\u8ba9\u4f60\u7528 Go \u8bed\u8a00\u8bbe\u8ba1\u548c\u5b9e\u73b0\u4e00\u4e2a\u5b89\u5168\u7684\u6587\u4ef6\u5206\u4eab\u7cfb\u7edf\u3002\u6211\u82b1\u4e86\u6574\u6574\u4e09\u5929\u624d\u5b8c\u6210\u4e86\u8fd9\u4e2a\u975e\u5e38\u8650\u7684 Project\uff0c\u603b\u4ee3\u7801\u91cf\u8d85\u8fc7 3k \u884c\u3002\u5728\u8fd9\u6837\u5bc6\u96c6\u578b\u7684\u5f00\u53d1\u8fc7\u7a0b\u4e2d\uff0c\u80fd\u6781\u5927\u5730\u953b\u70bc\u4f60\u8bbe\u8ba1\u548c\u5b9e\u73b0\u4e00\u4e2a\u5b89\u5168\u7cfb\u7edf\u7684\u80fd\u529b\u3002 2020 \u5e74\u590f\u5b63\u5b66\u671f\u7684\u7248\u672c\u5f00\u6e90\u4e86\u8bfe\u7a0b\u5f55\u5f71\uff0c\u5927\u5bb6\u53ef\u4ee5\u5728\u4e0b\u9762\u7684\u8bfe\u7a0b\u7f51\u7ad9\u94fe\u63a5\u91cc\u627e\u5230\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://su20.cs161.org/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a https://textbook.cs161.org/ \u8bfe\u7a0b\u4f5c\u4e1a\uff1a7 \u4e2a\u5728\u7ebf HW + 3 \u4e2a Lab + 3 \u4e2a Project \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/UCB-CS161 - GitHub \u4e2d\u3002","title":"UCB CS161: Computer Security"},{"location":"%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/CS161/#ucb-cs161-computer-security","text":"","title":"UCB CS161: Computer Security"},{"location":"%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/CS161/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS61A, CS61B, CS61C \u7f16\u7a0b\u8bed\u8a00\uff1aC, Go \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 \u4f2f\u514b\u5229\u7684\u8ba1\u7b97\u673a\u7cfb\u7edf\u5b89\u5168\u8bfe\u7a0b\uff0c\u8bfe\u7a0b\u5185\u5bb9\u5206\u4e3a5\u4e2a\u90e8\u5206\uff1a Security principles: how to design a secure system Memory safety: buffer overflow attack Cryptography: symmetric encryption, asymmetric encryption, MAC, digital signature ......... Web: SQL-injection, XSS, XSRF ....... Networking: attacks for each layer \u8fd9\u95e8\u8bfe\u8ba9\u6211\u5370\u8c61\u6700\u4e3a\u6df1\u523b\u7684\u90e8\u5206\u662f Project2\uff0c\u8ba9\u4f60\u7528 Go \u8bed\u8a00\u8bbe\u8ba1\u548c\u5b9e\u73b0\u4e00\u4e2a\u5b89\u5168\u7684\u6587\u4ef6\u5206\u4eab\u7cfb\u7edf\u3002\u6211\u82b1\u4e86\u6574\u6574\u4e09\u5929\u624d\u5b8c\u6210\u4e86\u8fd9\u4e2a\u975e\u5e38\u8650\u7684 Project\uff0c\u603b\u4ee3\u7801\u91cf\u8d85\u8fc7 3k \u884c\u3002\u5728\u8fd9\u6837\u5bc6\u96c6\u578b\u7684\u5f00\u53d1\u8fc7\u7a0b\u4e2d\uff0c\u80fd\u6781\u5927\u5730\u953b\u70bc\u4f60\u8bbe\u8ba1\u548c\u5b9e\u73b0\u4e00\u4e2a\u5b89\u5168\u7cfb\u7edf\u7684\u80fd\u529b\u3002 2020 \u5e74\u590f\u5b63\u5b66\u671f\u7684\u7248\u672c\u5f00\u6e90\u4e86\u8bfe\u7a0b\u5f55\u5f71\uff0c\u5927\u5bb6\u53ef\u4ee5\u5728\u4e0b\u9762\u7684\u8bfe\u7a0b\u7f51\u7ad9\u94fe\u63a5\u91cc\u627e\u5230\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/CS161/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a https://su20.cs161.org/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a https://textbook.cs161.org/ \u8bfe\u7a0b\u4f5c\u4e1a\uff1a7 \u4e2a\u5728\u7ebf HW + 3 \u4e2a Lab + 3 \u4e2a Project","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/CS161/#_3","text":"@PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/UCB-CS161 - GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/MIT6.858/","text":"MIT 6.858: Computer System Security \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784\uff0c\u5bf9\u8ba1\u7b97\u673a\u7cfb\u7edf\u6709\u521d\u6b65\u4e86\u89e3 \u7f16\u7a0b\u8bed\u8a00\uff1aC, Python \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 MIT \u7684\u8ba1\u7b97\u673a\u7cfb\u7edf\u5b89\u5168\u8bfe\u7a0b\uff0c\u5b9e\u9a8c\u73af\u5883\u662f\u4e00\u4e2a Web Application Zoobar\u3002\u5b66\u751f\u5b66\u4e60\u653b\u9632\u6280\u672f\u5e76\u5e94\u7528\u4e8e\u8be5 Web Application\u3002 Lab 1: you will explore the zoobar web application, and use buffer overflow attacks to break its security properties. Lab 2: you will improve the zoobar web application by using privilege separation, so that if one component is compromised, the adversary doesn't get control over the whole web application. Lab 3: you will build a program analysis tool based on symbolic execution to find bugs in Python code such as the zoobar web application. Lab 4: you will improve the zoobar application against browser attacks. \u8fd9\u4e2a\u8bfe\u6211\u4e3b\u8981\u662f\u505a\u4e86 Lab 3\u3002Lab 3 \u662f\u901a\u8fc7\u6df7\u5408\u7b26\u53f7\u6267\u884c\u6765\u904d\u5386\u7a0b\u5e8f\u7684\u6240\u6709\u5206\u652f\uff0c\u7406\u89e3\u4e86\u7b26\u53f7\u6267\u884c\u7684\u601d\u60f3\u540e Lab \u5e76\u4e0d\u96be\u505a\u3002\u8fd9\u4e2a Lab \u76f4\u89c2\u5c55\u793a\u7b26\u53f7\u6267\u884c\u8fd9\u79cd\u6280\u672f\u7684\u4f7f\u7528\u65b9\u6cd5\u3002 \u8fd9\u4e2a\u8bfe\u7684 Final Project \u662f\u5b9e\u73b0 SecFS \uff0c\u4e00\u4e2a\u8fdc\u7aef\u6587\u4ef6\u7cfb\u7edf\uff0c\u9762\u5bf9\u5b8c\u5168\u4e0d\u53ef\u4fe1\u7684\u670d\u52a1\u5668\uff0c\u63d0\u4f9b\u673a\u5bc6\u6027\u548c\u5b8c\u6574\u6027\u3002\u53c2\u8003\u8bba\u6587\u4e3a SUNDR \u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://css.csail.mit.edu/6.858/2022/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a4 \u4e2a Lab + Final Project / Lab5","title":"MIT 6.858: Computer System Security"},{"location":"%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/MIT6.858/#mit-6858-computer-system-security","text":"","title":"MIT 6.858: Computer System Security"},{"location":"%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/MIT6.858/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784\uff0c\u5bf9\u8ba1\u7b97\u673a\u7cfb\u7edf\u6709\u521d\u6b65\u4e86\u89e3 \u7f16\u7a0b\u8bed\u8a00\uff1aC, Python \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 MIT \u7684\u8ba1\u7b97\u673a\u7cfb\u7edf\u5b89\u5168\u8bfe\u7a0b\uff0c\u5b9e\u9a8c\u73af\u5883\u662f\u4e00\u4e2a Web Application Zoobar\u3002\u5b66\u751f\u5b66\u4e60\u653b\u9632\u6280\u672f\u5e76\u5e94\u7528\u4e8e\u8be5 Web Application\u3002 Lab 1: you will explore the zoobar web application, and use buffer overflow attacks to break its security properties. Lab 2: you will improve the zoobar web application by using privilege separation, so that if one component is compromised, the adversary doesn't get control over the whole web application. Lab 3: you will build a program analysis tool based on symbolic execution to find bugs in Python code such as the zoobar web application. Lab 4: you will improve the zoobar application against browser attacks. \u8fd9\u4e2a\u8bfe\u6211\u4e3b\u8981\u662f\u505a\u4e86 Lab 3\u3002Lab 3 \u662f\u901a\u8fc7\u6df7\u5408\u7b26\u53f7\u6267\u884c\u6765\u904d\u5386\u7a0b\u5e8f\u7684\u6240\u6709\u5206\u652f\uff0c\u7406\u89e3\u4e86\u7b26\u53f7\u6267\u884c\u7684\u601d\u60f3\u540e Lab \u5e76\u4e0d\u96be\u505a\u3002\u8fd9\u4e2a Lab \u76f4\u89c2\u5c55\u793a\u7b26\u53f7\u6267\u884c\u8fd9\u79cd\u6280\u672f\u7684\u4f7f\u7528\u65b9\u6cd5\u3002 \u8fd9\u4e2a\u8bfe\u7684 Final Project \u662f\u5b9e\u73b0 SecFS \uff0c\u4e00\u4e2a\u8fdc\u7aef\u6587\u4ef6\u7cfb\u7edf\uff0c\u9762\u5bf9\u5b8c\u5168\u4e0d\u53ef\u4fe1\u7684\u670d\u52a1\u5668\uff0c\u63d0\u4f9b\u673a\u5bc6\u6027\u548c\u5b8c\u6574\u6027\u3002\u53c2\u8003\u8bba\u6587\u4e3a SUNDR \u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/MIT6.858/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://css.csail.mit.edu/6.858/2022/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a4 \u4e2a Lab + Final Project / Lab5","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106B_CS106X/","text":"Stanford CS106B/X: Programming Abstractions in C++ \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u8ba1\u7b97\u673a\u57fa\u7840 (CS50/CS106A/CS61A or equivalent) \u7f16\u7a0b\u8bed\u8a00\uff1aC++ \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a50-70 \u5c0f\u65f6 Stanford \u7684\u8fdb\u9636\u7f16\u7a0b\u8bfe\uff0cCS106X \u5728\u96be\u5ea6\u548c\u6df1\u5ea6\u4e0a\u4f1a\u6bd4 CS106B \u6709\u6240\u63d0\u9ad8\uff0c\u4f46\u4e3b\u4f53\u5185\u5bb9\u7c7b\u4f3c\u3002\u4e3b\u8981\u901a\u8fc7 C++ \u8bed\u8a00\u8ba9\u5b66\u751f\u5728\u5b9e\u9645\u7684\u7f16\u7a0b\u4f5c\u4e1a\u91cc\u57f9\u517b\u901a\u8fc7\u7f16\u7a0b\u62bd\u8c61\u89e3\u51b3\u5b9e\u9645\u95ee\u9898\u7684\u80fd\u529b\uff0c\u540c\u65f6\u4e5f\u4f1a\u6d89\u53ca\u4e00\u4e9b\u7b80\u5355\u7684\u6570\u636e\u7ed3\u6784\u548c\u7b97\u6cd5\u7684\u77e5\u8bc6\uff0c\u4f46\u603b\u4f53\u6765\u8bf4\u6ca1\u6709\u4e00\u95e8\u4e13\u95e8\u7684\u6570\u636e\u7ed3\u6784\u8bfe\u90a3\u4e48\u7cfb\u7edf\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a CS106B , CS106X \u8bfe\u7a0b\u6559\u6750\uff1a https://web.stanford.edu/class/cs106x/res/reader/CS106BX-Reader.pdf \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1G7411k7jG","title":"Stanford CS106B/X"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106B_CS106X/#stanford-cs106bx-programming-abstractions-in-c","text":"","title":"Stanford CS106B/X: Programming Abstractions in C++"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106B_CS106X/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u8ba1\u7b97\u673a\u57fa\u7840 (CS50/CS106A/CS61A or equivalent) \u7f16\u7a0b\u8bed\u8a00\uff1aC++ \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a50-70 \u5c0f\u65f6 Stanford \u7684\u8fdb\u9636\u7f16\u7a0b\u8bfe\uff0cCS106X \u5728\u96be\u5ea6\u548c\u6df1\u5ea6\u4e0a\u4f1a\u6bd4 CS106B \u6709\u6240\u63d0\u9ad8\uff0c\u4f46\u4e3b\u4f53\u5185\u5bb9\u7c7b\u4f3c\u3002\u4e3b\u8981\u901a\u8fc7 C++ \u8bed\u8a00\u8ba9\u5b66\u751f\u5728\u5b9e\u9645\u7684\u7f16\u7a0b\u4f5c\u4e1a\u91cc\u57f9\u517b\u901a\u8fc7\u7f16\u7a0b\u62bd\u8c61\u89e3\u51b3\u5b9e\u9645\u95ee\u9898\u7684\u80fd\u529b\uff0c\u540c\u65f6\u4e5f\u4f1a\u6d89\u53ca\u4e00\u4e9b\u7b80\u5355\u7684\u6570\u636e\u7ed3\u6784\u548c\u7b97\u6cd5\u7684\u77e5\u8bc6\uff0c\u4f46\u603b\u4f53\u6765\u8bf4\u6ca1\u6709\u4e00\u95e8\u4e13\u95e8\u7684\u6570\u636e\u7ed3\u6784\u8bfe\u90a3\u4e48\u7cfb\u7edf\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106B_CS106X/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a CS106B , CS106X \u8bfe\u7a0b\u6559\u6750\uff1a https://web.stanford.edu/class/cs106x/res/reader/CS106BX-Reader.pdf \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1G7411k7jG","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106L/","text":"CS106L: Standard C++ Programming \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6700\u597d\u638c\u63e1\u81f3\u5c11\u4e00\u95e8\u7f16\u7a0b\u8bed\u8a00 \u7f16\u7a0b\u8bed\u8a00\uff1aC++ \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a20 \u5c0f\u65f6 \u6211\u4ece\u5927\u4e00\u5f00\u59cb\u4e00\u76f4\u90fd\u662f\u5199\u7684 C++ \u4ee3\u7801\uff0c\u76f4\u5230\u5b66\u5b8c\u8fd9\u95e8\u8bfe\u6211\u624d\u610f\u8bc6\u5230\uff0c\u6211\u5199\u7684 C++ \u4ee3\u7801\u5927\u6982\u53ea\u662f C \u8bed\u8a00 + cin / cout \u800c\u5df2\u3002 \u8fd9\u95e8\u8bfe\u4f1a\u6df1\u5165\u5230\u5f88\u591a\u6807\u51c6 C++ \u7684\u7279\u6027\u548c\u8bed\u6cd5\uff0c\u8ba9\u4f60\u7f16\u5199\u51fa\u9ad8\u8d28\u91cf\u7684 C++ \u4ee3\u7801\u3002\u4f8b\u5982 auto binding, uniform initialization, lambda function, move semantics\uff0cRAII \u7b49\u6280\u5de7\u90fd\u5728\u6211\u6b64\u540e\u7684\u4ee3\u7801\u751f\u6daf\u4e2d\u88ab\u53cd\u590d\u7528\u5230\uff0c\u975e\u5e38\u5b9e\u7528\u3002 \u503c\u5f97\u4e00\u63d0\u7684\u662f\uff0c\u8fd9\u95e8\u8bfe\u7684\u4f5c\u4e1a\u91cc\u4f60\u4f1a\u5b9e\u73b0\u4e00\u4e2a HashMap\uff08\u7c7b\u4f3c\u4e8e STL \u4e2d\u7684 unordered_map ), \u8fd9\u4e2a\u4f5c\u4e1a\u51e0\u4e4e\u628a\u6574\u4e2a\u8bfe\u7a0b\u4e32\u8054\u4e86\u8d77\u6765\uff0c\u975e\u5e38\u8003\u9a8c\u4ee3\u7801\u80fd\u529b\u3002\u7279\u522b\u662f iterator \u7684\u5b9e\u73b0\uff0c\u505a\u5b8c\u8fd9\u4e2a\u4f5c\u4e1a\u6211\u5f00\u59cb\u7406\u89e3\u4e3a\u4ec0\u4e48 Linus \u5bf9 C/C++ \u55e4\u4e4b\u4ee5\u9f3b\u4e86\uff0c\u56e0\u4e3a\u771f\u7684\u5f88\u96be\u5199\u5bf9\u3002 \u603b\u7684\u6765\u8bb2\u8fd9\u95e8\u8bfe\u5e76\u4e0d\u96be\uff0c\u4f46\u662f\u4fe1\u606f\u91cf\u5f88\u5927\uff0c\u9700\u8981\u4f60\u5728\u4e4b\u540e\u7684\u5f00\u53d1\u5b9e\u8df5\u4e2d\u53cd\u590d\u5de9\u56fa\u3002Stanford \u4e4b\u6240\u4ee5\u5355\u5f00\u4e00\u95e8 C++ \u7684\u7f16\u7a0b\u8bfe\uff0c\u662f\u56e0\u4e3a\u5b83\u540e\u7eed\u7684\u5f88\u591a CS \u8bfe\u7a0b Project \u90fd\u662f\u57fa\u4e8e C++\u7684\u3002\u4f8b\u5982 CS144 \u8ba1\u7b97\u673a\u7f51\u7edc\u548c CS143 \u7f16\u8bd1\u5668\u3002\u8fd9\u4e24\u95e8\u8bfe\u5728\u672c\u4e66\u4e2d\u5747\u6709\u6536\u5f55\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs106l/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.youtube.com/channel/UCSqr6y-eaQT_qZJVUm_4QxQ/playlists \u8bfe\u7a0b\u6559\u6750\uff1a http://web.stanford.edu/class/cs106l/full_course_reader.pdf Assignment1\u4e0b\u8f7d\u7f51\u5740\uff1a https://github.com/snme/cs106L-assignment1 Assignment2\u4e0b\u8f7d\u7f51\u5740\uff1a https://github.com/snme/cs106L-assignment2 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u5177\u4f53\u5185\u5bb9\u89c1\u8bfe\u7a0b\u7f51\u7ad9\uff0c\u6211\u505a\u7684\u65f6\u5019\u4e00\u5171\u662f\u4e24\u4e2a\uff1a \u5b9e\u73b0\u4e00\u4e2a WikiRacer \u7684\u5c0f\u6e38\u620f \u5b9e\u73b0\u4e00\u4e2a\u7c7b\u4f3c STL \u5e93\u7684 HashMap \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS106L - GitHub \u4e2d\u3002","title":"Stanford CS106L: Standard C++ Programming"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106L/#cs106l-standard-c-programming","text":"","title":"CS106L: Standard C++ Programming"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106L/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6700\u597d\u638c\u63e1\u81f3\u5c11\u4e00\u95e8\u7f16\u7a0b\u8bed\u8a00 \u7f16\u7a0b\u8bed\u8a00\uff1aC++ \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a20 \u5c0f\u65f6 \u6211\u4ece\u5927\u4e00\u5f00\u59cb\u4e00\u76f4\u90fd\u662f\u5199\u7684 C++ \u4ee3\u7801\uff0c\u76f4\u5230\u5b66\u5b8c\u8fd9\u95e8\u8bfe\u6211\u624d\u610f\u8bc6\u5230\uff0c\u6211\u5199\u7684 C++ \u4ee3\u7801\u5927\u6982\u53ea\u662f C \u8bed\u8a00 + cin / cout \u800c\u5df2\u3002 \u8fd9\u95e8\u8bfe\u4f1a\u6df1\u5165\u5230\u5f88\u591a\u6807\u51c6 C++ \u7684\u7279\u6027\u548c\u8bed\u6cd5\uff0c\u8ba9\u4f60\u7f16\u5199\u51fa\u9ad8\u8d28\u91cf\u7684 C++ \u4ee3\u7801\u3002\u4f8b\u5982 auto binding, uniform initialization, lambda function, move semantics\uff0cRAII \u7b49\u6280\u5de7\u90fd\u5728\u6211\u6b64\u540e\u7684\u4ee3\u7801\u751f\u6daf\u4e2d\u88ab\u53cd\u590d\u7528\u5230\uff0c\u975e\u5e38\u5b9e\u7528\u3002 \u503c\u5f97\u4e00\u63d0\u7684\u662f\uff0c\u8fd9\u95e8\u8bfe\u7684\u4f5c\u4e1a\u91cc\u4f60\u4f1a\u5b9e\u73b0\u4e00\u4e2a HashMap\uff08\u7c7b\u4f3c\u4e8e STL \u4e2d\u7684 unordered_map ), \u8fd9\u4e2a\u4f5c\u4e1a\u51e0\u4e4e\u628a\u6574\u4e2a\u8bfe\u7a0b\u4e32\u8054\u4e86\u8d77\u6765\uff0c\u975e\u5e38\u8003\u9a8c\u4ee3\u7801\u80fd\u529b\u3002\u7279\u522b\u662f iterator \u7684\u5b9e\u73b0\uff0c\u505a\u5b8c\u8fd9\u4e2a\u4f5c\u4e1a\u6211\u5f00\u59cb\u7406\u89e3\u4e3a\u4ec0\u4e48 Linus \u5bf9 C/C++ \u55e4\u4e4b\u4ee5\u9f3b\u4e86\uff0c\u56e0\u4e3a\u771f\u7684\u5f88\u96be\u5199\u5bf9\u3002 \u603b\u7684\u6765\u8bb2\u8fd9\u95e8\u8bfe\u5e76\u4e0d\u96be\uff0c\u4f46\u662f\u4fe1\u606f\u91cf\u5f88\u5927\uff0c\u9700\u8981\u4f60\u5728\u4e4b\u540e\u7684\u5f00\u53d1\u5b9e\u8df5\u4e2d\u53cd\u590d\u5de9\u56fa\u3002Stanford \u4e4b\u6240\u4ee5\u5355\u5f00\u4e00\u95e8 C++ \u7684\u7f16\u7a0b\u8bfe\uff0c\u662f\u56e0\u4e3a\u5b83\u540e\u7eed\u7684\u5f88\u591a CS \u8bfe\u7a0b Project \u90fd\u662f\u57fa\u4e8e C++\u7684\u3002\u4f8b\u5982 CS144 \u8ba1\u7b97\u673a\u7f51\u7edc\u548c CS143 \u7f16\u8bd1\u5668\u3002\u8fd9\u4e24\u95e8\u8bfe\u5728\u672c\u4e66\u4e2d\u5747\u6709\u6536\u5f55\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106L/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs106l/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.youtube.com/channel/UCSqr6y-eaQT_qZJVUm_4QxQ/playlists \u8bfe\u7a0b\u6559\u6750\uff1a http://web.stanford.edu/class/cs106l/full_course_reader.pdf Assignment1\u4e0b\u8f7d\u7f51\u5740\uff1a https://github.com/snme/cs106L-assignment1 Assignment2\u4e0b\u8f7d\u7f51\u5740\uff1a https://github.com/snme/cs106L-assignment2 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u5177\u4f53\u5185\u5bb9\u89c1\u8bfe\u7a0b\u7f51\u7ad9\uff0c\u6211\u505a\u7684\u65f6\u5019\u4e00\u5171\u662f\u4e24\u4e2a\uff1a \u5b9e\u73b0\u4e00\u4e2a WikiRacer \u7684\u5c0f\u6e38\u620f \u5b9e\u73b0\u4e00\u4e2a\u7c7b\u4f3c STL \u5e93\u7684 HashMap","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106L/#_3","text":"@PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS106L - GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS110L/","text":"CS110L: Safety in Systems Programming \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6700\u597d\u6709\u4e00\u5b9a\u7684\u7f16\u7a0b\u80cc\u666f\u5e76\u5bf9\u8ba1\u7b97\u673a\u7cfb\u7edf\u6709\u521d\u6b65\u7684\u8ba4\u8bc6\u3002 \u7f16\u7a0b\u8bed\u8a00\uff1aRust \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a30 \u5c0f\u65f6 \u5728\u8fd9\u95e8\u8bfe\u4e2d\u4f60\u5c06\u4f1a\u5b66\u4e60 Rust \u8fd9\u95e8\u795e\u5947\u7684\u8bed\u8a00\u3002 \u5982\u679c\u4f60\u5b66\u8fc7 C \u5e76\u63a5\u89e6\u8fc7\u4e00\u4e9b\u7cfb\u7edf\u7f16\u7a0b\u7684\u8bdd\uff0c\u5e94\u8be5\u5bf9 C \u7684\u5185\u5b58\u6cc4\u6f0f\u4ee5\u53ca\u6307\u9488\u7684\u5371\u9669\u6709\u6240\u8033\u95fb\uff0c\u4f46 C \u7684\u5e95\u5c42\u7279\u6027\u4ee5\u53ca\u9ad8\u6548\u4ecd\u7136\u8ba9\u5b83\u5728\u7cfb\u7edf\u7ea7\u7f16\u7a0b\u4e2d\u65e0\u6cd5\u88ab\u4f8b\u5982 Java \u7b49\u81ea\u5e26\u5783\u573e\u6536\u96c6\u673a\u5236\u7684\u9ad8\u7ea7\u8bed\u8a00\u6240\u66ff\u4ee3\u3002\u800c Rust \u7684\u76ee\u6807\u5219\u662f\u5e0c\u671b\u5728 C \u7684\u9ad8\u6548\u57fa\u7840\u4e0a\uff0c\u5f25\u8865\u5176\u5b89\u5168\u4e0d\u8db3\u7684\u7f3a\u70b9\u3002\u56e0\u6b64 Rust \u5728\u8bbe\u8ba1\u4e4b\u521d\uff0c\u5c31\u6709\u5e26\u6709\u5f88\u591a\u7cfb\u7edf\u7f16\u7a0b\u7684\u89c2\u70b9\u3002\u5b66\u4e60 Rust\uff0c\u4e5f\u80fd\u8ba9\u4f60\u4e4b\u540e\u80fd\u7528 C \u8bed\u8a00\u7f16\u5199\u51fa\u66f4\u5b89\u5168\u66f4\u4f18\u96c5\u7684\u7cfb\u7edf\u7ea7\u4ee3\u7801\uff08\u4f8b\u5982\u64cd\u4f5c\u7cfb\u7edf\u7b49\uff09\u3002 \u8fd9\u95e8\u8bfe\u7684\u540e\u534a\u90e8\u5206\u5173\u6ce8\u5728\u5e76\u53d1\uff08concurrency\uff09\u8fd9\u4e00\u4e3b\u9898\u4e0a\uff0c\u4f60\u5c06\u4f1a\u7cfb\u7edf\u5730\u638c\u63e1\u591a\u8fdb\u7a0b\u3001\u591a\u7ebf\u7a0b\u3001\u57fa\u4e8e\u4e8b\u4ef6\u9a71\u52a8\u7684\u5e76\u53d1\u7b49\u82e5\u5e72\u5e76\u53d1\u6280\u672f\uff0c\u5e76\u5728\u7b2c\u4e8c\u4e2a Project \u4e2d\u6bd4\u8f83\u5b83\u4eec\u5404\u81ea\u7684\u4f18\u52a3\u3002Rust \u4e2d \u201cfutures\u201d \u7684\u6982\u5ff5\u975e\u5e38\u6709\u8da3\u548c\u4f18\u96c5\uff0c\u8fd9\u4e9b\u57fa\u7840\u77e5\u8bc6\u5bf9\u4f60\u540e\u7eed\u5bf9\u8ba1\u7b97\u673a\u7cfb\u7edf\u76f8\u5173\u8bfe\u7a0b\u7684\u5b66\u4e60\u5f88\u6709\u5e2e\u52a9\u3002\u53e6\u5916\uff0c\u6e05\u534e\u5927\u5b66\u7684\u64cd\u7edf\u5b9e\u9a8c rCore \u5c31\u662f\u57fa\u4e8e Rust \u7f16\u5199\u7684\uff0c\u5177\u4f53\u53c2\u89c1 \u6587\u6863 \u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://reberhardt.com/cs110l/spring-2020/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://youtu.be/j7AQrtLevUE \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u5171 6 \u4e2a Lab \u548c 2 \u4e2a Project\uff0c\u4f5c\u4e1a\u6587\u6863\u548c\u4ee3\u7801\u6846\u67b6\u8be6\u89c1\u8bfe\u7a0b\u7f51\u7ad9\u3002\u5176\u4e2d\u4e24\u4e2a Project \u975e\u5e38\u6709\u8da3\uff0c\u5206\u522b\u662f\uff1a \u7528 Rust \u5b9e\u73b0\u4e00\u4e2a\u7c7b\u4f3c\u4e8e GDB \u7684 debugger \u7528 Rust 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\u8fd9\u95e8\u795e\u5947\u7684\u8bed\u8a00\u3002 \u5982\u679c\u4f60\u5b66\u8fc7 C \u5e76\u63a5\u89e6\u8fc7\u4e00\u4e9b\u7cfb\u7edf\u7f16\u7a0b\u7684\u8bdd\uff0c\u5e94\u8be5\u5bf9 C \u7684\u5185\u5b58\u6cc4\u6f0f\u4ee5\u53ca\u6307\u9488\u7684\u5371\u9669\u6709\u6240\u8033\u95fb\uff0c\u4f46 C \u7684\u5e95\u5c42\u7279\u6027\u4ee5\u53ca\u9ad8\u6548\u4ecd\u7136\u8ba9\u5b83\u5728\u7cfb\u7edf\u7ea7\u7f16\u7a0b\u4e2d\u65e0\u6cd5\u88ab\u4f8b\u5982 Java \u7b49\u81ea\u5e26\u5783\u573e\u6536\u96c6\u673a\u5236\u7684\u9ad8\u7ea7\u8bed\u8a00\u6240\u66ff\u4ee3\u3002\u800c Rust \u7684\u76ee\u6807\u5219\u662f\u5e0c\u671b\u5728 C \u7684\u9ad8\u6548\u57fa\u7840\u4e0a\uff0c\u5f25\u8865\u5176\u5b89\u5168\u4e0d\u8db3\u7684\u7f3a\u70b9\u3002\u56e0\u6b64 Rust \u5728\u8bbe\u8ba1\u4e4b\u521d\uff0c\u5c31\u6709\u5e26\u6709\u5f88\u591a\u7cfb\u7edf\u7f16\u7a0b\u7684\u89c2\u70b9\u3002\u5b66\u4e60 Rust\uff0c\u4e5f\u80fd\u8ba9\u4f60\u4e4b\u540e\u80fd\u7528 C 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\u7f16\u7a0b\u8bed\u8a00\uff1aC++ \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a60 \u5c0f\u65f6 \u5b98\u65b9\u4ecb\u7ecd: \u672c\u8bfe\u7a0b\u5c06\u5168\u9762\u5730\u4ecb\u7ecd\u73b0\u4ee3\u5b9e\u65f6\u6e32\u67d3\u4e2d\u7684\u5173\u952e\u95ee\u9898\u4e0e\u89e3\u51b3\u65b9\u6cd5\u3002\u7531\u4e8e\u5b9e\u65f6\u6e32\u67d3 (>30 FPS) \u5bf9\u901f\u5ea6\u8981\u6c42\u6781\u9ad8\uff0c\u56e0\u6b64\u672c\u8bfe\u7a0b\u7684\u5173\u6ce8\u70b9\u5c06\u662f\u5728\u82db\u523b\u7684\u65f6\u95f4\u9650\u5236\u4e0b\uff0c\u4eba\u4eec\u5982\u4f55\u6253\u7834\u901f\u5ea6\u4e0e\u8d28\u91cf\u4e4b\u95f4\u7684\u6743\u8861\uff0c\u540c\u65f6\u4fdd\u8bc1\u5b9e\u65f6\u7684\u9ad8\u901f\u5ea6\u4e0e\u7167\u7247\u7ea7\u7684\u771f\u5b9e\u611f\u3002 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Approach"},{"location":"%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/topdown_ustc/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1a\u4e2d\u56fd\u79d1\u5b66\u6280\u672f\u5927\u5b66 \u6388\u8bfe\u6559\u5e08\uff1a\u90d1\u70c7\u3001\u6768\u575a \u5148\u4fee\u8981\u6c42\uff1a\u64cd\u4f5c\u7cfb\u7edf\uff08\u975e\u5fc5\u9700\uff09 \u7f16\u7a0b\u8bed\u8a00\uff1a\u65e0 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a40 \u5c0f\u65f6 \u8fd9\u95e8\u8bfe\u5e94\u8be5\u662f\u4e2d\u6587\u4e92\u8054\u7f51\u4e0a\u6bd4\u8f83\u706b\u7684\u8ba1\u7b97\u673a\u7f51\u7edc\u8bfe\u4e86\uff0c\u6559\u6750\u91c7\u7528\u795e\u4e66\u8ba1\u7b97\u673a\u7f51\u7edc\uff08\u81ea\u9876\u5411\u4e0b\u65b9\u6cd5\uff09\uff0c\u6388\u8bfe\u98ce\u683c\u66f4\u504f\u5411\u5b9e\u9645\u800c\u975e\u7eaf\u7406\u8bba\uff08 \u5f3a\u70c8\u5efa\u8bae \u5148\u9605\u8bfb\u6559\u6750\u9884\u4e60\u518d\u770b\u8bfe\uff0c\u5426\u5219\u4e0a\u8bfe\u65f6 \u53ef\u80fd 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\u7b2c7\u7248\uff09\uff0c\u673a\u68b0\u5de5\u4e1a\u51fa\u7248\u793e\uff0c2016","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E8%BD%AF%E4%BB%B6%E5%B7%A5%E7%A8%8B/6031/","text":"MIT 6.031: Software Construction \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u638c\u63e1\u81f3\u5c11\u4e00\u95e8\u7f16\u7a0b\u8bed\u8a00 \u7f16\u7a0b\u8bed\u8a00\uff1aJava \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 \u8fd9\u95e8\u8bfe\u7684\u76ee\u6807\u5c31\u662f\u8ba9\u5b66\u751f\u5b66\u4f1a\u5982\u4f55\u5199\u51fa\u9ad8\u8d28\u91cf\u7684\u4ee3\u7801\uff0c\u6240\u8c13\u9ad8\u8d28\u91cf\uff0c\u5219\u662f\u6ee1\u8db3\u4e0b\u9762\u4e09\u4e2a\u76ee\u6807\uff08\u8bfe\u7a0b\u8bbe\u8ba1\u8005\u539f\u8bdd\u590d\u5236\uff0c\u4ee5\u9632\u81ea\u5df1\u7ffb\u8bd1\u66f2\u89e3\u672c\u610f\uff09\uff1a Safe from bugs. Correctness (correct behavior right now) and defensiveness (correct behavior in the future) are required in any software we build. Easy to understand. The code has to communicate to future programmers who need to understand it and make changes in it (fixing bugs or adding new features). That future programmer might be you, months or years from now. You\u2019ll be surprised how much you forget if you don\u2019t write it down, and how much it helps your own future self to have a good design. Ready for change. Software always changes. Some designs make it easy to make changes; others require throwing away and rewriting a lot of code. \u4e3a\u6b64\uff0c\u8fd9\u95e8\u8bfe\u7684\u8bbe\u8ba1\u8005\u4eec\u7cbe\u5fc3\u7f16\u5199\u4e86\u4e00\u672c\u4e66\u6765\u9610\u91ca\u8bf8\u591a\u8f6f\u4ef6\u6784\u5efa\u7684\u6838\u5fc3\u539f\u5219\u4e0e\u524d\u4eba\u603b\u7ed3\u4e0b\u6765\u7684\u5b9d\u8d35\u7ecf\u9a8c\uff0c\u5185\u5bb9\u7ec6\u8282\u5230\u5982\u4f55\u7f16\u5199\u6ce8\u91ca\u548c\u51fd\u6570 Specification\uff0c\u5982\u4f55\u8bbe\u8ba1\u62bd\u8c61\u6570\u636e\u7ed3\u6784\u4ee5\u53ca\u8bf8\u591a\u5e76\u884c\u7f16\u7a0b\u7684\u5185\u5bb9\uff0c\u5e76\u4e14\u4f1a\u8ba9\u4f60\u5728\u7cbe\u5fc3\u8bbe\u8ba1\u7684 Java \u7f16\u7a0b\u9879\u76ee\u91cc\u4f53\u9a8c\u548c\u7ec3\u4e60\u8fd9\u4e9b\u7f16\u7a0b\u6a21\u5f0f\u3002 2016\u5e74\u6625\u5b63\u5b66\u671f\u8fd9\u95e8\u8bfe\u5f00\u6e90\u4e86\u5176\u6240\u6709\u7f16\u7a0b\u4f5c\u4e1a\u7684\u4ee3\u7801\u6846\u67b6\uff0c\u800c\u6700\u65b0\u7684\u8bfe\u7a0b\u6559\u6750\u53ef\u4ee5\u5728\u5176\u6700\u65b0\u7684\u6559\u5b66\u7f51\u7ad9\u4e0a\u627e\u5230\uff0c\u5177\u4f53\u94fe\u63a5\u53c2\u89c1\u4e0b\u65b9\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a 2021spring , 2016spring \u8bfe\u7a0b\u89c6\u9891\uff1a\u65e0 \u8bfe\u7a0b\u6559\u6750\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9\u7684\u8bfe\u7a0b notes \u8bfe\u7a0b\u4f5c\u4e1a\uff1a4 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a + 1 \u4e2a Project \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/MIT6.031-software-construction - GitHub \u4e2d\u3002 @pengzhangzhi \u5b8c\u6210\u4e86\u8fd9\u95e8\u8bfe\u7684\u4f5c\u4e1a\u5e76\u8bb0\u5f55\u4e86\u7b14\u8bb0, \u4ee3\u7801\u5f00\u6e90\u5728 pengzhangzhi/self-taught-CS/Software Construction - Github \u3002","title":"MIT 6.031: Software Construction"},{"location":"%E8%BD%AF%E4%BB%B6%E5%B7%A5%E7%A8%8B/6031/#mit-6031-software-construction","text":"","title":"MIT 6.031: Software Construction"},{"location":"%E8%BD%AF%E4%BB%B6%E5%B7%A5%E7%A8%8B/6031/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u638c\u63e1\u81f3\u5c11\u4e00\u95e8\u7f16\u7a0b\u8bed\u8a00 \u7f16\u7a0b\u8bed\u8a00\uff1aJava \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 \u8fd9\u95e8\u8bfe\u7684\u76ee\u6807\u5c31\u662f\u8ba9\u5b66\u751f\u5b66\u4f1a\u5982\u4f55\u5199\u51fa\u9ad8\u8d28\u91cf\u7684\u4ee3\u7801\uff0c\u6240\u8c13\u9ad8\u8d28\u91cf\uff0c\u5219\u662f\u6ee1\u8db3\u4e0b\u9762\u4e09\u4e2a\u76ee\u6807\uff08\u8bfe\u7a0b\u8bbe\u8ba1\u8005\u539f\u8bdd\u590d\u5236\uff0c\u4ee5\u9632\u81ea\u5df1\u7ffb\u8bd1\u66f2\u89e3\u672c\u610f\uff09\uff1a Safe from bugs. Correctness (correct behavior right now) and defensiveness (correct behavior in the future) are required in any software we build. Easy to understand. The code has to communicate to future programmers who need to understand it and make changes in it (fixing bugs or adding new features). That future programmer might be you, months or years from now. You\u2019ll be surprised how much you forget if you don\u2019t write it down, and how much it helps your own future self to have a good design. Ready for change. Software always changes. Some designs make it easy to make changes; others require throwing away and rewriting a lot of code. \u4e3a\u6b64\uff0c\u8fd9\u95e8\u8bfe\u7684\u8bbe\u8ba1\u8005\u4eec\u7cbe\u5fc3\u7f16\u5199\u4e86\u4e00\u672c\u4e66\u6765\u9610\u91ca\u8bf8\u591a\u8f6f\u4ef6\u6784\u5efa\u7684\u6838\u5fc3\u539f\u5219\u4e0e\u524d\u4eba\u603b\u7ed3\u4e0b\u6765\u7684\u5b9d\u8d35\u7ecf\u9a8c\uff0c\u5185\u5bb9\u7ec6\u8282\u5230\u5982\u4f55\u7f16\u5199\u6ce8\u91ca\u548c\u51fd\u6570 Specification\uff0c\u5982\u4f55\u8bbe\u8ba1\u62bd\u8c61\u6570\u636e\u7ed3\u6784\u4ee5\u53ca\u8bf8\u591a\u5e76\u884c\u7f16\u7a0b\u7684\u5185\u5bb9\uff0c\u5e76\u4e14\u4f1a\u8ba9\u4f60\u5728\u7cbe\u5fc3\u8bbe\u8ba1\u7684 Java \u7f16\u7a0b\u9879\u76ee\u91cc\u4f53\u9a8c\u548c\u7ec3\u4e60\u8fd9\u4e9b\u7f16\u7a0b\u6a21\u5f0f\u3002 2016\u5e74\u6625\u5b63\u5b66\u671f\u8fd9\u95e8\u8bfe\u5f00\u6e90\u4e86\u5176\u6240\u6709\u7f16\u7a0b\u4f5c\u4e1a\u7684\u4ee3\u7801\u6846\u67b6\uff0c\u800c\u6700\u65b0\u7684\u8bfe\u7a0b\u6559\u6750\u53ef\u4ee5\u5728\u5176\u6700\u65b0\u7684\u6559\u5b66\u7f51\u7ad9\u4e0a\u627e\u5230\uff0c\u5177\u4f53\u94fe\u63a5\u53c2\u89c1\u4e0b\u65b9\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E8%BD%AF%E4%BB%B6%E5%B7%A5%E7%A8%8B/6031/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a 2021spring , 2016spring \u8bfe\u7a0b\u89c6\u9891\uff1a\u65e0 \u8bfe\u7a0b\u6559\u6750\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9\u7684\u8bfe\u7a0b notes \u8bfe\u7a0b\u4f5c\u4e1a\uff1a4 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a + 1 \u4e2a Project","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E8%BD%AF%E4%BB%B6%E5%B7%A5%E7%A8%8B/6031/#_3","text":"@PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/MIT6.031-software-construction - 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GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/","text":"Foreword The English version is still under development, please check this issue if you want to contribute. This is a self-learning guide to computer science, and a memento of my three years of self-learning at university. It is also a gift to the young students at Peking University. It would be a great encouragement and comfort to me if this book could be of even the slightest help to you in your college life. The book is currently organized to include the following sections (if you have other good suggestions, or would like to join the ranks of contributors, please feel free to email zhongyinmin@pku.edu.cn or ask questions in the issue). Productivity Toolkit: IDE, VPN, StackOverflow, Git, Github, Vim, Latex, GNU Make and so on. Environment configuration: PC/Server development environment setup, DevOps tutorials and so on. Book recommendations: Those who have read the CSAPP must have realized the importance of good books. I will list links to books and resources in different areas of Computer Science that I find rewarding to read. List of high quality CS courses : I will summarize all the high quality foreign CS courses I have taken into different categories and give relevant self-learning advice. Most of them will have a separate repository containing relevant resources as well as my homework/project implementations. The place where dreams start \u2014\u2014 CS61A In my freshman year, I was a novice who knew nothing about computers. I installed a giant IDE Visual Studio and fight with OJ every day. With my high school maths background, I did pretty well in maths courses, but I felt struggled to learn courses in my major. When it came to programming, all I could do was open up that clunky IDE, create a new project that I didn't know exactly what it was for, and then cin , cout , for loops, and then CE, RE, WA loops. I was in a state where I was desperately trying to learn well but I didn't know how to learn. I listened carefully in class but I couldn't solve the homework problems. I spent almost all my spare time doing the homework after class, but the results were disappointing. I still retain the source code of the project for Introduction to Computing course \u2014\u2014 a single 1200-line C++ file with no header files, no class abstraction, no unit tests, no makefile, no version control. The only good thing is that it can run, the disadvantage is the complement of \"can run\". For a while I wondered if I wasn't cut out for computer science, as all my childhood imaginings of geekiness had been completely ruined by my first semester's experience. It all turned around during the winter break of my freshman year, when I had a hankering to learn Python. I overheard someone recommend CS61A, a freshman introductory course at UC Berkeley on Python. I'll never forget that day, when I opened the CS61A course website. It was like Columbus discovering a new continent, and I opened the door to a new world. I finished the course in 3 weeks and for the first time I felt that CS could be so fulfilling and interesting, and I was shocked that there existed such a great course in the world. To avoid any suspicion of pandering to foreign courses, I will tell you about my experience of studying CS61A from the perspective of a pure student. Course website developed by course staffs : The course website integrates all the course resources into one, with a well organised course schedule, links to all slides, recorded videos and homework, detailed and clear syllabus, list of exams and solutions from previous years. Aesthetics aside, this website is so convenient for students. Textbook written by course instructor : The course instructor has adapted the classic MIT textbook Structure and Interpretation of Computer Programs (SICP) into Python (the original textbook was based on Scheme). This is a great way to ensure that the classroom content is consistent with the textbook, while adding more details. The entire book is open source and can be read directly online. Various, comprehensive and interesting homework : There are 14 labs to reinforce the knowledge gained in class, 10 homework assignments to practice, and 4 projects each with thousands of lines of code, all with well-organized skeleton code and babysitting instructions. Unlike the old-school OJ and Word document assignments, each lab/homework/project has a detailed handout document, fully automated grading scripts, and CS61A staffs have even developed an automated assignment submission and grading system . Of course, one might say \"How much can you learn from a project where most of code are written by your teaching assistants?\" . For someone who is new to CS and even stumbling over installing Python, this well-developed skeleton code allows students to focus on reinforcing the core knowledge they've learned in class, but also gives them a sense of achievement that they already can make a little game despite of learning Python only for a month. It also gives them the opportunity to read and learn from other people's high quality code so that they can reuse it later. I think in the freshman year, this kind of skeleton code is absolutely beneficial. The only bad thing perhaps is for the instructors and teaching assistants, as developing such assignments can conceivably require a considerable time commitment. Weekly discussion sessions : The teaching assistants will explain the difficult knowledge in class and add some supplementary materials which may not be covered in class. Also, there will be exercises from exams of previous years. All the exercises are written in LaTeX with solutions. In CS61A, You don't need any prerequesites about CS at all. You just need to pay attention, spend time and work hard. The feeling that you do not know what to do, that you are not getting anything in return for all the time you put in, is gone. It suited me so well that I fell in love with self-learning. Imagine that if someone could chew up the hard knowledge and present it to you in a vivid and straightforward way, with so many fancy and varied projects to reinforce your theoretical knowledge, you'd think they were really trying their best to make you fully grasp the course, and it was even an insult to the course builders not to learn it well. If you think I'm exaggerating, start with CS61A , because it's where my dreams began. Why write this book? In the 2020 Fall semester, I worked as a teaching assistant for the class Introduction to Computer Systems at Peking University. At that time, I had been studying totally on my own for over a year. I enjoyed this style of learning immensely. To share this joy, I have made a CS Self-learning Materials List for students in my seminar. It was purely on a whim at the time, as I wouldn't dare to encourage my students to skip classes and study on their own. But after another year of maintenance, the list has become quite comprehensive, covering most of the courses in Computer Science, Artificial Intelligence and Soft Engineering, and I have built separate repositories for each course, summarising the self-learning materials that I used. In my last college year, when I opened up my curriculum book, I realized that it was already a subset of my self-learning list. By then, it was only two and a half years after I had started my self-learning journey. Then, a bold idea came to my mind: perhaps I could create a self-learning book, write down the difficulty I encountered and the interest I found during these years of self-learning, hoping to make it easy for students who may also enjoy self-learning to start their wonderful self-learning journey. If you can build up the whole CS foundation in less than three years, have relatively solid mathematical skills and coding ability, experience dozens of projects with thousands of lines of code, master at least C/C++/Java/JS/Python/Go/Rust and other mainstream programming languages, have a good understanding of algorithms, circuits, architectures, networks, operating systems, compilers, artificial intelligence, machine learning, computer vision, natural language processing, reinforcement learning, cryptography, information theory, game theory, numerical analysis, statistics, distributed systems, parallel computing, database systems, computer graphics, web development, cloud computing, supercomputing etc. I think you will be confident enough to choose the area you are interested in, and you will be quite competitive in both industry and academia. I firmly believe that if you have read to this line, you do not lack the ability and committment to learn CS well, you just need a good teacher to teach you a good course. And I will try my best to pick such courses for you, based on my three years of experience. Pros For me, the biggest advantage of self-learning is that I can adjust the pace of learning entirely according to my own progress. For difficult parts, I can watch the videos over and over again, Google it online and ask questions on StackOverflow until I have it all figured out. For those that I mastered relatively quickly, I could skip them at twice or even three times the speed. Another great thing about self-learning is that you can learn from different perspectives. I have taken core courses such as architectures, networking, operating systems, and compilers from different universities. Different instructors may have different views on the same knowledge, which will broaden your horizon. A third advantage of self-learning is that you do not need to go to the class, listening to the boring lectures. Cons Of course, as a big fan of self-learning, I have to admit that it has its disadvantages. The first is the difficulty of communication. I'm actually a very keen questioner, and I like to follow up all the points I don't understand. But when you're facing a screen and you hear a teacher talking about something you don't understand, you can't go to the other end of the network and ask him or her for clarification. I try to mitigate this by thinking independently and making good use of Google, but it would be great to have a few friends to study together. You can refer to README for more information on participating a community group. The second thing is that these courses are basically in English. From the videos to the slides to the assignments, all in English. You may struggle at first, but I think it's a challenge that if you overcome, it will be extremely rewarding. Because at the moment, as reluctant as I am, I have to admit that in computer science, a lot of high quality documentation, forums and websites are all in English. The third, and I think the most difficult one, is self-discipline. Because have no DDL can sometimes be a really scary thing, especially when you get deeper, many foreign courses are quite difficult. You have to be self-driven enough to force yourself to settle down, read dozens of pages of Project Handout, understand thousands of lines of skeleton code and endure hours of debugging time. With no credits, no grades, no teachers, no classmates, just one belief - that you are getting better. Who is this book for? As I said in the beginning, anyone who is interested in learning computer science on their own can refer to this book. If you already have some basic skills and are just interested in a particular area, you can selectively pick and choose what you are interested in to study. Of course, if you are a novice who knows nothing about computers like I did back then, and just begin your college journey, I hope this book will be your cheat sheet to get the knowledge and skills you need in the least amount of time. In a way, this book is more like a course search engine ordered according to my experience, helping you to learn high quality CS courses from the world's top universities without leaving home. Of course, as an undergraduate student who has not yet graduated, I feel that I am not in a position nor have the right to preach one way of learning. I just hope that this material will help those who are also self-motivated and persistent to gain a richer, more varied and satisfying college life. Special thanks I would like to express my sincere gratitude to all the professors who have made their courses public for free. These courses are the culmination of decades of their teaching careers, and they have chosen to selflessly make such a high quality CS education available to all. Without them, my university life would not have been as fulfilling and enjoyable. Many of the professors would even reply with hundreds of words in length after I had sent them a thank you email, which really touched me beyond words. They also inspired me all the time that if decide to do something, do it with all heart and soul. Want to join as a contributor? There is a limit to how much one person can do, and this book was written by me under a heavy research schedule, so there are inevitably imperfections. In addition, as I work in the area of systems, many of the courses focus on systems, and there is relatively little content related to advanced mathematics, computing theory, and advanced algorithms. If any of you would like to share your self-learning experience and resources in other areas, you can directly initiate a Pull Request in the project, or feel free to contact me by email ( zhongyinmin@pku.edu.cn ).","title":"Foreword"},{"location":"en/#foreword","text":"The English version is still under development, please check this issue if you want to contribute. This is a self-learning guide to computer science, and a memento of my three years of self-learning at university. It is also a gift to the young students at Peking University. It would be a great encouragement and comfort to me if this book could be of even the slightest help to you in your college life. The book is currently organized to include the following sections (if you have other good suggestions, or would like to join the ranks of contributors, please feel free to email zhongyinmin@pku.edu.cn or ask questions in the issue). Productivity Toolkit: IDE, VPN, StackOverflow, Git, Github, Vim, Latex, GNU Make and so on. Environment configuration: PC/Server development environment setup, DevOps tutorials and so on. Book recommendations: Those who have read the CSAPP must have realized the importance of good books. I will list links to books and resources in different areas of Computer Science that I find rewarding to read. List of high quality CS courses : I will summarize all the high quality foreign CS courses I have taken into different categories and give relevant self-learning advice. Most of them will have a separate repository containing relevant resources as well as my homework/project implementations.","title":"Foreword"},{"location":"en/#the-place-where-dreams-start-cs61a","text":"In my freshman year, I was a novice who knew nothing about computers. I installed a giant IDE Visual Studio and fight with OJ every day. With my high school maths background, I did pretty well in maths courses, but I felt struggled to learn courses in my major. When it came to programming, all I could do was open up that clunky IDE, create a new project that I didn't know exactly what it was for, and then cin , cout , for loops, and then CE, RE, WA loops. I was in a state where I was desperately trying to learn well but I didn't know how to learn. I listened carefully in class but I couldn't solve the homework problems. I spent almost all my spare time doing the homework after class, but the results were disappointing. I still retain the source code of the project for Introduction to Computing course \u2014\u2014 a single 1200-line C++ file with no header files, no class abstraction, no unit tests, no makefile, no version control. The only good thing is that it can run, the disadvantage is the complement of \"can run\". For a while I wondered if I wasn't cut out for computer science, as all my childhood imaginings of geekiness had been completely ruined by my first semester's experience. It all turned around during the winter break of my freshman year, when I had a hankering to learn Python. I overheard someone recommend CS61A, a freshman introductory course at UC Berkeley on Python. I'll never forget that day, when I opened the CS61A course website. It was like Columbus discovering a new continent, and I opened the door to a new world. I finished the course in 3 weeks and for the first time I felt that CS could be so fulfilling and interesting, and I was shocked that there existed such a great course in the world. To avoid any suspicion of pandering to foreign courses, I will tell you about my experience of studying CS61A from the perspective of a pure student. Course website developed by course staffs : The course website integrates all the course resources into one, with a well organised course schedule, links to all slides, recorded videos and homework, detailed and clear syllabus, list of exams and solutions from previous years. Aesthetics aside, this website is so convenient for students. Textbook written by course instructor : The course instructor has adapted the classic MIT textbook Structure and Interpretation of Computer Programs (SICP) into Python (the original textbook was based on Scheme). This is a great way to ensure that the classroom content is consistent with the textbook, while adding more details. The entire book is open source and can be read directly online. Various, comprehensive and interesting homework : There are 14 labs to reinforce the knowledge gained in class, 10 homework assignments to practice, and 4 projects each with thousands of lines of code, all with well-organized skeleton code and babysitting instructions. Unlike the old-school OJ and Word document assignments, each lab/homework/project has a detailed handout document, fully automated grading scripts, and CS61A staffs have even developed an automated assignment submission and grading system . Of course, one might say \"How much can you learn from a project where most of code are written by your teaching assistants?\" . For someone who is new to CS and even stumbling over installing Python, this well-developed skeleton code allows students to focus on reinforcing the core knowledge they've learned in class, but also gives them a sense of achievement that they already can make a little game despite of learning Python only for a month. It also gives them the opportunity to read and learn from other people's high quality code so that they can reuse it later. I think in the freshman year, this kind of skeleton code is absolutely beneficial. The only bad thing perhaps is for the instructors and teaching assistants, as developing such assignments can conceivably require a considerable time commitment. Weekly discussion sessions : The teaching assistants will explain the difficult knowledge in class and add some supplementary materials which may not be covered in class. Also, there will be exercises from exams of previous years. All the exercises are written in LaTeX with solutions. In CS61A, You don't need any prerequesites about CS at all. You just need to pay attention, spend time and work hard. The feeling that you do not know what to do, that you are not getting anything in return for all the time you put in, is gone. It suited me so well that I fell in love with self-learning. Imagine that if someone could chew up the hard knowledge and present it to you in a vivid and straightforward way, with so many fancy and varied projects to reinforce your theoretical knowledge, you'd think they were really trying their best to make you fully grasp the course, and it was even an insult to the course builders not to learn it well. If you think I'm exaggerating, start with CS61A , because it's where my dreams began.","title":"The place where dreams start \u2014\u2014 CS61A"},{"location":"en/#why-write-this-book","text":"In the 2020 Fall semester, I worked as a teaching assistant for the class Introduction to Computer Systems at Peking University. At that time, I had been studying totally on my own for over a year. I enjoyed this style of learning immensely. To share this joy, I have made a CS Self-learning Materials List for students in my seminar. It was purely on a whim at the time, as I wouldn't dare to encourage my students to skip classes and study on their own. But after another year of maintenance, the list has become quite comprehensive, covering most of the courses in Computer Science, Artificial Intelligence and Soft Engineering, and I have built separate repositories for each course, summarising the self-learning materials that I used. In my last college year, when I opened up my curriculum book, I realized that it was already a subset of my self-learning list. By then, it was only two and a half years after I had started my self-learning journey. Then, a bold idea came to my mind: perhaps I could create a self-learning book, write down the difficulty I encountered and the interest I found during these years of self-learning, hoping to make it easy for students who may also enjoy self-learning to start their wonderful self-learning journey. If you can build up the whole CS foundation in less than three years, have relatively solid mathematical skills and coding ability, experience dozens of projects with thousands of lines of code, master at least C/C++/Java/JS/Python/Go/Rust and other mainstream programming languages, have a good understanding of algorithms, circuits, architectures, networks, operating systems, compilers, artificial intelligence, machine learning, computer vision, natural language processing, reinforcement learning, cryptography, information theory, game theory, numerical analysis, statistics, distributed systems, parallel computing, database systems, computer graphics, web development, cloud computing, supercomputing etc. I think you will be confident enough to choose the area you are interested in, and you will be quite competitive in both industry and academia. I firmly believe that if you have read to this line, you do not lack the ability and committment to learn CS well, you just need a good teacher to teach you a good course. And I will try my best to pick such courses for you, based on my three years of experience.","title":"Why write this book?"},{"location":"en/#pros","text":"For me, the biggest advantage of self-learning is that I can adjust the pace of learning entirely according to my own progress. For difficult parts, I can watch the videos over and over again, Google it online and ask questions on StackOverflow until I have it all figured out. For those that I mastered relatively quickly, I could skip them at twice or even three times the speed. Another great thing about self-learning is that you can learn from different perspectives. I have taken core courses such as architectures, networking, operating systems, and compilers from different universities. Different instructors may have different views on the same knowledge, which will broaden your horizon. A third advantage of self-learning is that you do not need to go to the class, listening to the boring lectures.","title":"Pros"},{"location":"en/#cons","text":"Of course, as a big fan of self-learning, I have to admit that it has its disadvantages. The first is the difficulty of communication. I'm actually a very keen questioner, and I like to follow up all the points I don't understand. But when you're facing a screen and you hear a teacher talking about something you don't understand, you can't go to the other end of the network and ask him or her for clarification. I try to mitigate this by thinking independently and making good use of Google, but it would be great to have a few friends to study together. You can refer to README for more information on participating a community group. The second thing is that these courses are basically in English. From the videos to the slides to the assignments, all in English. You may struggle at first, but I think it's a challenge that if you overcome, it will be extremely rewarding. Because at the moment, as reluctant as I am, I have to admit that in computer science, a lot of high quality documentation, forums and websites are all in English. The third, and I think the most difficult one, is self-discipline. Because have no DDL can sometimes be a really scary thing, especially when you get deeper, many foreign courses are quite difficult. You have to be self-driven enough to force yourself to settle down, read dozens of pages of Project Handout, understand thousands of lines of skeleton code and endure hours of debugging time. With no credits, no grades, no teachers, no classmates, just one belief - that you are getting better.","title":"Cons"},{"location":"en/#who-is-this-book-for","text":"As I said in the beginning, anyone who is interested in learning computer science on their own can refer to this book. If you already have some basic skills and are just interested in a particular area, you can selectively pick and choose what you are interested in to study. Of course, if you are a novice who knows nothing about computers like I did back then, and just begin your college journey, I hope this book will be your cheat sheet to get the knowledge and skills you need in the least amount of time. In a way, this book is more like a course search engine ordered according to my experience, helping you to learn high quality CS courses from the world's top universities without leaving home. Of course, as an undergraduate student who has not yet graduated, I feel that I am not in a position nor have the right to preach one way of learning. I just hope that this material will help those who are also self-motivated and persistent to gain a richer, more varied and satisfying college life.","title":"Who is this book for?"},{"location":"en/#special-thanks","text":"I would like to express my sincere gratitude to all the professors who have made their courses public for free. These courses are the culmination of decades of their teaching careers, and they have chosen to selflessly make such a high quality CS education available to all. Without them, my university life would not have been as fulfilling and enjoyable. Many of the professors would even reply with hundreds of words in length after I had sent them a thank you email, which really touched me beyond words. They also inspired me all the time that if decide to do something, do it with all heart and soul.","title":"Special thanks"},{"location":"en/#want-to-join-as-a-contributor","text":"There is a limit to how much one person can do, and this book was written by me under a heavy research schedule, so there are inevitably imperfections. In addition, as I work in the area of systems, many of the courses focus on systems, and there is relatively little content related to advanced mathematics, computing theory, and advanced algorithms. If any of you would like to share your self-learning experience and resources in other areas, you can directly initiate a Pull Request in the project, or feel free to contact me by email ( zhongyinmin@pku.edu.cn ).","title":"Want to join as a contributor?"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/","text":"\u4e00\u4e2a\u4ec5\u4f9b\u53c2\u8003\u7684 CS \u5b66\u4e60\u89c4\u5212 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Calculus Course \u548c 18.06: Linear Algebra \u7684\u8bfe\u7a0b notes\uff0c\u81f3\u5c11\u4e8e\u6211\u800c\u8a00\uff0c\u5b83\u5e2e\u52a9\u6211\u6df1\u523b\u7406\u89e3\u4e86\u5fae\u79ef\u5206\u548c\u7ebf\u6027\u4ee3\u6570\u7684\u8bb8\u591a\u672c\u8d28\u3002\u987a\u9053\u518d\u5b89\u5229\u4e00\u4e2a\u6cb9\u7ba1\u6570\u5b66\u7f51\u7ea2 3Blue1Brown \uff0c\u4ed6\u7684\u9891\u9053\u6709\u5f88\u591a\u7528\u751f\u52a8\u5f62\u8c61\u7684\u52a8\u753b\u9610\u91ca\u6570\u5b66\u672c\u8d28\u5185\u6838\u7684\u89c6\u9891\uff0c\u517c\u5177\u6df1\u5ea6\u548c\u5e7f\u5ea6\uff0c\u8d28\u91cf\u975e\u5e38\u9ad8\u3002 \u4fe1\u606f\u8bba\u5165\u95e8 \u4f5c\u4e3a\u8ba1\u7b97\u673a\u7cfb\u7684\u5b66\u751f\uff0c\u53ca\u65e9\u4e86\u89e3\u4e00\u4e9b\u4fe1\u606f\u8bba\u7684\u57fa\u7840\u77e5\u8bc6\uff0c\u6211\u89c9\u5f97\u662f\u5927\u6709\u88e8\u76ca\u7684\u3002\u4f46\u5927\u591a\u4fe1\u606f\u8bba\u8bfe\u7a0b\u90fd\u9762\u5411\u9ad8\u5e74\u7ea7\u672c\u79d1\u751f\u751a\u81f3\u7814\u7a76\u751f\uff0c\u5bf9\u65b0\u624b\u6781\u4e0d\u53cb\u597d\u3002\u800c MIT \u7684 6.050J: Information theory and Entropy \u8fd9\u95e8\u8bfe\u6b63\u662f\u4e3a\u5927\u4e00\u65b0\u751f\u91cf\u8eab\u5b9a\u5236\u7684\uff0c\u51e0\u4e4e\u6ca1\u6709\u5148\u4fee\u8981\u6c42\uff0c\u6db5\u76d6\u4e86\u7f16\u7801\u3001\u538b\u7f29\u3001\u901a\u4fe1\u3001\u4fe1\u606f\u71b5\u7b49\u7b49\u5185\u5bb9\uff0c\u975e\u5e38\u6709\u8da3\u3002 \u6570\u5b66\u8fdb\u9636 \u79bb\u6563\u6570\u5b66\u4e0e\u6982\u7387\u8bba \u96c6\u5408\u8bba\u3001\u56fe\u8bba\u3001\u6982\u7387\u8bba\u7b49\u7b49\u662f\u7b97\u6cd5\u63a8\u5bfc\u4e0e\u8bc1\u660e\u7684\u91cd\u8981\u5de5\u5177\uff0c\u4e5f\u662f\u540e\u7eed\u9ad8\u9636\u6570\u5b66\u8bfe\u7a0b\u7684\u57fa\u7840\u3002\u4f46\u6211\u89c9\u5f97\u8fd9\u7c7b\u8bfe\u7a0b\u7684\u8bb2\u6388\u5f88\u5bb9\u6613\u843d\u5165\u7406\u8bba\u5316\u4e0e\u5f62\u5f0f\u5316\u7684\u7aa0\u81fc\uff0c\u8ba9\u8bfe\u5802\u6210\u4e3a\u5b9a\u7406\u7ed3\u8bba\u7684\u5806\u780c\uff0c\u800c\u65e0\u6cd5\u4f7f\u5b66\u751f\u6df1\u523b\u628a\u63e1\u7406\u8bba\u7684\u672c\u8d28\uff0c\u8fdb\u800c\u9020\u6210\u5b66\u4e86\u5c31\u80cc\uff0c\u8003\u4e86\u5c31\u5fd8\u7684\u602a\u5708\u3002\u5982\u679c\u80fd\u5728\u7406\u8bba\u6559\u5b66\u4e2d\u7a7f\u63d2\u7b97\u6cd5\u8fd0\u7528\u5b9e\u4f8b\uff0c\u5b66\u751f\u5728\u62d3\u5c55\u7b97\u6cd5\u77e5\u8bc6\u7684\u540c\u65f6\u4e5f\u80fd\u7aa5\u89c1\u7406\u8bba\u7684\u529b\u91cf\u548c\u9b45\u529b\u3002 UCB CS70 : discrete Math and probability theory \u548c UCB CS126 : Probability theory \u662f UC Berkeley \u7684\u6982\u7387\u8bba\u8bfe\u7a0b\uff0c\u524d\u8005\u8986\u76d6\u4e86\u79bb\u6563\u6570\u5b66\u548c\u6982\u7387\u8bba\u57fa\u7840\uff0c\u540e\u8005\u5219\u6d89\u53ca\u968f\u673a\u8fc7\u7a0b\u4ee5\u53ca\u6df1\u5165\u7684\u7406\u8bba\u5185\u5bb9\u3002\u4e24\u8005\u90fd\u975e\u5e38\u6ce8\u91cd\u7406\u8bba\u548c\u5b9e\u8df5\u7684\u7ed3\u5408\uff0c\u6709\u4e30\u5bcc\u7684\u7b97\u6cd5\u5b9e\u9645\u8fd0\u7528\u5b9e\u4f8b\uff0c\u540e\u8005\u8fd8\u6709\u5927\u91cf\u7684 Python \u7f16\u7a0b\u4f5c\u4e1a\u6765\u8ba9\u5b66\u751f\u8fd0\u7528\u6982\u7387\u8bba\u7684\u77e5\u8bc6\u89e3\u51b3\u5b9e\u9645\u95ee\u9898\u3002 \u6570\u503c\u5206\u6790 \u4f5c\u4e3a\u8ba1\u7b97\u673a\u7cfb\u7684\u5b66\u751f\uff0c\u57f9\u517b\u8ba1\u7b97\u601d\u7ef4\u662f\u5f88\u91cd\u8981\u7684\uff0c\u5b9e\u9645\u95ee\u9898\u7684\u5efa\u6a21\u3001\u79bb\u6563\u5316\uff0c\u8ba1\u7b97\u673a\u7684\u6a21\u62df\u3001\u5206\u6790\uff0c\u662f\u4e00\u9879\u5f88\u91cd\u8981\u7684\u80fd\u529b\u3002\u800c\u8fd9\u4e24\u5e74\u5f00\u59cb\u98ce\u9761\u7684\uff0c\u7531 MIT \u6253\u9020\u7684 Julia \u7f16\u7a0b\u8bed\u8a00\u4ee5\u5176 C \u4e00\u6837\u7684\u901f\u5ea6\u548c Python \u4e00\u6837\u53cb\u597d\u7684\u8bed\u6cd5\u5728\u6570\u503c\u8ba1\u7b97\u9886\u57df\u6709\u4e00\u7edf\u5929\u4e0b\u4e4b\u52bf\uff0cMIT \u7684\u8bb8\u591a\u6570\u5b66\u8bfe\u7a0b\u4e5f\u5f00\u59cb\u7528 Julia \u4f5c\u4e3a\u6559\u5b66\u5de5\u5177\uff0c\u628a\u8270\u6df1\u7684\u6570\u5b66\u7406\u8bba\u7528\u76f4\u89c2\u6e05\u6670\u7684\u4ee3\u7801\u5c55\u793a\u51fa\u6765\u3002 ComputationalThinking \u662f MIT \u5f00\u8bbe\u7684\u4e00\u95e8\u8ba1\u7b97\u601d\u7ef4\u5165\u95e8\u8bfe\uff0c\u6240\u6709\u8bfe\u7a0b\u5185\u5bb9\u5168\u90e8\u5f00\u6e90\uff0c\u53ef\u4ee5\u5728\u8bfe\u7a0b\u7f51\u7ad9\u76f4\u63a5\u8bbf\u95ee\u3002\u8fd9\u95e8\u8bfe\u5229\u7528 Julia \u7f16\u7a0b\u8bed\u8a00\uff0c\u5728\u56fe\u50cf\u5904\u7406\u3001\u793e\u4f1a\u79d1\u5b66\u4e0e\u6570\u636e\u79d1\u5b66\u3001\u6c14\u5019\u5b66\u5efa\u6a21\u4e09\u4e2a topic \u4e0b\u5e26\u9886\u5b66\u751f\u7406\u89e3\u7b97\u6cd5\u3001\u6570\u5b66\u5efa\u6a21\u3001\u6570\u636e\u5206\u6790\u3001\u4ea4\u4e92\u8bbe\u8ba1\u3001\u56fe\u4f8b\u5c55\u793a\uff0c\u8ba9\u5b66\u751f\u4f53\u9a8c\u8ba1\u7b97\u4e0e\u79d1\u5b66\u7684\u7f8e\u5999\u7ed3\u5408\u3002\u5185\u5bb9\u867d\u7136\u4e0d\u96be\uff0c\u4f46\u7ed9\u6211\u6700\u6df1\u523b\u7684\u611f\u53d7\u5c31\u662f\uff0c\u79d1\u5b66\u7684\u9b45\u529b\u5e76\u4e0d\u662f\u6545\u5f04\u7384\u865a\u7684\u8270\u6df1\u7406\u8bba\uff0c\u4e0d\u662f\u8bd8\u5c48\u8071\u7259\u7684\u672f\u8bed\u884c\u8bdd\uff0c\u800c\u662f\u7528\u76f4\u89c2\u751f\u52a8\u7684\u6848\u4f8b\uff0c\u7528\u7b80\u7ec3\u6df1\u523b\u7684\u8bed\u8a00\uff0c\u8ba9\u6bcf\u4e2a\u666e\u901a\u4eba\u90fd\u80fd\u7406\u89e3\u3002 \u4e0a\u5b8c\u4e0a\u9762\u7684\u4f53\u9a8c\u8bfe\u4e4b\u540e\uff0c\u5982\u679c\u610f\u72b9\u672a\u5c3d\u7684\u8bdd\uff0c\u4e0d\u59a8\u8bd5\u8bd5 MIT \u7684 18.330 : Introduction to numerical analysis \uff0c\u8fd9\u95e8\u8bfe\u7684\u7f16\u7a0b\u4f5c\u4e1a\u540c\u6837\u4f1a\u7528 Julia \u7f16\u7a0b\u8bed\u8a00\uff0c\u4e0d\u8fc7\u96be\u5ea6\u548c\u6df1\u5ea6\u4e0a\u90fd\u4e0a\u4e86\u4e00\u4e2a\u53f0\u9636\u3002\u5185\u5bb9\u6d89\u53ca\u4e86\u6d6e\u70b9\u7f16\u7801\u3001Root finding\u3001\u7ebf\u6027\u7cfb\u7edf\u3001\u5fae\u5206\u65b9\u7a0b\u7b49\u7b49\u65b9\u9762\uff0c\u6574\u95e8\u8bfe\u7684\u4e3b\u65e8\u5c31\u662f\u8ba9\u4f60\u5229\u7528\u79bb\u6563\u5316\u7684\u8ba1\u7b97\u673a\u8868\u793a\u53bb\u4f30\u8ba1\u548c\u903c\u8fd1\u4e00\u4e2a\u6570\u5b66\u4e0a\u8fde\u7eed\u7684\u6982\u5ff5\u3002\u8fd9\u95e8\u8bfe\u7684\u6559\u6388\u8fd8\u4e13\u95e8\u64b0\u5199\u4e86\u4e00\u672c\u914d\u5957\u7684\u5f00\u6e90\u6559\u6750 Fundamentals of Numerical Computation \uff0c\u91cc\u9762\u9644\u6709\u4e30\u5bcc\u7684 Julia \u4ee3\u7801\u5b9e\u4f8b\u548c\u4e25\u8c28\u7684\u516c\u5f0f\u63a8\u5bfc\u3002 \u5982\u679c\u4f60\u8fd8\u610f\u72b9\u672a\u5c3d\u7684\u8bdd\uff0c\u8fd8\u6709 MIT \u7684\u6570\u503c\u5206\u6790\u7814\u7a76\u751f\u8bfe\u7a0b 18.335: Introduction to numerical method \u4f9b\u4f60\u53c2\u8003\u3002 \u5fae\u5206\u65b9\u7a0b \u5982\u679c\u4e16\u95f4\u4e07\u7269\u7684\u8fd0\u52a8\u53d1\u5c55\u90fd\u80fd\u7528\u65b9\u7a0b\u6765\u523b\u753b\u548c\u63cf\u8ff0\uff0c\u8fd9\u662f\u4e00\u4ef6\u591a\u4e48\u9177\u7684\u4e8b\u60c5\u5440\uff01\u867d\u7136\u51e0\u4e4e\u4efb\u4f55\u4e00\u6240\u5b66\u6821\u7684 CS \u57f9\u517b\u65b9\u6848\u4e2d\u90fd\u6ca1\u6709\u5fae\u5206\u65b9\u7a0b\u76f8\u5173\u7684\u5fc5\u4fee\u8bfe\u7a0b\uff0c\u4f46\u6211\u8fd8\u662f\u89c9\u5f97\u638c\u63e1\u5b83\u4f1a\u8d4b\u4e88\u4f60\u4e00\u4e2a\u65b0\u7684\u89c6\u89d2\u6765\u5ba1\u89c6\u8fd9\u4e2a\u4e16\u754c\u3002 \u7531\u4e8e\u5fae\u5206\u65b9\u7a0b\u4e2d\u5f80\u5f80\u4f1a\u7528\u5230\u5f88\u591a\u590d\u53d8\u51fd\u6570\u7684\u77e5\u8bc6\uff0c\u6240\u4ee5\u5927\u5bb6\u53ef\u4ee5\u53c2\u8003 MIT18.04: Complex variables functions \u7684\u8bfe\u7a0b notes \u6765\u8865\u9f50\u5148\u4fee\u77e5\u8bc6\u3002 MIT18.03: differential equations ) \u4e3b\u8981\u8986\u76d6\u4e86\u5e38\u5fae\u5206\u65b9\u7a0b\u7684\u6c42\u89e3\uff0c\u5728\u6b64\u57fa\u7840\u4e4b\u4e0a MIT18.152: Partial differential equations ) \u5219\u4f1a\u6df1\u5165\u504f\u5fae\u5206\u65b9\u7a0b\u7684\u5efa\u6a21\u4e0e\u6c42\u89e3\u3002\u638c\u63e1\u4e86\u5fae\u5206\u65b9\u7a0b\u8fd9\u4e00\u6709\u5229\u5de5\u5177\uff0c\u76f8\u4fe1\u5bf9\u4e8e\u4f60\u7684\u5b9e\u9645\u95ee\u9898\u7684\u5efa\u6a21\u80fd\u529b\u4ee5\u53ca\u4ece\u4f17\u591a\u566a\u58f0\u53d8\u91cf\u4e2d\u628a\u63e1\u672c\u8d28\u7684\u76f4\u89c9\u90fd\u4f1a\u6709\u5f88\u5927\u5e2e\u52a9\u3002 \u6570\u5b66\u9ad8\u9636 \u4f5c\u4e3a\u8ba1\u7b97\u673a\u7cfb\u7684\u5b66\u751f\uff0c\u6211\u7ecf\u5e38\u542c\u5230\u6570\u5b66\u65e0\u7528\u8bba\u7684\u8bba\u65ad\uff0c\u5bf9\u6b64\u6211\u4e0d\u6562\u82df\u540c\u4f46\u4e5f\u65e0\u6743\u53cd\u5bf9\uff0c\u4f46\u82e5\u51e1\u4e8b\u90fd\u786c\u8981\u4e89\u51fa\u4e2a\u6709\u7528\u548c\u65e0\u7528\u7684\u533a\u522b\u6765\uff0c\u5012\u4e5f\u7740\u5b9e\u65e0\u8da3\uff0c\u56e0\u6b64\u4e0b\u9762\u8fd9\u4e9b\u9762\u5411\u9ad8\u5e74\u7ea7\u751a\u81f3\u7814\u7a76\u751f\u7684\u6570\u5b66\u8bfe\u7a0b\uff0c\u5927\u5bb6\u6309\u5174\u8da3\u81ea\u53d6\u6240\u9700\u3002 \u51f8\u4f18\u5316 Standford EE364A: Convex Optimization \u4fe1\u606f\u8bba MIT6.441: Information Theory \u5e94\u7528\u7edf\u8ba1\u5b66 MIT18.650: Statistics for Applications \u521d\u7b49\u6570\u8bba MIT18.781: Theory of Numbers \u5bc6\u7801\u5b66 Standford CS255: Cryptography \u7f16\u7a0b\u5165\u95e8 Languages are tools, you choose the right tool to do the right thing. Since there's no universally perfect tool, there's no universally perfect language. Shell MIT-Missing-Semester Python Harvard CS50: This is CS50x UCB CS61A: Structure and Interpretation of Computer Programs C++ Stanford CS106B/X: Programming Abstractions Stanford CS106L: Standard C++ Programming Rust Stanford CS110L: Safety in Systems Programming OCaml Cornell CS3110 textbook: Functional Programming in OCaml \u7535\u5b50\u57fa\u7840 \u7535\u8def\u57fa\u7840 \u4f5c\u4e3a\u8ba1\u7b97\u673a\u7cfb\u7684\u5b66\u751f\uff0c\u4e86\u89e3\u4e00\u4e9b\u57fa\u7840\u7684\u7535\u8def\u77e5\u8bc6\uff0c\u611f\u53d7\u4ece\u4f20\u611f\u5668\u6536\u96c6\u6570\u636e\u5230\u6570\u636e\u5206\u6790\u518d\u5230\u7b97\u6cd5\u9884\u6d4b\u6574\u6761\u6d41\u6c34\u7ebf\uff0c\u5bf9\u4e8e\u540e\u7eed\u77e5\u8bc6\u7684\u5b66\u4e60\u4ee5\u53ca\u8ba1\u7b97\u601d\u7ef4\u7684\u57f9\u517b\u8fd8\u662f\u5f88\u6709\u5e2e\u52a9\u7684\u3002 EE16A&B: Designing Information Devices and Systems I&II \u662f\u4f2f\u514b\u5229 EE \u5b66\u751f\u7684\u5927\u4e00\u5165\u95e8\u8bfe\uff0c\u5176\u4e2d EE16A \u6ce8\u91cd\u901a\u8fc7\u7535\u8def\u4ece\u5b9e\u9645\u73af\u5883\u4e2d\u6536\u96c6\u548c\u5206\u6790\u6570\u636e\uff0c\u800c EE16B \u5219\u4fa7\u91cd\u4ece\u8fd9\u4e9b\u6536\u96c6\u5230\u7684\u6570\u636e\u8fdb\u884c\u5206\u6790\u5e76\u505a\u51fa\u9884\u6d4b\u884c\u4e3a\u3002 \u4fe1\u53f7\u4e0e\u7cfb\u7edf \u4fe1\u53f7\u4e0e\u7cfb\u7edf\u662f\u4e00\u95e8\u6211\u89c9\u5f97\u975e\u5e38\u503c\u5f97\u4e00\u4e0a\u7684\u8bfe\uff0c\u6700\u521d\u5b66\u5b83\u53ea\u662f\u4e3a\u4e86\u6ee1\u8db3\u6211\u5bf9\u5085\u91cc\u53f6\u53d8\u6362\u7684\u597d\u5947\uff0c\u4f46\u5b66\u5b8c\u4e4b\u540e\u6211\u624d\u4e0d\u7981\u611f\u53f9\uff0c\u5085\u7acb\u53f6\u53d8\u6362\u7ed9\u6211\u63d0\u4f9b\u4e86\u4e00\u4e2a\u5168\u65b0\u7684\u89c6\u89d2\u53bb\u770b\u5f85\u8fd9\u4e2a\u4e16\u754c\uff0c\u5c31\u5982\u540c\u5fae\u5206\u65b9\u7a0b\u4e00\u6837\uff0c\u8ba9\u4f60\u6c89\u6d78\u5728\u7528\u6570\u5b66\u53bb\u7cbe\u786e\u63cf\u7ed8\u548c\u523b\u753b\u8fd9\u4e2a\u4e16\u754c\u7684\u4f18\u96c5\u4e0e\u795e\u5947\u4e4b\u4e2d\u3002 MIT 6.003: signal and systems \u63d0\u4f9b\u4e86\u5168\u90e8\u7684\u8bfe\u7a0b\u5f55\u5f71\u3001\u4e66\u9762\u4f5c\u4e1a\u4ee5\u53ca\u7b54\u6848\u3002\u4e5f\u53ef\u4ee5\u53bb\u770b\u8fd9\u95e8\u8bfe\u7684 \u8fdc\u53e4\u7248\u672c \u800c UCB EE120: Signal and Systems \u5173\u4e8e\u5085\u7acb\u53f6\u53d8\u6362\u7684 notes \u5199\u5f97\u975e\u5e38\u597d\uff0c\u5e76\u4e14\u63d0\u4f9b\u4e866 \u4e2a\u975e\u5e38\u6709\u8da3\u7684 Python \u7f16\u7a0b\u4f5c\u4e1a\uff0c\u8ba9\u4f60\u5b9e\u8df5\u4e2d\u8fd0\u7528\u4fe1\u53f7\u4e0e\u7cfb\u7edf\u7684\u7406\u8bba\u4e0e\u7b97\u6cd5\u3002 \u6570\u636e\u7ed3\u6784\u4e0e\u7b97\u6cd5 \u6570\u636e\u7ed3\u6784\u4e0e\u7b97\u6cd5 UCB CS61B: Data Structures and Algorithms Coursera: Algorithms I & II \u7b97\u6cd5\u8bbe\u8ba1\u4e0e\u5206\u6790 UCB CS170: Efficient Algorithms and Intractable Problems \u8f6f\u4ef6\u5de5\u7a0b \u5165\u95e8\u8bfe \u4e00\u4efd\u201c\u80fd\u8dd1\u201d\u7684\u4ee3\u7801\uff0c\u548c\u4e00\u4efd\u9ad8\u8d28\u91cf\u7684\u5de5\u4e1a\u7ea7\u4ee3\u7801\u662f\u6709\u672c\u8d28\u533a\u522b\u7684\u3002\u56e0\u6b64\u6211\u975e\u5e38\u63a8\u8350\u4f4e\u5e74\u7ea7\u7684\u540c\u5b66\u5b66\u4e60\u4e00\u4e0b MIT 6.031: Software Construction \u8fd9\u95e8\u8bfe\uff0c\u5b83\u4f1a\u4ee5 Java \u8bed\u8a00\u4e3a\u57fa\u7840\uff0c\u4ee5\u4e30\u5bcc\u7ec6\u81f4\u7684\u9605\u8bfb\u6750\u6599\u548c\u7cbe\u5fc3\u8bbe\u8ba1\u7684\u7f16\u7a0b\u7ec3\u4e60\u4f20\u6388\u5982\u4f55\u7f16\u5199 \u4e0d\u6613\u51fa bug\u3001\u7b80\u660e\u6613\u61c2\u3001\u6613\u4e8e\u7ef4\u62a4\u4fee\u6539 \u7684\u9ad8\u8d28\u91cf\u4ee3\u7801\u3002\u5927\u5230\u5b8f\u89c2\u6570\u636e\u7ed3\u6784\u8bbe\u8ba1\uff0c\u5c0f\u5230\u5982\u4f55\u5199\u6ce8\u91ca\uff0c\u9075\u5faa\u8fd9\u4e9b\u524d\u4eba\u603b\u7ed3\u7684\u7ec6\u8282\u548c\u7ecf\u9a8c\uff0c\u5bf9\u4e8e\u4f60\u6b64\u540e\u7684\u7f16\u7a0b\u751f\u6daf\u5927\u6709\u88e8\u76ca\u3002 \u4e13\u4e1a\u8bfe \u5f53\u7136\uff0c\u5982\u679c\u4f60\u60f3\u7cfb\u7edf\u6027\u5730\u4e0a\u4e00\u95e8\u8f6f\u4ef6\u5de5\u7a0b\u7684\u8bfe\u7a0b\uff0c\u90a3\u6211\u63a8\u8350\u7684\u662f\u4f2f\u514b\u5229\u7684 UCB CS169: software engineering 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\u4e13\u4e1a\u8bfe \u5f53\u7136\uff0c\u5982\u679c\u60f3\u6df1\u5165\u73b0\u4ee3\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784\u7684\u590d\u6742\u7ec6\u8282\uff0c\u8fd8\u5f97\u4e0a\u4e00\u95e8\u5927\u5b66\u672c\u79d1\u96be\u5ea6\u7684\u8bfe\u7a0b UCB CS61C: Great Ideas in Computer Architecture \u3002UC Berkeley \u4f5c\u4e3a RISC-V \u67b6\u6784\u7684\u53d1\u6e90\u5730\uff0c\u5728\u4f53\u7cfb\u7ed3\u6784\u9886\u57df\u7b97\u5f97\u4e0a\u9996\u5c48\u4e00\u6307\u3002\u5176\u8bfe\u7a0b\u975e\u5e38\u6ce8\u91cd\u5b9e\u8df5\uff0c\u4f60\u4f1a\u5728 Project \u4e2d\u624b\u5199\u6c47\u7f16\u6784\u9020\u795e\u7ecf\u7f51\u7edc\uff0c\u4ece\u96f6\u5f00\u59cb\u642d\u5efa\u4e00\u4e2a CPU\uff0c\u8fd9\u4e9b\u5b9e\u8df5\u90fd\u4f1a\u8ba9\u4f60\u5bf9\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784\u6709\u66f4\u4e3a\u6df1\u5165\u7684\u7406\u89e3\uff0c\u800c\u4e0d\u662f\u4ec5\u505c\u7559\u4e8e\u201c\u53d6\u6307\u8bd1\u7801\u6267\u884c\u8bbf\u5b58\u5199\u56de\u201d\u7684\u5355\u8c03\u80cc\u8bf5\u91cc\u3002 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MIT6.033: System Engineering \u662f MIT \u7684\u7cfb\u7edf\u5165\u95e8\u8bfe\uff0c\u4e3b\u9898\u6d89\u53ca\u4e86\u64cd\u4f5c\u7cfb\u7edf\u3001\u7f51\u7edc\u3001\u5206\u5e03\u5f0f\u548c\u7cfb\u7edf\u5b89\u5168\uff0c\u9664\u4e86\u77e5\u8bc6\u70b9\u7684\u4f20\u6388\u5916\uff0c\u8fd9\u95e8\u8bfe\u8fd8\u4f1a\u8bb2\u6388\u4e00\u4e9b\u5199\u4f5c\u548c\u8868\u8fbe\u4e0a\u7684\u6280\u5de7\uff0c\u8ba9\u4f60\u5b66\u4f1a\u5982\u4f55\u8bbe\u8ba1\u5e76\u5411\u522b\u4eba\u4ecb\u7ecd\u548c\u5206\u6790\u81ea\u5df1\u7684\u7cfb\u7edf\u3002\u8fd9\u672c\u4e66\u914d\u5957\u7684\u6559\u6750 Principles of Computer System Design: An Introduction \u4e5f\u5199\u5f97\u975e\u5e38\u597d\uff0c\u63a8\u8350\u5927\u5bb6\u9605\u8bfb\u3002 CMU 15-213: Introduction to Computer System \u662f CMU 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\uff0c\u8fd9\u95e8\u8bfe\u7684\u7f16\u7a0b\u4f5c\u4e1a\u540c\u6837\u4f1a\u7528 Julia \u7f16\u7a0b\u8bed\u8a00\uff0c\u4e0d\u8fc7\u96be\u5ea6\u548c\u6df1\u5ea6\u4e0a\u90fd\u4e0a\u4e86\u4e00\u4e2a\u53f0\u9636\u3002\u5185\u5bb9\u6d89\u53ca\u4e86\u6d6e\u70b9\u7f16\u7801\u3001Root finding\u3001\u7ebf\u6027\u7cfb\u7edf\u3001\u5fae\u5206\u65b9\u7a0b\u7b49\u7b49\u65b9\u9762\uff0c\u6574\u95e8\u8bfe\u7684\u4e3b\u65e8\u5c31\u662f\u8ba9\u4f60\u5229\u7528\u79bb\u6563\u5316\u7684\u8ba1\u7b97\u673a\u8868\u793a\u53bb\u4f30\u8ba1\u548c\u903c\u8fd1\u4e00\u4e2a\u6570\u5b66\u4e0a\u8fde\u7eed\u7684\u6982\u5ff5\u3002\u8fd9\u95e8\u8bfe\u7684\u6559\u6388\u8fd8\u4e13\u95e8\u64b0\u5199\u4e86\u4e00\u672c\u914d\u5957\u7684\u5f00\u6e90\u6559\u6750 Fundamentals of Numerical Computation \uff0c\u91cc\u9762\u9644\u6709\u4e30\u5bcc\u7684 Julia \u4ee3\u7801\u5b9e\u4f8b\u548c\u4e25\u8c28\u7684\u516c\u5f0f\u63a8\u5bfc\u3002 \u5982\u679c\u4f60\u8fd8\u610f\u72b9\u672a\u5c3d\u7684\u8bdd\uff0c\u8fd8\u6709 MIT \u7684\u6570\u503c\u5206\u6790\u7814\u7a76\u751f\u8bfe\u7a0b 18.335: Introduction to numerical method \u4f9b\u4f60\u53c2\u8003\u3002","title":"\u6570\u503c\u5206\u6790"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_12","text":"\u5982\u679c\u4e16\u95f4\u4e07\u7269\u7684\u8fd0\u52a8\u53d1\u5c55\u90fd\u80fd\u7528\u65b9\u7a0b\u6765\u523b\u753b\u548c\u63cf\u8ff0\uff0c\u8fd9\u662f\u4e00\u4ef6\u591a\u4e48\u9177\u7684\u4e8b\u60c5\u5440\uff01\u867d\u7136\u51e0\u4e4e\u4efb\u4f55\u4e00\u6240\u5b66\u6821\u7684 CS \u57f9\u517b\u65b9\u6848\u4e2d\u90fd\u6ca1\u6709\u5fae\u5206\u65b9\u7a0b\u76f8\u5173\u7684\u5fc5\u4fee\u8bfe\u7a0b\uff0c\u4f46\u6211\u8fd8\u662f\u89c9\u5f97\u638c\u63e1\u5b83\u4f1a\u8d4b\u4e88\u4f60\u4e00\u4e2a\u65b0\u7684\u89c6\u89d2\u6765\u5ba1\u89c6\u8fd9\u4e2a\u4e16\u754c\u3002 \u7531\u4e8e\u5fae\u5206\u65b9\u7a0b\u4e2d\u5f80\u5f80\u4f1a\u7528\u5230\u5f88\u591a\u590d\u53d8\u51fd\u6570\u7684\u77e5\u8bc6\uff0c\u6240\u4ee5\u5927\u5bb6\u53ef\u4ee5\u53c2\u8003 MIT18.04: Complex variables functions \u7684\u8bfe\u7a0b notes \u6765\u8865\u9f50\u5148\u4fee\u77e5\u8bc6\u3002 MIT18.03: differential equations ) \u4e3b\u8981\u8986\u76d6\u4e86\u5e38\u5fae\u5206\u65b9\u7a0b\u7684\u6c42\u89e3\uff0c\u5728\u6b64\u57fa\u7840\u4e4b\u4e0a MIT18.152: Partial differential equations ) 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Since there's no universally perfect tool, there's no universally perfect language.","title":"\u7f16\u7a0b\u5165\u95e8"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#shell","text":"MIT-Missing-Semester","title":"Shell"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#python","text":"Harvard CS50: This is CS50x UCB CS61A: Structure and Interpretation of Computer Programs","title":"Python"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#c","text":"Stanford CS106B/X: Programming Abstractions Stanford CS106L: Standard C++ Programming","title":"C++"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#rust","text":"Stanford CS110L: Safety in Systems Programming","title":"Rust"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#ocaml","text":"Cornell CS3110 textbook: Functional Programming in OCaml","title":"OCaml"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_20","text":"","title":"\u7535\u5b50\u57fa\u7840"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_21","text":"\u4f5c\u4e3a\u8ba1\u7b97\u673a\u7cfb\u7684\u5b66\u751f\uff0c\u4e86\u89e3\u4e00\u4e9b\u57fa\u7840\u7684\u7535\u8def\u77e5\u8bc6\uff0c\u611f\u53d7\u4ece\u4f20\u611f\u5668\u6536\u96c6\u6570\u636e\u5230\u6570\u636e\u5206\u6790\u518d\u5230\u7b97\u6cd5\u9884\u6d4b\u6574\u6761\u6d41\u6c34\u7ebf\uff0c\u5bf9\u4e8e\u540e\u7eed\u77e5\u8bc6\u7684\u5b66\u4e60\u4ee5\u53ca\u8ba1\u7b97\u601d\u7ef4\u7684\u57f9\u517b\u8fd8\u662f\u5f88\u6709\u5e2e\u52a9\u7684\u3002 EE16A&B: Designing Information Devices and Systems I&II \u662f\u4f2f\u514b\u5229 EE \u5b66\u751f\u7684\u5927\u4e00\u5165\u95e8\u8bfe\uff0c\u5176\u4e2d EE16A \u6ce8\u91cd\u901a\u8fc7\u7535\u8def\u4ece\u5b9e\u9645\u73af\u5883\u4e2d\u6536\u96c6\u548c\u5206\u6790\u6570\u636e\uff0c\u800c EE16B \u5219\u4fa7\u91cd\u4ece\u8fd9\u4e9b\u6536\u96c6\u5230\u7684\u6570\u636e\u8fdb\u884c\u5206\u6790\u5e76\u505a\u51fa\u9884\u6d4b\u884c\u4e3a\u3002","title":"\u7535\u8def\u57fa\u7840"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_22","text":"\u4fe1\u53f7\u4e0e\u7cfb\u7edf\u662f\u4e00\u95e8\u6211\u89c9\u5f97\u975e\u5e38\u503c\u5f97\u4e00\u4e0a\u7684\u8bfe\uff0c\u6700\u521d\u5b66\u5b83\u53ea\u662f\u4e3a\u4e86\u6ee1\u8db3\u6211\u5bf9\u5085\u91cc\u53f6\u53d8\u6362\u7684\u597d\u5947\uff0c\u4f46\u5b66\u5b8c\u4e4b\u540e\u6211\u624d\u4e0d\u7981\u611f\u53f9\uff0c\u5085\u7acb\u53f6\u53d8\u6362\u7ed9\u6211\u63d0\u4f9b\u4e86\u4e00\u4e2a\u5168\u65b0\u7684\u89c6\u89d2\u53bb\u770b\u5f85\u8fd9\u4e2a\u4e16\u754c\uff0c\u5c31\u5982\u540c\u5fae\u5206\u65b9\u7a0b\u4e00\u6837\uff0c\u8ba9\u4f60\u6c89\u6d78\u5728\u7528\u6570\u5b66\u53bb\u7cbe\u786e\u63cf\u7ed8\u548c\u523b\u753b\u8fd9\u4e2a\u4e16\u754c\u7684\u4f18\u96c5\u4e0e\u795e\u5947\u4e4b\u4e2d\u3002 MIT 6.003: signal and systems 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Algorithms and Intractable Problems","title":"\u7b97\u6cd5\u8bbe\u8ba1\u4e0e\u5206\u6790"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_26","text":"","title":"\u8f6f\u4ef6\u5de5\u7a0b"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_27","text":"\u4e00\u4efd\u201c\u80fd\u8dd1\u201d\u7684\u4ee3\u7801\uff0c\u548c\u4e00\u4efd\u9ad8\u8d28\u91cf\u7684\u5de5\u4e1a\u7ea7\u4ee3\u7801\u662f\u6709\u672c\u8d28\u533a\u522b\u7684\u3002\u56e0\u6b64\u6211\u975e\u5e38\u63a8\u8350\u4f4e\u5e74\u7ea7\u7684\u540c\u5b66\u5b66\u4e60\u4e00\u4e0b MIT 6.031: Software Construction \u8fd9\u95e8\u8bfe\uff0c\u5b83\u4f1a\u4ee5 Java \u8bed\u8a00\u4e3a\u57fa\u7840\uff0c\u4ee5\u4e30\u5bcc\u7ec6\u81f4\u7684\u9605\u8bfb\u6750\u6599\u548c\u7cbe\u5fc3\u8bbe\u8ba1\u7684\u7f16\u7a0b\u7ec3\u4e60\u4f20\u6388\u5982\u4f55\u7f16\u5199 \u4e0d\u6613\u51fa bug\u3001\u7b80\u660e\u6613\u61c2\u3001\u6613\u4e8e\u7ef4\u62a4\u4fee\u6539 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2021\u5e7412\u670812\u65e5\u5199\u4e8e\u71d5\u56ed","title":"\u540e\u8bb0"},{"location":"en/%E5%9F%B9%E5%85%BB%E6%96%B9%E6%A1%88Pro/","text":"under construction.","title":"\u57f9\u517b\u65b9\u6848Pro"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/","text":"\u597d\u4e66\u63a8\u8350 \u7531\u4e8e\u7248\u6743\u539f\u56e0\uff0c\u4e0b\u9762\u5217\u4e3e\u7684\u56fe\u4e66\u4e2d\u9664\u4e86\u5f00\u6e90\u8d44\u6e90\u63d0\u4f9b\u4e86\u94fe\u63a5\uff0c\u5176\u4ed6\u7684\u8d44\u6e90\u8bf7\u5927\u5bb6\u81ea\u884c\u901a\u8fc7 libgen \u6216 z-lib \u67e5\u627e\u3002 \u8d44\u6e90\u6c47\u603b Free Programming Books : \u5f00\u6e90\u7f16\u7a0b\u4e66\u7c4d\u8d44\u6e90\u6c47\u603b CS Textbook Recommendations : \u8ba1\u7b97\u673a\u79d1\u5b66\u65b9\u5411\u63a8\u8350\u6559\u6750\u5217\u8868 C Book Guide and List : C\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868 C++ Book Guide and List : C++\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868 Python Book Guide and List : Python\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868 \u7cfb\u7edf\u5165\u95e8 Computer Systems: A Programmer's Perspective [ \u8c46\u74e3 ] Principles of Computer System Design: An Introduction [ \u8c46\u74e3 ] \u64cd\u4f5c\u7cfb\u7edf \u73b0\u4ee3\u64cd\u4f5c\u7cfb\u7edf: \u539f\u7406\u4e0e\u5b9e\u73b0 [ \u8c46\u74e3 ] Operating Systems: Three Easy Pieces [ \u8c46\u74e3 ] Modern Operating Systems [ \u8c46\u74e3 ] Operating Systems: Principles and Practice [ \u8c46\u74e3 ] \u8ba1\u7b97\u673a\u7f51\u7edc Computer Networks: A Systems Approach [ \u8c46\u74e3 ] Computer Networking: A Top-Down Approach [ \u8c46\u74e3 ] \u5206\u5e03\u5f0f\u7cfb\u7edf Patterns of Distributed System (Blog) Distributed Systems for Fun and Profit (Blog) Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems [ \u8c46\u74e3 ] \u6570\u636e\u5e93\u7cfb\u7edf Architecture of a Database System [ \u8c46\u74e3 ] Readings in Database Systems [ \u8c46\u74e3 ] Database System Concepts [ \u8c46\u74e3 ] \u7f16\u8bd1\u539f\u7406 Engineering a Compiler [ \u8c46\u74e3 ] Compilers: Principles, Techniques, and Tools [ \u8c46\u74e3 ] \u8ba1\u7b97\u673a\u7f16\u7a0b\u8bed\u8a00 \u8ba1\u7b97\u673a\u7a0b\u5e8f\u7684\u6784\u9020\u548c\u89e3\u91ca [ \u8c46\u74e3 ] Essentials of Programming Languages [ \u8c46\u74e3 ] Practical Foundations for Programming Languages [ \u8c46\u74e3 ] Software Foundations [ \u8c46\u74e3 ] [ \u5317\u5927\u76f8\u5173\u8bfe\u7a0b ] Types and Programming Languages [ \u8c46\u74e3 ] [ \u5317\u5927\u76f8\u5173\u8bfe\u7a0b ] \u4f53\u7cfb\u7ed3\u6784 \u8d85\u6807\u91cf\u5904\u7406\u5668\u8bbe\u8ba1: Superscalar RISC Processor Design [ \u8c46\u74e3 ] Computer Organization and Design RISC-V Edition [ \u8c46\u74e3 ] Computer Organization and Design: The Hardware/Software Interface [ \u8c46\u74e3 ] Computer Architecture: A Quantitative Approach [ \u8c46\u74e3 ] \u7406\u8bba\u8ba1\u7b97\u673a\u79d1\u5b66 Introduction to the Theory of Computation [ \u8c46\u74e3 ] \u5bc6\u7801\u5b66 Cryptography Engineering: Design Principles and Practical Applications [ \u8c46\u74e3 ] Introduction to Modern Cryptography [ \u8c46\u74e3 ] \u9006\u5411\u5de5\u7a0b \u9006\u5411\u5de5\u7a0b\u6838\u5fc3\u539f\u7406 [ \u8c46\u74e3 ] \u52a0\u5bc6\u4e0e\u89e3\u5bc6 [ \u8c46\u74e3 ] \u8ba1\u7b97\u673a\u56fe\u5f62\u5b66 Monte Carlo theory, methods and examples Advanced Global Illumination [ \u8c46\u74e3 ] Fundamentals of Computer Graphics [ \u8c46\u74e3 ] Fluid Simulation for Computer Graphics [ \u8c46\u74e3 ] Physically Based Rendering: From Theory To Implementation [ \u8c46\u74e3 ] Real-Time Rendering [ \u8c46\u74e3 ] \u6e38\u620f\u5f15\u64ce \u6e38\u620f\u7f16\u7a0b\u6a21\u5f0f: Game Programming Patterns [ \u8c46\u74e3 ] \u5b9e\u65f6\u78b0\u649e\u68c0\u6d4b\u7b97\u6cd5\u6280\u672f [ \u8c46\u74e3 ] Game AI Pro Series [ \u8c46\u74e3 ] Artificial Intelligence for Games [ \u8c46\u74e3 ] Game Engine Architecture [ \u8c46\u74e3 ] Game Programming Gems Series [ \u8c46\u74e3 ] \u8f6f\u4ef6\u5de5\u7a0b Software Engineering at Google [ \u8c46\u74e3 ] \u8bbe\u8ba1\u6a21\u5f0f \u8bbe\u8ba1\u6a21\u5f0f: \u53ef\u590d\u7528\u9762\u5411\u5bf9\u8c61\u8f6f\u4ef6\u7684\u57fa\u7840 [ \u8c46\u74e3 ] \u5927\u8bdd\u8bbe\u8ba1\u6a21\u5f0f [ \u8c46\u74e3 ] Head First \u8bbe\u8ba1\u6a21\u5f0f [ \u8c46\u74e3 ] \u6df1\u5ea6\u5b66\u4e60 \u52a8\u624b\u5b66\u6df1\u5ea6\u5b66\u4e60 [ \u8c46\u74e3 ] \u795e\u7ecf\u7f51\u7edc\u4e0e\u6df1\u5ea6\u5b66\u4e60 [ \u8c46\u74e3 ] \u6df1\u5ea6\u5b66\u4e60\u5165\u95e8 [ \u8c46\u74e3 ] \u7b80\u5355\u7c97\u66b4 TensorFlow 2 (Tutorial) Speech and Language Processing [ \u8c46\u74e3 ] \u8ba1\u7b97\u673a\u89c6\u89c9 Multiple View Geometry in Computer Vision [ \u8c46\u74e3 ] \u673a\u5668\u4eba Probabilistic Robotics [ \u8c46\u74e3 ] \u9762\u8bd5 \u5251\u6307 Offer\uff1a\u540d\u4f01\u9762\u8bd5\u5b98\u7cbe\u8bb2\u5178\u578b\u7f16\u7a0b\u9898 [ \u8c46\u74e3 ] Cracking The Coding Interview [ \u8c46\u74e3 ]","title":"Book Recommendation"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_1","text":"\u7531\u4e8e\u7248\u6743\u539f\u56e0\uff0c\u4e0b\u9762\u5217\u4e3e\u7684\u56fe\u4e66\u4e2d\u9664\u4e86\u5f00\u6e90\u8d44\u6e90\u63d0\u4f9b\u4e86\u94fe\u63a5\uff0c\u5176\u4ed6\u7684\u8d44\u6e90\u8bf7\u5927\u5bb6\u81ea\u884c\u901a\u8fc7 libgen \u6216 z-lib \u67e5\u627e\u3002","title":"\u597d\u4e66\u63a8\u8350"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_2","text":"Free Programming Books : \u5f00\u6e90\u7f16\u7a0b\u4e66\u7c4d\u8d44\u6e90\u6c47\u603b CS Textbook Recommendations : \u8ba1\u7b97\u673a\u79d1\u5b66\u65b9\u5411\u63a8\u8350\u6559\u6750\u5217\u8868 C Book Guide and List : C\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868 C++ Book Guide and List : C++\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868 Python Book Guide and List : Python\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_3","text":"Computer Systems: A Programmer's Perspective [ \u8c46\u74e3 ] Principles of Computer System Design: An Introduction [ \u8c46\u74e3 ]","title":"\u7cfb\u7edf\u5165\u95e8"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_4","text":"\u73b0\u4ee3\u64cd\u4f5c\u7cfb\u7edf: \u539f\u7406\u4e0e\u5b9e\u73b0 [ \u8c46\u74e3 ] Operating Systems: Three Easy Pieces [ \u8c46\u74e3 ] Modern Operating Systems [ \u8c46\u74e3 ] Operating Systems: Principles and Practice [ \u8c46\u74e3 ]","title":"\u64cd\u4f5c\u7cfb\u7edf"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_5","text":"Computer Networks: A Systems Approach [ \u8c46\u74e3 ] Computer Networking: A Top-Down Approach [ \u8c46\u74e3 ]","title":"\u8ba1\u7b97\u673a\u7f51\u7edc"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_6","text":"Patterns of Distributed System (Blog) Distributed Systems for Fun and Profit (Blog) Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems [ \u8c46\u74e3 ]","title":"\u5206\u5e03\u5f0f\u7cfb\u7edf"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_7","text":"Architecture of a Database System [ \u8c46\u74e3 ] Readings in Database Systems [ \u8c46\u74e3 ] Database System Concepts [ \u8c46\u74e3 ]","title":"\u6570\u636e\u5e93\u7cfb\u7edf"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_8","text":"Engineering a Compiler [ \u8c46\u74e3 ] Compilers: Principles, Techniques, and Tools [ \u8c46\u74e3 ]","title":"\u7f16\u8bd1\u539f\u7406"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_9","text":"\u8ba1\u7b97\u673a\u7a0b\u5e8f\u7684\u6784\u9020\u548c\u89e3\u91ca [ \u8c46\u74e3 ] Essentials of Programming Languages [ \u8c46\u74e3 ] Practical Foundations for Programming Languages [ \u8c46\u74e3 ] Software Foundations [ \u8c46\u74e3 ] [ 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Surely you will enjoy the elegance and magic of computers in a relaxing and jolly journey.","title":"Descriptions"},{"location":"en/%E4%BD%93%E7%B3%BB%E7%BB%93%E6%9E%84/N2T/#course-resources","text":"Course Website\uff1a Nand2Tetris I , Nand2Tetris II Recordings\uff1aRefer to course website Textbook: The Elements of Computing Systems: Building a Modern Computer from First Principles (CN-zh version) Assignments\uff1a10 projects to construct a computer, refer to the course website for more details","title":"Course Resources"},{"location":"en/%E4%BD%93%E7%B3%BB%E7%BB%93%E6%9E%84/N2T/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig are maintained in PKUFlyingPig/NandToTetris - GitHub .","title":"Personal Resources"},{"location":"en/%E5%B9%B6%E8%A1%8C%E4%B8%8E%E5%88%86%E5%B8%83%E5%BC%8F%E7%B3%BB%E7%BB%9F/CS149/","text":"CMU 15-418/Stanford CS149: Parallel Computing \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aCMU \u548c Stanford \u5148\u4fee\u8981\u6c42\uff1a\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784\uff0c\u719f\u6089 C++ \u7f16\u7a0b\u8bed\u8a00\uff1aC++ \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 Kayvon Fatahalian \u6559\u6388\u6b64\u524d\u5728 CMU \u5f00\u4e86 15-418 \u8fd9\u95e8\u8bfe\uff0c\u540e\u6765\u4ed6\u6210\u4e3a Stanford \u7684\u52a9\u7406\u6559\u6388\u540e\u53c8\u5f00\u4e86\u7c7b\u4f3c\u7684\u8bfe\u7a0b CS149\u3002\u4f46\u603b\u4f53\u6765\u8bf4\uff0c15-418 \u5305\u542b\u7684\u8bfe\u7a0b\u5185\u5bb9\u66f4\u4e30\u5bcc\uff0c\u5e76\u4e14\u6709\u8bfe\u7a0b\u56de\u653e\uff0c\u4f46 CS149 \u7684\u7f16\u7a0b\u4f5c\u4e1a\u66f4 fashion \u4e00\u4e9b\u3002\u6211\u4e2a\u4eba\u662f\u89c2\u770b\u7684 15-418 \u7684\u8bfe\u7a0b\u5f55\u5f71\u4f46\u5b8c\u6210\u7684 CS149 \u7684\u4f5c\u4e1a\u3002 \u8fd9\u95e8\u8bfe\u4f1a\u5e26\u4f60\u6df1\u5165\u7406\u89e3\u73b0\u4ee3\u5e76\u884c\u8ba1\u7b97\u67b6\u6784\u7684\u8bbe\u8ba1\u539f\u5219\u4e0e\u5fc5\u8981\u6743\u8861\uff0c\u5e76\u5b66\u4f1a\u5982\u4f55\u5145\u5206\u5229\u7528\u786c\u4ef6\u8d44\u6e90\u4ee5\u53ca\u8f6f\u4ef6\u7f16\u7a0b\u6846\u67b6\uff08\u4f8b\u5982 CUDA\uff0cMPI\uff0cOpenMP \u7b49\uff09\u7f16\u5199\u9ad8\u6027\u80fd\u7684\u5e76\u884c\u7a0b\u5e8f\u3002\u7531\u4e8e\u5e76\u884c\u8ba1\u7b97\u67b6\u6784\u7684\u590d\u6742\u6027\uff0c\u8fd9\u95e8\u8bfe\u4f1a\u6d89\u53ca\u8bf8\u591a\u9ad8\u7ea7\u4f53\u7cfb\u7ed3\u6784\u4e0e\u7f51\u7edc\u901a\u4fe1\u7684\u5185\u5bb9\uff0c\u77e5\u8bc6\u70b9\u76f8\u5f53\u5e95\u5c42\u4e14\u786c\u6838\u3002\u4e0e\u6b64\u540c\u65f6\uff0c5 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a\u5219\u662f\u4ece\u8f6f\u4ef6\u7684\u5c42\u9762\u57f9\u517b\u5b66\u751f\u5bf9\u4e0a\u5c42\u62bd\u8c61\u7684\u7406\u89e3\u4e0e\u8fd0\u7528\uff0c\u5177\u4f53\u4f1a\u8ba9\u4f60\u5206\u6790\u5e76\u884c\u7a0b\u5e8f\u7684\u74f6\u9888\u3001\u7f16\u5199\u591a\u7ebf\u7a0b\u540c\u6b65\u4ee3\u7801\u3001\u5b66\u4e60 CUDA \u7f16\u7a0b\u3001OpenMP \u7f16\u7a0b\u4ee5\u53ca\u524d\u6bb5\u65f6\u95f4\u5927\u70ed\u7684 Spark \u6846\u67b6\u7b49\u7b49\u3002\u771f\u6b63\u610f\u4e49\u4e0a\u5c06\u7406\u8bba\u4e0e\u5b9e\u8df5\u5b8c\u7f8e\u5730\u7ed3\u5408\u5728\u4e86\u4e00\u8d77\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a CMU15418 , CS149 \u8bfe\u7a0b\u89c6\u9891\uff1a http://15418.courses.cs.cmu.edu/spring2016/lectures \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://gfxcourses.stanford.edu/cs149/fall21 \uff0c5 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS149-parallel-computing - 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It uses CMakeLists.txt to define build configuration, and have more functionalities compared to GNU make. It is highly recommanded to learn GNU Make and get familiar with Makefile first before learning CMake.","title":"Why CMake"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/CMake/#how-to-learn-cmake","text":"Compare to Makefile , CMakeLists.txt is more obscure and difficult to understand and use. Nowadays many IDEs (e.g., Visual Studio, CLion) offer functionalities to generate CMakeLists.txt automaticly, but it's still necessary to manage basic usage of CMakeLists.txt . Besides Official CMake Tutorial , this one-hour video tutorial (in Chinese) presented by IPADS group at SJTU is also a good learning resource.","title":"How to learn CMake"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Docker/","text":"Docker \u4e3a\u4ec0\u4e48\u4f7f\u7528 Docker \u4f7f\u7528\u522b\u4eba\u5199\u597d\u7684\u8f6f\u4ef6/\u5de5\u5177\u6700\u5927\u7684\u969c\u788d\u662f\u4ec0\u4e48\u2014\u2014\u5fc5\u7136\u662f\u914d\u73af\u5883\u3002\u914d\u73af\u5883\u5e26\u6765\u7684\u6298\u78e8\u4f1a\u6781\u5927\u5730\u6d88\u89e3\u4f60\u5bf9\u8f6f\u4ef6\u3001\u7f16\u7a0b\u672c\u8eab\u7684\u5174\u8da3\u3002\u865a\u62df\u673a\u53ef\u4ee5\u89e3\u51b3\u914d\u73af\u5883\u7684\u4e00\u90e8\u5206\u95ee\u9898\uff0c\u4f46\u5b83\u5e9e\u5927\u7b28\u91cd\uff0c\u4e14\u4e3a\u4e86\u67d0\u4e2a\u5e94\u7528\u7684\u73af\u5883\u914d\u7f6e\u597d\u50cf\u4e5f\u4e0d\u503c\u5f97\u6a21\u62df\u4e00\u4e2a\u5168\u65b0\u7684\u64cd\u4f5c\u7cfb\u7edf\u3002 Docker 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The father of Linux, Linus Torvalds developed Git to maintain the version control of Linux, replacing the centralized version control tools which were difficult and costly to use. The design of Git is very elegant, but beginners usually find it very difficult to use without understanding its internal logic. It is very easy to mess up the version history if misusing the commands. Git is a powerful tool and when you finally master it, you will find all the effort paid off. How to learn Git Different from Vim, I don't suggest beginners use Git rashly without fully understanding it, because its inner logic can not be acquainted by practicing. Here is my recommended learning path: Read this Git tutorial in English, or you can watch this Git tutorial (by \u5c1a\u7845\u8c37) in Chinese. Read Chap1 - Chap5 of this open source book Pro Git . Yes, to learn Git, you need to read a book. Now that you have understood its principles and most of its usages, it's time to consolidate those commands by practicing. How to use Git properly is a kind of philosophy. I recommend reading this blog How to Write a Git Commit Message . You are now in love with Git and are not content with only using it, you want to build a Git by yourself! Great, that's exactly what I was thinking. This tutorial will satisfy you! What? Building your own Git is not enough? Seems that you are also passionate about reinventing the wheels. These two GitHub projects, build-your-own-x and project-based-learning , collected many wheel-reinventing tutorials, e.g., text editor, virtual machine, docker, TCP and so on.","title":"Git"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Git/#git","text":"","title":"Git"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Git/#why-git","text":"Git is a distributed version control system. The father of Linux, Linus Torvalds developed Git to maintain the version control of Linux, replacing the centralized version control tools which were difficult and costly to use. The design of Git is very elegant, but beginners usually find it very difficult to use without understanding its internal logic. It is very easy to mess up the version history if misusing the commands. Git is a powerful tool and when you finally master it, you will find all the effort paid off.","title":"Why Git"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Git/#how-to-learn-git","text":"Different from Vim, I don't suggest beginners use Git rashly without fully understanding it, because its inner logic can not be acquainted by practicing. Here is my recommended learning path: Read this Git tutorial in English, or you can watch this Git tutorial (by \u5c1a\u7845\u8c37) in Chinese. Read Chap1 - Chap5 of this open source book Pro Git . Yes, to learn Git, you need to read a book. Now that you have understood its principles and most of its usages, it's time to consolidate those commands by practicing. How to use Git properly is a kind of philosophy. I recommend reading this blog How to Write a Git Commit Message . You are now in love with Git and are not content with only using it, you want to build a Git by yourself! Great, that's exactly what I was thinking. This tutorial will satisfy you! What? Building your own Git is not enough? Seems that you are also passionate about reinventing the wheels. These two GitHub projects, build-your-own-x and project-based-learning , collected many wheel-reinventing tutorials, e.g., text editor, virtual machine, docker, TCP and so on.","title":"How to learn Git"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/GitHub/","text":"GitHub GitHub \u662f\u4ec0\u4e48 \u4ece\u529f\u80fd\u4e0a\u6765\u8bf4\uff0cGitHub \u662f\u4e00\u4e2a\u5728\u7ebf\u4ee3\u7801\u6258\u7ba1\u5e73\u53f0\u3002\u4f60\u53ef\u4ee5\u5c06\u4f60\u7684\u672c\u5730 Git \u4ed3\u5e93\u6258\u7ba1\u5230 GitHub \u4e0a\uff0c\u4f9b\u591a\u4eba\u540c\u65f6\u5f00\u53d1\u6d4f\u89c8\u3002\u4f46\u73b0\u5982\u4eca GitHub \u7684\u610f\u4e49\u5df2\u8fdc\u4e0d\u6b62\u5982\u6b64\uff0c\u5b83\u5df2\u7ecf\u6f14\u53d8\u4e3a\u4e00\u4e2a\u975e\u5e38\u6d3b\u8dc3\u4e14\u8d44\u6e90\u6781\u4e3a\u4e30\u5bcc\u7684\u5f00\u6e90\u4ea4\u6d41\u793e\u533a\u3002\u5168\u4e16\u754c\u7684\u8f6f\u4ef6\u5f00\u53d1\u8005\u5728 GitHub 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\u5373\u53ef\u8fdb\u5165\u7ec3\u4e60\u7a0b\u5e8f\u3002 \u6700\u540e\u5c31\u662f\u5f3a\u8feb\u81ea\u5df1\u4f7f\u7528 Vim \u8fdb\u884c\u5f00\u53d1\uff0cIDE \u91cc\u53ef\u4ee5\u5b89\u88c5 Vim \u63d2\u4ef6\u3002 \u7b49\u4f60\u5b8c\u5168\u9002\u5e94 Vim \u4e4b\u540e\u65b0\u7684\u4e16\u754c\u4fbf\u5411\u4f60\u655e\u5f00\u4e86\u5927\u95e8\uff0c\u4f60\u53ef\u4ee5\u6309\u9700\u914d\u7f6e\u81ea\u5df1\u7684 Vim\uff08\u4fee\u6539 .vimrc \u6587\u4ef6\uff09\uff0c\u7f51\u4e0a\u6709\u6570\u4e0d\u80dc\u6570\u7684\u8d44\u6e90\u53ef\u4ee5\u501f\u9274\u3002 \u63a8\u8350\u53c2\u8003\u8d44\u6599 Neil, Drew. Practical Vim: Edit Text at the Speed of Thought. N.p., Pragmatic Bookshelf, 2015. Neil, Drew. Modern Vim: Craft Your Development Environment with Vim 8 and Neovim. United States, Pragmatic Bookshelf.","title":"Vim"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Vim/#vim","text":"","title":"Vim"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Vim/#vim_1","text":"\u5728\u6211\u770b\u6765 Vim \u7f16\u8f91\u5668\u6709\u5982\u4e0b\u7684\u597d\u5904\uff1a \u8ba9\u4f60\u7684\u6574\u4e2a\u5f00\u53d1\u8fc7\u7a0b\u624b\u6307\u4e0d\u9700\u8981\u79bb\u5f00\u952e\u76d8\uff0c\u800c\u4e14\u5149\u6807\u7684\u79fb\u52a8\u4e0d\u9700\u8981\u65b9\u5411\u952e\u4f7f\u5f97\u4f60\u7684\u624b\u6307\u4e00\u76f4\u5904\u5728\u6253\u5b57\u7684\u6700\u4f73\u4f4d\u7f6e\u3002 \u65b9\u4fbf\u7684\u6587\u4ef6\u5207\u6362\u4ee5\u53ca\u9762\u677f\u63a7\u5236\u53ef\u4ee5\u8ba9\u4f60\u540c\u65f6\u5f00\u53d1\u591a\u4efd\u6587\u4ef6\u751a\u81f3\u540c\u4e00\u4e2a\u6587\u4ef6\u7684\u4e0d\u540c\u4f4d\u7f6e\u3002 Vim \u7684\u5b8f\u64cd\u4f5c\u53ef\u4ee5\u6279\u91cf\u5316\u5904\u7406\u91cd\u590d\u64cd\u4f5c\uff08\u4f8b\u5982\u591a\u884c tab\uff0c\u6279\u91cf\u52a0\u53cc\u5f15\u53f7\u7b49\u7b49\uff09 Vim \u662f\u5f88\u591a\u670d\u52a1\u5668\u81ea\u5e26\u7684\u547d\u4ee4\u884c\u7f16\u8f91\u5668\uff0c\u5f53\u4f60\u901a\u8fc7 ssh \u8fde\u63a5\u8fdc\u7a0b\u670d\u52a1\u5668\u4e4b\u540e\uff0c\u7531\u4e8e\u6ca1\u6709\u56fe\u5f62\u754c\u9762\uff0c\u53ea\u80fd\u5728\u547d\u4ee4\u884c\u91cc\u8fdb\u884c\u5f00\u53d1\uff08\u5f53\u7136\u73b0\u5728\u5f88\u591a IDE \u5982 VS Code \u63d0\u4f9b\u4e86 ssh \u63d2\u4ef6\u53ef\u4ee5\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\uff09\u3002 \u5f02\u5e38\u4e30\u5bcc\u7684\u63d2\u4ef6\u751f\u6001\uff0c\u8ba9\u4f60\u62e5\u6709\u4e16\u754c\u4e0a\u6700\u82b1\u91cc\u80e1\u54e8\u7684\u547d\u4ee4\u884c\u7f16\u8f91\u5668\u3002","title":"\u4e3a\u4ec0\u4e48\u5b66\u4e60 Vim"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Vim/#vim_2","text":"\u4e0d\u5e78\u7684\u662f Vim \u7684\u5b66\u4e60\u66f2\u7ebf\u786e\u5b9e\u76f8\u5f53\u9661\u5ced\uff0c\u6211\u82b1\u4e86\u597d\u51e0\u4e2a\u661f\u671f\u624d\u6162\u6162\u9002\u5e94\u4e86\u7528 Vim \u8fdb\u884c\u5f00\u53d1\u7684\u8fc7\u7a0b\u3002\u6700\u5f00\u59cb\u4f60\u4f1a\u89c9\u5f97\u975e\u5e38\u4e0d\u9002\u5e94\uff0c\u4f46\u4e00\u65e6\u71ac\u8fc7\u4e86\u521d\u59cb\u9636\u6bb5\uff0c\u76f8\u4fe1\u6211\uff0c\u4f60\u4f1a\u7231\u4e0a Vim\u3002 Vim \u7684\u5b66\u4e60\u8d44\u6599\u6d69\u5982\u70df\u6d77\uff0c\u4f46\u638c\u63e1\u5b83\u6700\u597d\u7684\u65b9\u5f0f\u8fd8\u662f\u5c06\u5b83\u7528\u5728\u65e5\u5e38\u7684\u5f00\u53d1\u8fc7\u7a0b\u4e2d\uff0c\u800c\u4e0d\u662f\u4e00\u4e0a\u6765\u5c31\u53bb\u5b66\u5404\u79cd\u82b1\u91cc\u80e1\u54e8\u7684\u9ad8\u7ea7 Vim \u6280\u5de7\u3002\u4e2a\u4eba\u63a8\u8350\u7684\u5b66\u4e60\u8def\u7ebf\u5982\u4e0b\uff1a \u5148\u9605\u8bfb \u8fd9\u7bc7 tutorial \uff0c\u638c\u63e1\u57fa\u672c\u7684 Vim \u6982\u5ff5\u548c\u4f7f\u7528\u65b9\u5f0f\u3002 \u7528 Vim \u81ea\u5e26\u7684 vimtutor \u8fdb\u884c\u7ec3\u4e60\uff0c\u5b89\u88c5\u5b8c Vim \u4e4b\u540e\u76f4\u63a5\u5728\u547d\u4ee4\u884c\u91cc\u8f93\u5165 vimtutor \u5373\u53ef\u8fdb\u5165\u7ec3\u4e60\u7a0b\u5e8f\u3002 \u6700\u540e\u5c31\u662f\u5f3a\u8feb\u81ea\u5df1\u4f7f\u7528 Vim \u8fdb\u884c\u5f00\u53d1\uff0cIDE \u91cc\u53ef\u4ee5\u5b89\u88c5 Vim \u63d2\u4ef6\u3002 \u7b49\u4f60\u5b8c\u5168\u9002\u5e94 Vim \u4e4b\u540e\u65b0\u7684\u4e16\u754c\u4fbf\u5411\u4f60\u655e\u5f00\u4e86\u5927\u95e8\uff0c\u4f60\u53ef\u4ee5\u6309\u9700\u914d\u7f6e\u81ea\u5df1\u7684 Vim\uff08\u4fee\u6539 .vimrc \u6587\u4ef6\uff09\uff0c\u7f51\u4e0a\u6709\u6570\u4e0d\u80dc\u6570\u7684\u8d44\u6e90\u53ef\u4ee5\u501f\u9274\u3002","title":"\u5982\u4f55\u5b66\u4e60 Vim"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Vim/#_1","text":"Neil, Drew. Practical Vim: Edit Text at the Speed of Thought. N.p., Pragmatic Bookshelf, 2015. Neil, Drew. Modern Vim: Craft Your Development Environment with Vim 8 and Neovim. United States, Pragmatic Bookshelf.","title":"\u63a8\u8350\u53c2\u8003\u8d44\u6599"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/thesis/","text":"\u6bd5\u4e1a\u8bba\u6587 \u4e3a\u4ec0\u4e48\u5199\u8fd9\u4efd\u6559\u7a0b 2022\u5e74\uff0c\u6211\u672c\u79d1\u6bd5\u4e1a\u4e86\u3002\u5728\u5f00\u59cb\u52a8\u624b\u5199\u6bd5\u4e1a\u8bba\u6587\u7684\u65f6\u5019\uff0c\u6211\u5c34\u5c2c\u5730\u53d1\u73b0\uff0c\u6211\u5bf9 Word \u7684\u638c\u63e1\u7a0b\u5ea6\u4ec5\u9650\u4e8e\u8c03\u8282\u5b57\u4f53\u3001\u4fdd\u5b58\u5bfc\u51fa\u8fd9\u4e9b\u50bb\u74dc\u529f\u80fd\u3002\u66fe\u60f3\u8f6c\u6218 Latex\uff0c\u4f46\u8bba\u6587\u7684\u6bb5\u843d\u683c\u5f0f\u8981\u6c42\u8c03\u6574\u8d77\u6765\u8fd8\u662f\u7528 Word 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\u786e\u5b9a\u8bba\u6587\u7684\u683c\u5f0f\u8981\u6c42\uff1a\u901a\u5e38\u5b66\u9662\u90fd\u4f1a\u4e0b\u53d1\u6bd5\u4e1a\u8bba\u6587\u7684\u683c\u5f0f\u8981\u6c42\uff08\u5404\u7ea7\u6807\u9898\u7684\u5b57\u4f53\u5b57\u53f7\u3001\u56fe\u4f8b\u548c\u5f15\u7528\u7684\u683c\u5f0f\u7b49\u7b49\uff09\uff0c\u5982\u679c\u66f4\u4e3a\u8d34\u5fc3\u7684\u8bdd\u751a\u81f3\u4f1a\u76f4\u63a5\u7ed9\u51fa\u8bba\u6587\u6a21\u7248\uff08\u5982\u662f\u6b64\u60c5\u51b5\u8bf7\u76f4\u63a5\u8df3\u8f6c\u5230\u4e0b\u4e00\u6b65\uff09\u3002\u5f88\u4e0d\u5e78\u7684\u662f\uff0c\u6211\u7684\u5b66\u9662\u5e76\u6ca1\u6709\u4e0b\u53d1\u6807\u51c6\u7684\u8bba\u6587\u683c\u5f0f\u8981\u6c42\uff0c\u8fd8\u63d0\u4f9b\u4e86\u4e00\u4efd\u683c\u5f0f\u6df7\u4e71\u51e0\u4e4e\u6beb\u65e0\u7528\u5904\u7684\u8bba\u6587\u6a21\u7248\u8188\u5e94\u6211\uff0c\u88ab\u903c\u65e0\u5948\u4e4b\u4e0b\u6211\u627e\u5230\u4e86\u5317\u4eac\u5927\u5b66\u7814\u7a76\u751f\u7684 \u8bba\u6587\u683c\u5f0f\u8981\u6c42 \uff0c\u5e76\u6309\u7167\u5176\u8981\u6c42\u5236\u4f5c\u4e86 \u4e00\u4efd\u6a21\u7248 \uff0c\u5927\u5bb6\u9700\u8981\u7684\u8bdd\u81ea\u53d6\uff0c\u672c\u4eba\u4e0d\u627f\u62c5\u65e0\u6cd5\u6bd5\u4e1a\u7b49\u4efb\u4f55\u8d23\u4efb\u3002 \u5b66\u4e60 Word \u6392\u7248\uff1a\u5230\u8fbe\u8fd9\u4e00\u6b65\u7684\u7ae5\u978b\u5206\u4e3a\u4e24\u7c7b\uff0c\u4e00\u662f\u5df2\u7ecf\u62e5\u6709\u4e86\u5b66\u9662\u63d0\u4f9b\u7684\u6807\u51c6\u6a21\u7248\uff0c\u4e8c\u662f\u53ea\u6709\u4e00\u4efd\u865a\u65e0\u7f25\u7f08\u7684\u683c\u5f0f\u8981\u6c42\u3002\u90a3\u73b0\u5728\u5f53\u52a1\u4e4b\u6025\u5c31\u662f\u5b66\u4e60\u57fa\u7840\u7684 Word \u6392\u7248\u6280\u672f\uff0c\u5bf9\u4e8e\u524d\u8005\u53ef\u4ee5\u5b66\u4f1a\u4f7f\u7528\u6a21\u7248\uff0c\u5bf9\u4e8e\u540e\u8005\u5219\u53ef\u4ee5\u5b66\u4f1a\u5236\u4f5c\u6a21\u7248\u3002\u6b64\u65f6\u5207\u8bb0\u4e0d\u8981\u96c4\u5fc3\u52c3\u52c3\u5730\u9009\u62e9\u4e00\u4e2a\u5341\u51e0\u4e2a\u5c0f\u65f6\u7684 Word \u6559\u5b66\u89c6\u9891\u5f00\u59cb\u5934\u60ac\u6881\u9525\u523a\u80a1\uff0c\u56e0\u4e3a\u751f\u4ea7\u4e00\u4efd\u5e94\u4ed8\u6bd5\u4e1a\u7684\u5b66\u672f\u5783\u573e\u53ea\u8981\u5b66\u534a\u5c0f\u65f6\u80fd\u4e0a\u624b\u5c31\u591f\u4e86\u3002\u6211\u5f53\u65f6\u770b\u7684 \u4e00\u4e2a B \u7ad9\u7684\u6559\u5b66\u89c6\u9891 \uff0c\u77ed\u5c0f\u7cbe\u608d\u975e\u5e38\u5b9e\u7528\uff0c\u5168\u957f\u534a\u5c0f\u65f6\u6781\u901f\u5165\u95e8\u3002 \u751f\u4ea7\u5b66\u672f\u5783\u573e\uff1a\u6700\u5bb9\u6613\u7684\u4e00\u6b65\uff0c\u5927\u5bb6\u516b\u4ed9\u8fc7\u6d77\uff0c\u5404\u663e\u795e\u901a\u5427\uff0c\u795d\u5927\u5bb6\u6bd5\u4e1a\u987a\u5229\uff5e\uff5e","title":"\u5982\u4f55\u7528 Word \u5199\u6bd5\u4e1a\u8bba\u6587"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/","text":"\u5b9e\u7528\u5de5\u5177\u7bb1 \u4e0b\u8f7d\u5de5\u5177 Libgen : PDF\u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9\u3002 z-epub : ePub\u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9\u3002 bitdownloader : \u6cb9\u7ba1\u89c6\u9891\u4e0b\u8f7d\u5668\u3002 zlibrary : \u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9(\u53ef\u80fd\u9700\u8981\u7ffb\u5899)\u3002 \u8bbe\u8ba1\u5de5\u5177 excalidraw : \u4e00\u6b3e\u624b\u7ed8\u98ce\u683c\u7684\u7ed8\u56fe\u5de5\u5177\uff0c\u975e\u5e38\u9002\u5408\u7ed8\u5236\u8bfe\u7a0b\u62a5\u544a\u6216\u8005PPT\u5185\u7684\u793a\u610f\u56fe\u3002 origamiway : \u624b\u628a\u624b\u6559\u4f60\u600e\u4e48\u6298\u7eb8\u3002 thingiverse : \u56ca\u62ec\u5404\u7c7b 2D/3D \u8bbe\u8ba1\u8d44\u6e90\uff0c\u5176 STL \u6587\u4ef6\u4e0b\u8f7d\u53ef\u76f4\u63a5 3D \u6253\u5370\u3002 iconfont : \u56fd\u5185\u6700\u5927\u7684\u56fe\u6807\u548c\u63d2\u753b\u8d44\u6e90\u5e93\uff0c\u53ef\u7528\u4e8e\u5f00\u53d1\u6216\u7ed8\u5236\u7cfb\u7edf\u67b6\u6784\u56fe\u3002 turbosquid : \u53ef\u4ee5\u8d2d\u4e70\u5404\u5f0f\u5404\u6837\u7684\u6a21\u578b\u3002 \u7f16\u7a0b\u76f8\u5173 sqlfiddle : \u4e00\u4e2a\u7b80\u6613\u7684\u5728\u7ebf SQL Playground\u3002 godbolt : \u975e\u5e38\u65b9\u4fbf\u7684\u7f16\u8bd1\u5668\u63a2\u7d22\u5de5\u5177\u3002\u4f60\u53ef\u4ee5\u5199\u4e00\u6bb5 C/C++ \u4ee3\u7801\uff0c\u9009\u62e9\u4e00\u6b3e\u7f16\u8bd1\u5668\uff0c\u7136\u540e\u4fbf\u53ef\u4ee5\u89c2\u5bdf\u751f\u6210\u7684\u5177\u4f53\u6c47\u7f16\u4ee3\u7801\u3002 explainshell : \u4f60\u662f\u5426\u66fe\u4e3a\u4e00\u6bb5 shell \u4ee3\u7801\u7684\u5177\u4f53\u542b\u4e49\u611f\u5230\u56f0\u6270\uff1fmanpage \u770b\u534a\u5929\u8fd8\u662f\u4e0d\u660e\u6240\u4ee5\uff1f\u8bd5\u8bd5\u8fd9\u4e2a\u7f51\u7ad9\uff01 regex101 : \u6b63\u5219\u8868\u8fbe\u5f0f\u8c03\u8bd5\u7f51\u7ad9\uff0c\u652f\u6301\u5404\u79cd\u7f16\u7a0b\u8bed\u8a00\u7684\u5339\u914d\u6807\u51c6\u3002 typingtom : \u9488\u5bf9\u7a0b\u5e8f\u5458\u7684\u6253\u5b57\u7ec3\u4e60/\u6d4b\u901f\u7f51\u7ad9\u3002 \u5b66\u4e60\u7f51\u7ad9 HFS : \u5404\u7c7b\u8f6f\u4ef6\u6559\u7a0b\u3002 os-wiki : \u64cd\u4f5c\u7cfb\u7edf\u6280\u672f\u8d44\u6e90\u767e\u79d1\u5168\u4e66\u3002 Shadertoy : \u7f16\u5199\u5404\u5f0f\u5404\u6837\u7684 shader\u3002 \u6742\u9879 tophub : \u65b0\u95fb\u70ed\u699c\u5408\u96c6\uff08\u7efc\u5408\u4e86\u77e5\u4e4e\u3001\u5fae\u535a\u3001\u767e\u5ea6\u3001\u5fae\u4fe1\u7b49\uff09\u3002 speedtest : \u5728\u7ebf\u7f51\u7edc\u6d4b\u901f\u7f51\u7ad9\u3002 public-apis : \u516c\u5171 API \u5408\u96c6\u5217\u8868\u3002","title":"\u5b9e\u7528\u5de5\u5177\u7bb1"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_1","text":"","title":"\u5b9e\u7528\u5de5\u5177\u7bb1"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_2","text":"Libgen : PDF\u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9\u3002 z-epub : ePub\u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9\u3002 bitdownloader : \u6cb9\u7ba1\u89c6\u9891\u4e0b\u8f7d\u5668\u3002 zlibrary : \u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9(\u53ef\u80fd\u9700\u8981\u7ffb\u5899)\u3002","title":"\u4e0b\u8f7d\u5de5\u5177"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_3","text":"excalidraw : \u4e00\u6b3e\u624b\u7ed8\u98ce\u683c\u7684\u7ed8\u56fe\u5de5\u5177\uff0c\u975e\u5e38\u9002\u5408\u7ed8\u5236\u8bfe\u7a0b\u62a5\u544a\u6216\u8005PPT\u5185\u7684\u793a\u610f\u56fe\u3002 origamiway : \u624b\u628a\u624b\u6559\u4f60\u600e\u4e48\u6298\u7eb8\u3002 thingiverse : \u56ca\u62ec\u5404\u7c7b 2D/3D \u8bbe\u8ba1\u8d44\u6e90\uff0c\u5176 STL \u6587\u4ef6\u4e0b\u8f7d\u53ef\u76f4\u63a5 3D \u6253\u5370\u3002 iconfont : \u56fd\u5185\u6700\u5927\u7684\u56fe\u6807\u548c\u63d2\u753b\u8d44\u6e90\u5e93\uff0c\u53ef\u7528\u4e8e\u5f00\u53d1\u6216\u7ed8\u5236\u7cfb\u7edf\u67b6\u6784\u56fe\u3002 turbosquid : \u53ef\u4ee5\u8d2d\u4e70\u5404\u5f0f\u5404\u6837\u7684\u6a21\u578b\u3002","title":"\u8bbe\u8ba1\u5de5\u5177"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_4","text":"sqlfiddle : \u4e00\u4e2a\u7b80\u6613\u7684\u5728\u7ebf SQL Playground\u3002 godbolt : \u975e\u5e38\u65b9\u4fbf\u7684\u7f16\u8bd1\u5668\u63a2\u7d22\u5de5\u5177\u3002\u4f60\u53ef\u4ee5\u5199\u4e00\u6bb5 C/C++ \u4ee3\u7801\uff0c\u9009\u62e9\u4e00\u6b3e\u7f16\u8bd1\u5668\uff0c\u7136\u540e\u4fbf\u53ef\u4ee5\u89c2\u5bdf\u751f\u6210\u7684\u5177\u4f53\u6c47\u7f16\u4ee3\u7801\u3002 explainshell : \u4f60\u662f\u5426\u66fe\u4e3a\u4e00\u6bb5 shell \u4ee3\u7801\u7684\u5177\u4f53\u542b\u4e49\u611f\u5230\u56f0\u6270\uff1fmanpage \u770b\u534a\u5929\u8fd8\u662f\u4e0d\u660e\u6240\u4ee5\uff1f\u8bd5\u8bd5\u8fd9\u4e2a\u7f51\u7ad9\uff01 regex101 : \u6b63\u5219\u8868\u8fbe\u5f0f\u8c03\u8bd5\u7f51\u7ad9\uff0c\u652f\u6301\u5404\u79cd\u7f16\u7a0b\u8bed\u8a00\u7684\u5339\u914d\u6807\u51c6\u3002 typingtom : \u9488\u5bf9\u7a0b\u5e8f\u5458\u7684\u6253\u5b57\u7ec3\u4e60/\u6d4b\u901f\u7f51\u7ad9\u3002","title":"\u7f16\u7a0b\u76f8\u5173"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_5","text":"HFS : \u5404\u7c7b\u8f6f\u4ef6\u6559\u7a0b\u3002 os-wiki : \u64cd\u4f5c\u7cfb\u7edf\u6280\u672f\u8d44\u6e90\u767e\u79d1\u5168\u4e66\u3002 Shadertoy : \u7f16\u5199\u5404\u5f0f\u5404\u6837\u7684 shader\u3002","title":"\u5b66\u4e60\u7f51\u7ad9"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_6","text":"tophub : \u65b0\u95fb\u70ed\u699c\u5408\u96c6\uff08\u7efc\u5408\u4e86\u77e5\u4e4e\u3001\u5fae\u535a\u3001\u767e\u5ea6\u3001\u5fae\u4fe1\u7b49\uff09\u3002 speedtest : \u5728\u7ebf\u7f51\u7edc\u6d4b\u901f\u7f51\u7ad9\u3002 public-apis : \u516c\u5171 API \u5408\u96c6\u5217\u8868\u3002","title":"\u6742\u9879"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/%E7%BF%BB%E5%A2%99/","text":"\u7ffb\u5899 \u6b64\u94fe\u63a5 \u51fa\u73b0\u5728\u8fd9\u91cc\u7eaf\u5c5e\u4e8c\u8fdb\u5236 bit \u7684\u968f\u610f\u7ec4\u5408\uff0c\u4e0e\u672c\u4eba\u6beb\u65e0\u5173\u7cfb\u3002","title":"\u7ffb\u5899"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/%E7%BF%BB%E5%A2%99/#_1","text":"\u6b64\u94fe\u63a5 \u51fa\u73b0\u5728\u8fd9\u91cc\u7eaf\u5c5e\u4e8c\u8fdb\u5236 bit \u7684\u968f\u610f\u7ec4\u5408\uff0c\u4e0e\u672c\u4eba\u6beb\u65e0\u5173\u7cfb\u3002","title":"\u7ffb\u5899"},{"location":"en/%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/CS162/","text":"CS162: Operating System \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS61A, CS61B, CS61C \u7f16\u7a0b\u8bed\u8a00\uff1aC, x86\u6c47\u7f16 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a200 \u5c0f\u65f6+\uff0c\u4e0a\u4e0d\u5c01\u9876 \u8fd9\u95e8\u8bfe\u8ba9\u6211\u8bb0\u5fc6\u72b9\u65b0\u7684\u6709\u4e24\u4e2a\u90e8\u5206\uff1a \u9996\u5148\u662f\u6559\u6750\uff0c\u8fd9\u672c\u4e66\u7528\u7684\u6559\u6750 Operating Systems: Principles and Practice (2nd Edition) \u4e00\u5171\u56db\u5377\uff0c\u5199\u5f97\u975e\u5e38\u6df1\u5165\u6d45\u51fa\uff0c\u5f88\u597d\u5730\u5f25\u8865\u4e86 MIT6.S081 \u5728\u7406\u8bba\u77e5\u8bc6\u4e0a\u7684\u4e9b\u8bb8\u7a7a\u767d\uff0c\u975e\u5e38\u5efa\u8bae\u5927\u5bb6\u9605\u8bfb\u3002\u76f8\u5173\u8d44\u6e90\u4f1a\u5206\u4eab\u5728\u672c\u4e66\u7684\u7ecf\u5178\u4e66\u7c4d\u63a8\u8350\u6a21\u5757\u3002 \u5176\u6b21\u662f\u8fd9\u95e8\u8bfe\u7684 Project \u2014\u2014 Pintos\u3002Pintos \u662f\u7531 Ben Pfaff \u7b49\u4eba\u5728 x86 \u5e73\u53f0\u4e0a\u7f16\u5199\u7684\u6559\u5b66\u7528\u64cd\u4f5c\u7cfb\u7edf\uff0cBen Pfaff \u751a\u81f3\u4e13\u95e8\u53d1\u4e86\u7bc7 paper \u6765\u9610\u8ff0 Pintos \u7684\u8bbe\u8ba1\u601d\u60f3\u3002 \u548c MIT \u7684 xv6 \u5c0f\u800c\u7cbe\u7684 lab \u8bbe\u8ba1\u7406\u5ff5\u4e0d\u540c\uff0cPintos \u66f4\u6ce8\u91cd\u7cfb\u7edf\u7684 Design and Implementation\u3002Pintos \u672c\u8eab\u4ec5\u4e00\u4e07\u884c\u5de6\u53f3\uff0c\u53ea\u63d0\u4f9b\u4e86\u64cd\u4f5c\u7cfb\u7edf\u6700\u57fa\u672c\u7684\u529f\u80fd\u3002\u800c 4 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Project\uff0c\u5177\u4f53\u8981\u6c42\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/CS162/#_3","text":"\u7531\u4e8e\u5317\u5927\u7684\u64cd\u7edf\u5b9e\u9a8c\u73ed\u91c7\u7528\u4e86\u8be5\u8bfe\u7a0b\u7684 Project\uff0c\u4e3a\u4e86\u9632\u6b62\u4ee3\u7801\u6284\u88ad\uff0c\u6211\u7684\u4ee3\u7801\u5b9e\u73b0\u6ca1\u6709\u5f00\u6e90\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/MIT6.S081/","text":"MIT 6.S081: Operating System Engineering Descriptions Offered by: MIT Prerequisites: Computer Architecture + Solid C Programming Skills + RISC-V Assembly Programming Languages: C, RISC-V Difficulty\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour\uff1a150 hours This is the undergraduate operating system course at MIT, offered by the well-known PDOS Group. One of the instructors, Robert Morris, was once a famous hacker who created 'Morris', the first worm virus in the world. The predecessor of this course was the famous MIT6.828. The same instructors at MIT created an educational operating system called JOS based on x86, which has been adopted by many other famous universities. While after the birth of RISC-V, they implemented it based on RISC-V, and offered MIT 6.S081. RISC-V is lightweight and user-friendly, so students don't have to struggle with the confusing legacy features in x86 as in JOS, but focus on the operating system design and implementation. The instructors have also written a tutorial , elaborately explaining the ideas of design and details of the implementation of xv6 operating system. The teaching style of this course is also interesting, the instructors guided the students to understand the numerous technical challenges and design principles in the operating systems by going through the xv6 source code, instead of merely teaching theoretical knowledge. Weekly Labs will let you add new features to xv6, which focus on enhancing students' practical skills. There are 11 labs in total during the whole semester which give you the chance to understand every aspect of the operating systems, bringing a great sense of achievement. Each lab has a complete framework for testing, some tests are more than a thousand lines of code, which shows how much effort the instructors have made to teach this course well. In the second half of the course, the instructors will discuss a couple of classic papers in the operating system field, covering file systems, system security, networking, virtualization, and so on, giving you a chance to have a taste of the cutting edge research directions in the academic field. Course Resources Course Website: https://pdos.csail.mit.edu/6.828/2021/schedule.html Lecture Videos\uff1a https://www.youtube.com/watch?v=L6YqHxYHa7A , videos for each lecture can be found on the course website. Translated documentation(Chinese) of Lecture videos: https://mit-public-courses-cn-translatio.gitbook.io/mit6-s081/ Text Book: https://pdos.csail.mit.edu/6.828/2021/xv6/book-riscv-rev2.pdf Assignments: https://pdos.csail.mit.edu/6.828/2021/schedule.html , 11 labs, can be found on the course website. xv6 Resources Detailed Explanation of xv6 xv6 Documentation(Chinese) Complementary Resources All resources used and assignments implemented by @PKUFlyingPig when learning this course are in PKUFlyingPig/MIT6.S081-2020fall - GitHub . @ KuangjuX documented his solutions with detailed explanations and complementary knowledge. Moreover, @ KuangjuX has reimplemented the xv6 operating system in Rust which contains more detailed reviews and discussions about xv6. Some Blogs for References doraemonzzz Xiao Fan (\u6a0a\u6f47) Miigon's blog Zhou Fang Yichun's Blog \u89e3\u6790Ta PKUFlyingPig \u661f\u9065\u89c1","title":"MIT 6.S081: Operating System Engineering"},{"location":"en/%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/MIT6.S081/#mit-6s081-operating-system-engineering","text":"","title":"MIT 6.S081: Operating System Engineering"},{"location":"en/%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/MIT6.S081/#descriptions","text":"Offered by: MIT Prerequisites: Computer Architecture + Solid C Programming Skills + RISC-V Assembly Programming Languages: C, RISC-V Difficulty\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour\uff1a150 hours This is the undergraduate operating system course at MIT, offered by the well-known PDOS Group. One of the instructors, Robert Morris, was once a famous hacker who created 'Morris', the first worm virus in the world. The predecessor of this course was the famous MIT6.828. The same instructors at MIT created an educational operating system called JOS based on x86, which has been adopted by many other famous universities. While after the birth of RISC-V, they implemented it based on RISC-V, and offered MIT 6.S081. RISC-V is lightweight and user-friendly, so students don't have to struggle with the confusing legacy features in x86 as in JOS, but focus on the operating system design and implementation. The instructors have also written a tutorial , elaborately explaining the ideas of design and details of the implementation of xv6 operating system. The teaching style of this course is also interesting, the instructors guided the students to understand the numerous technical challenges and design principles in the operating systems by going through the xv6 source code, instead of merely teaching theoretical knowledge. Weekly Labs will let you add new features to xv6, which focus on enhancing students' practical skills. There are 11 labs in total during the whole semester which give you the chance to understand every aspect of the operating systems, bringing a great sense of achievement. Each lab has a complete framework for testing, some tests are more than a thousand lines of code, which shows how much effort the instructors have made to teach this course well. In the second half of the course, the instructors will discuss a couple of classic papers in the operating system field, covering file systems, system security, networking, virtualization, and so on, giving you a chance to have a taste of the cutting edge research directions in the academic field.","title":"Descriptions"},{"location":"en/%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/MIT6.S081/#course-resources","text":"Course Website: https://pdos.csail.mit.edu/6.828/2021/schedule.html Lecture Videos\uff1a https://www.youtube.com/watch?v=L6YqHxYHa7A , videos for each lecture can be found on the course website. Translated documentation(Chinese) of Lecture videos: https://mit-public-courses-cn-translatio.gitbook.io/mit6-s081/ Text Book: https://pdos.csail.mit.edu/6.828/2021/xv6/book-riscv-rev2.pdf Assignments: https://pdos.csail.mit.edu/6.828/2021/schedule.html , 11 labs, can be found on the course website.","title":"Course Resources"},{"location":"en/%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/MIT6.S081/#xv6-resources","text":"Detailed Explanation of xv6 xv6 Documentation(Chinese)","title":"xv6 Resources"},{"location":"en/%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/MIT6.S081/#complementary-resources","text":"All resources used and assignments implemented by @PKUFlyingPig when learning this course are in PKUFlyingPig/MIT6.S081-2020fall - GitHub . @ KuangjuX documented his solutions with detailed explanations and complementary knowledge. Moreover, @ KuangjuX has reimplemented the xv6 operating system in Rust which contains more detailed reviews and discussions about xv6.","title":"Complementary Resources"},{"location":"en/%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/MIT6.S081/#some-blogs-for-references","text":"doraemonzzz Xiao Fan (\u6a0a\u6f47) Miigon's blog Zhou Fang Yichun's Blog \u89e3\u6790Ta PKUFlyingPig \u661f\u9065\u89c1","title":"Some Blogs for References"},{"location":"en/%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/NJUOS/","text":"NJU OS: Operating System Design and Implementation \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1a\u5357\u4eac\u5927\u5b66 \u5148\u4fee\u8981\u6c42\uff1a\u4f53\u7cfb\u7ed3\u6784 + \u624e\u5b9e\u7684 C \u8bed\u8a00\u529f\u5e95 \u7f16\u7a0b\u8bed\u8a00\uff1aC \u8bed\u8a00 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 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\u90fd\u80fd\u975e\u5e38\u65b9\u4fbf\u5730\u8fdb\u884c\u672c\u5730\u6d4b\u8bd5\uff0c\u5c31\u7b97\u6ca1\u6709\u8bc4\u6d4b\u673a\u4e5f\u4e0d\u5f71\u54cd\u81ea\u5b66\uff0c\u56e0\u6b64\u5e0c\u671b\u5927\u5bb6\u4e0d\u8981\u805a\u4f17\u201c\u9a9a\u6270\u201d\u8001\u5e08\u4ee5\u56fe\u8e6d\u8bfe\u3002 \u6700\u540e\u518d\u6b21\u611f\u8c22\u848b\u8001\u5e08\u8bbe\u8ba1\u5e76\u5f00\u653e\u4e86\u8fd9\u6837\u4e00\u95e8\u975e\u5e38\u68d2\u7684\u64cd\u4f5c\u7cfb\u7edf\u8bfe\u7a0b\uff0c\u8fd9\u4e5f\u662f\u672c\u4e66\u6536\u5f55\u7684\u7b2c\u4e00\u95e8\u56fd\u5185\u9ad8\u6821\u81ea\u4e3b\u5f00\u8bbe\u7684\u8ba1\u7b97\u673a\u8bfe\u7a0b\u3002\u6b63\u662f\u6709\u848b\u8001\u5e08\u8fd9\u4e9b\u5e74\u8f7b\u7684\u65b0\u751f\u4ee3\u6559\u5e08\u5728\u7e41\u91cd\u7684 Tenure \u8003\u6838\u4e4b\u4f59\u7684\u7528\u7231\u53d1\u7535\uff0c\u624d\u8ba9\u65e0\u6570\u5b66\u5b50\u6536\u83b7\u4e86\u96be\u5fd8\u7684\u672c\u79d1\u751f\u6daf\u3002\u4e5f\u671f\u5f85\u56fd\u5185\u80fd\u6709\u66f4\u591a\u8fd9\u6837\u7684\u826f\u5fc3\u597d\u8bfe\uff0c\u6211\u4e5f\u4f1a\u7b2c\u4e00\u65f6\u95f4\u6536\u5f55\u8fdb\u672c\u4e66\u4e2d\u8ba9\u66f4\u591a\u4eba\u53d7\u76ca\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://jyywiki.cn/OS/2022/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://space.bilibili.com/202224425/channel/collectiondetail?sid=192498 \u8bfe\u7a0b\u6559\u6750\uff1a http://pages.cs.wisc.edu/~remzi/OSTEP/ \u8bfe\u7a0b\u4f5c\u4e1a\uff1a http://jyywiki.cn/OS/2022/ \u8d44\u6e90\u6c47\u603b \u6309\u848b\u8001\u5e08\u7684\u8981\u6c42\uff0c\u6211\u7684\u4f5c\u4e1a\u5b9e\u73b0\u6ca1\u6709\u5f00\u6e90\u3002","title":"NJU OS: Operating System Design and Implementation"},{"location":"en/%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/NJUOS/#nju-os-operating-system-design-and-implementation","text":"","title":"NJU OS: Operating System Design and Implementation"},{"location":"en/%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/NJUOS/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1a\u5357\u4eac\u5927\u5b66 \u5148\u4fee\u8981\u6c42\uff1a\u4f53\u7cfb\u7ed3\u6784 + \u624e\u5b9e\u7684 C \u8bed\u8a00\u529f\u5e95 \u7f16\u7a0b\u8bed\u8a00\uff1aC \u8bed\u8a00 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 \u4e4b\u524d\u4e00\u76f4\u542c\u8bf4\u5357\u5927\u7684\u848b\u708e\u5ca9\u8001\u5e08\u5f00\u8bbe\u7684\u64cd\u4f5c\u7cfb\u7edf\u8bfe\u7a0b\u8bb2\u5f97\u5f88\u597d\uff0c\u4e45\u95fb\u4e0d\u5982\u4e00\u89c1\uff0c\u8fd9\u5b66\u671f\u6709\u5e78\u5728 B 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\u8003\u6838\u4e4b\u4f59\u7684\u7528\u7231\u53d1\u7535\uff0c\u624d\u8ba9\u65e0\u6570\u5b66\u5b50\u6536\u83b7\u4e86\u96be\u5fd8\u7684\u672c\u79d1\u751f\u6daf\u3002\u4e5f\u671f\u5f85\u56fd\u5185\u80fd\u6709\u66f4\u591a\u8fd9\u6837\u7684\u826f\u5fc3\u597d\u8bfe\uff0c\u6211\u4e5f\u4f1a\u7b2c\u4e00\u65f6\u95f4\u6536\u5f55\u8fdb\u672c\u4e66\u4e2d\u8ba9\u66f4\u591a\u4eba\u53d7\u76ca\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/NJUOS/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://jyywiki.cn/OS/2022/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://space.bilibili.com/202224425/channel/collectiondetail?sid=192498 \u8bfe\u7a0b\u6559\u6750\uff1a http://pages.cs.wisc.edu/~remzi/OSTEP/ \u8bfe\u7a0b\u4f5c\u4e1a\uff1a http://jyywiki.cn/OS/2022/","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/NJUOS/#_3","text":"\u6309\u848b\u8001\u5e08\u7684\u8981\u6c42\uff0c\u6211\u7684\u4f5c\u4e1a\u5b9e\u73b0\u6ca1\u6709\u5f00\u6e90\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITLA/","text":"MIT18.06: Linear Algebra Descriptions Offered by: MIT Prerequisites: English Programming languages: None Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: Varying from person to person Gilbert Strang, a great mathematician at MIT, still insists on teaching in his eighties. His classic text book Introduction to Linear Algebra has been adopted as an official textbook by Tsinghua University. After reading the PDF version, I felt deeply guilty and spent more than 200 yuan to purchase a genuine version in English as collection. The cover of this book is attached below. If you can fully understand the mathematical meaning of the cover picture, then your understanding of linear algebra will definitely reach a new height. In addition to the course materials, the famous Youtuber 3Blue1Brown 's video series The Essence of Linear Algebra are also great learning resources. Resources Course Website: https://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/syllabus/ Recordings: refer to the course website Textbook: Introduction to Linear Algebra, Gilbert Strang Assignments: refer to the course website","title":"MIT18.06: Linear Algebra"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITLA/#mit1806-linear-algebra","text":"","title":"MIT18.06: Linear Algebra"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITLA/#descriptions","text":"Offered by: MIT Prerequisites: English Programming languages: None Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: Varying from person to person Gilbert Strang, a great mathematician at MIT, still insists on teaching in his eighties. His classic text book Introduction to Linear Algebra has been adopted as an official textbook by Tsinghua University. After reading the PDF version, I felt deeply guilty and spent more than 200 yuan to purchase a genuine version in English as collection. The cover of this book is attached below. If you can fully understand the mathematical meaning of the cover picture, then your understanding of linear algebra will definitely reach a new height. In addition to the course materials, the famous Youtuber 3Blue1Brown 's video series The Essence of Linear Algebra are also great learning resources.","title":"Descriptions"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITLA/#resources","text":"Course Website: https://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/syllabus/ Recordings: refer to the course website Textbook: Introduction to Linear Algebra, Gilbert Strang Assignments: refer to the course website","title":"Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITmaths/","text":"MIT Calculus Course Descriptions Offered by: MIT Prerequisites: English Programming Languages: None Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: Varying from person to person The calculus course at MIT consists of MIT18.01: Single Variable Calculus and MIT18.02: Multivariable Calculus. If you are confident in your math, you can just read the course notes, which are written in a very simple and vivid way, so that you will not be tired of doing homework but can really see the essence of calculus. In addition to the course materials, the famous Youtuber 3Blue1Brown 's video series The Essence of Calculus are also great learning resources. Course Resources Course Website: 18.01 , 18.02 Recordings: refer to course website Textbook: refer to course website Assignments: refer to course website","title":"MIT18.01/18.02: Calculus"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITmaths/#mit-calculus-course","text":"","title":"MIT Calculus Course"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITmaths/#descriptions","text":"Offered by: MIT Prerequisites: English Programming Languages: None Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: Varying from person to person The calculus course at MIT consists of MIT18.01: Single Variable Calculus and MIT18.02: Multivariable Calculus. If you are confident in your math, you can just read the course notes, which are written in a very simple and vivid way, so that you will not be tired of doing homework but can really see the essence of calculus. In addition to the course materials, the famous Youtuber 3Blue1Brown 's video series The Essence of Calculus are also great learning resources.","title":"Descriptions"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITmaths/#course-resources","text":"Course Website: 18.01 , 18.02 Recordings: refer to course website Textbook: refer to course website Assignments: refer to course website","title":"Course Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/information/","text":"MIT6.050J: Information theory and Entropy Descriptions Offered by: MIT Prerequisites: None Programming Languages: None Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 100 hours This is MIT's introductory information theory course for freshmen, Professor Penfield has written a special textbook for this course as course notes, which is in-depth and interesting. Course Resources Course Website: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/index.htm Textbook: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/syllabus/MIT6_050JS08_textbook.pdf Assignments: see the course website for details, including written assignments and Matlab programming assignments.","title":"MIT6.050J: Information theory and Entropy"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/information/#mit6050j-information-theory-and-entropy","text":"","title":"MIT6.050J: Information theory and Entropy"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/information/#descriptions","text":"Offered by: MIT Prerequisites: None Programming Languages: None Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 100 hours This is MIT's introductory information theory course for freshmen, Professor Penfield has written a special textbook for this course as course notes, which is in-depth and interesting.","title":"Descriptions"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/information/#course-resources","text":"Course Website: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/index.htm Textbook: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/syllabus/MIT6_050JS08_textbook.pdf Assignments: see the course website for details, including written assignments and Matlab programming assignments.","title":"Course Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/6.042J/","text":"MIT 6.042J: Mathematics for Computer Science Descriptions Offered by: MIT Prerequisites: Calculus, Linear Algebra Programming Languages: Python preferred Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 50-70 hours This is MIT\u2018s discrete mathematics and probability course taught by the notable Tom Leighton (co-founder of Akamai). It is very useful for learning algorithms subsequently. Course Resources Course Website: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/ Recordings: https://www.youtube.com/playlist?list=PLB7540DEDD482705B Assignments: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/assignments/","title":"MIT 6.042J: Mathematics for Computer Science"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/6.042J/#mit-6042j-mathematics-for-computer-science","text":"","title":"MIT 6.042J: Mathematics for Computer Science"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/6.042J/#descriptions","text":"Offered by: MIT Prerequisites: Calculus, Linear Algebra Programming Languages: Python preferred Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 50-70 hours This is MIT\u2018s discrete mathematics and probability course taught by the notable Tom Leighton (co-founder of Akamai). It is very useful for learning algorithms subsequently.","title":"Descriptions"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/6.042J/#course-resources","text":"Course Website: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/ Recordings: https://www.youtube.com/playlist?list=PLB7540DEDD482705B Assignments: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/assignments/","title":"Course Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS126/","text":"UCB CS126 : Probability theory Descriptions Offered by: UC Berkeley Prerequisites: CS70, Calculus, Linear Algebra Programming Languages: Python Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 100 hours This is Berkeley's advanced probability course, which involves relatively advanced theoretical content such as statistics and stochastic processes, so a solid mathematical foundation is required. But as long as you stick with it you will certainly take your mastery of probability theory to a new level. The course is designed by Professor Jean Walrand, who has written an accompanying textbook, Probability in Electrical Engineering and Computer Science , in which each chapter uses a specific algorithm as a practical example to demonstrate the application of theory in practice. Such as PageRank, Route Planing, Speech Recognition, etc. The book is open source and can be downloaded as a free PDF or Epub version. Jean Walrand has also created accompanying Python implementations of the examples throughout the book, which are published online as Jupyter Notebook that readers can modify, debug and run them online interactively. In addition to the Homework, nine Labs will allow you to use probability theory to solve practical problems in Python. Course Resources Course Website: https://inst.eecs.berkeley.edu/~ee126/fa20/content.html Textbook: PDF , Epub , Jupyter Notebook Assignments: refer to the course website. Personal Resources All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/EECS126 - GitHub","title":"UCB CS126: probability theory"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS126/#ucb-cs126-probability-theory","text":"","title":"UCB CS126 : Probability theory"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS126/#descriptions","text":"Offered by: UC Berkeley Prerequisites: CS70, Calculus, Linear Algebra Programming Languages: Python Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 100 hours This is Berkeley's advanced probability course, which involves relatively advanced theoretical content such as statistics and stochastic processes, so a solid mathematical foundation is required. But as long as you stick with it you will certainly take your mastery of probability theory to a new level. The course is designed by Professor Jean Walrand, who has written an accompanying textbook, Probability in Electrical Engineering and Computer Science , in which each chapter uses a specific algorithm as a practical example to demonstrate the application of theory in practice. Such as PageRank, Route Planing, Speech Recognition, etc. The book is open source and can be downloaded as a free PDF or Epub version. Jean Walrand has also created accompanying Python implementations of the examples throughout the book, which are published online as Jupyter Notebook that readers can modify, debug and run them online interactively. In addition to the Homework, nine Labs will allow you to use probability theory to solve practical problems in Python.","title":"Descriptions"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS126/#course-resources","text":"Course Website: https://inst.eecs.berkeley.edu/~ee126/fa20/content.html Textbook: PDF , Epub , Jupyter Notebook Assignments: refer to the course website.","title":"Course Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS126/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/EECS126 - GitHub","title":"Personal Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS70/","text":"UCB CS70: Discrete Math and Probability Theory Descriptions Offered by: UC Berkeley Prerequisites: None Programming Languages: None Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 60 hours This is Berkeley's introductory discrete mathematics course. The biggest highlight of this course is that it not only teaches you theoretical knowledge, but also introduce the applications of theoretical knowledge in practical algorithms in each module. In this way, students majoring in CS can understand the essence of theoretical knowledge and use it in practice rather than struggle with cold formal mathematical symbols. Specific theory-algorithm correspondences are listed below. Logic proof: stable matching algorithm Graph theory: network topology design Basic number theory: RSA algorithm Polynomial ring: error-correcting code design Probability theory: Hash table design, load balancing, etc. The course notes are also written in a very in-depth manner, with derivations of formulas and practical examples, providing a good reading experience. Course Resources Course Website: http://www.eecs70.org/ Textbook: refer to the course website Assignments: refer to the course website Personal Resources All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/UCB-CS70 - GitHub","title":"UCB CS70: discrete Math and probability theory"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS70/#ucb-cs70-discrete-math-and-probability-theory","text":"","title":"UCB CS70: Discrete Math and Probability Theory"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS70/#descriptions","text":"Offered by: UC Berkeley Prerequisites: None Programming Languages: None Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 60 hours This is Berkeley's introductory discrete mathematics course. The biggest highlight of this course is that it not only teaches you theoretical knowledge, but also introduce the applications of theoretical knowledge in practical algorithms in each module. In this way, students majoring in CS can understand the essence of theoretical knowledge and use it in practice rather than struggle with cold formal mathematical symbols. Specific theory-algorithm correspondences are listed below. Logic proof: stable matching algorithm Graph theory: network topology design Basic number theory: RSA algorithm Polynomial ring: error-correcting code design Probability theory: Hash table design, load balancing, etc. The course notes are also written in a very in-depth manner, with derivations of formulas and practical examples, providing a good reading experience.","title":"Descriptions"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS70/#course-resources","text":"Course Website: http://www.eecs70.org/ Textbook: refer to the course website Assignments: refer to the course website","title":"Course Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS70/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/UCB-CS70 - GitHub","title":"Personal Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/The_Information_Theory_Pattern_Recognition_and_Neural_Networks/","text":"The Information Theory, Patter Recognition, and Neural Networks Descriptions Offered by: Cambridge Prerequisites: Calculus, Linear Algebra, Probabilities and Statistics Programming Languages: Anything would be OK, Python preferred Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 30-50 hours This is a course on information theory taught by Sir David MacKay at the University of Cambridge. The professor is a very famous scholar in information theory and neural networks, and the textbook for the course is a classic work in the field of information theory. Unfortunately, those whom God loves die young ... Course Resources Course Website: http://www.inference.org.uk/mackay/itila/ Recordings: https://www.youtube.com/playlist?list=PLruBu5BI5n4aFpG32iMbdWoRVAA-Vcso6 Textbooks: Information Theory, Inference, and Learning Algorithms Assignments: At the end of each lesson video, there are post-lesson exercises from the textbook R.I.P Prof. David MacKay","title":"The Information Theory, Patter Recognition, and Neural Networks"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/The_Information_Theory_Pattern_Recognition_and_Neural_Networks/#the-information-theory-patter-recognition-and-neural-networks","text":"","title":"The Information Theory, Patter Recognition, and Neural Networks"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/The_Information_Theory_Pattern_Recognition_and_Neural_Networks/#descriptions","text":"Offered by: Cambridge Prerequisites: Calculus, Linear Algebra, Probabilities and Statistics Programming Languages: Anything would be OK, Python preferred Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 30-50 hours This is a course on information theory taught by Sir David MacKay at the University of Cambridge. The professor is a very famous scholar in information theory and neural networks, and the textbook for the course is a classic work in the field of information theory. Unfortunately, those whom God loves die young ...","title":"Descriptions"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/The_Information_Theory_Pattern_Recognition_and_Neural_Networks/#course-resources","text":"Course Website: http://www.inference.org.uk/mackay/itila/ Recordings: https://www.youtube.com/playlist?list=PLruBu5BI5n4aFpG32iMbdWoRVAA-Vcso6 Textbooks: Information Theory, Inference, and Learning Algorithms Assignments: At the end of each lesson video, there are post-lesson exercises from the textbook","title":"Course Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/The_Information_Theory_Pattern_Recognition_and_Neural_Networks/#rip-prof-david-mackay","text":"","title":"R.I.P Prof. David MacKay"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/convex/","text":"Stanford EE364A: Convex Optimization Descriptions Offered by: Stanford Prerequisites: Python, Calculus, Linear Algebra, Probability Theory, Numerical Analysis Programming Languages: Python Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 150 hours Professor Stephen Boyd is a great expert in the field of convex optimization and his textbook Convex Optimization has been adopted by many prestigious universities. His team has also developed a programming framework for solving common convex optimization problems in Python, Julia, and other popular programming languages, and its homework assignments also use this programming framework to solve real-life convex optimization problems. In practice, you will deeply understand that for the same problem, a small change in the modeling process can make a world of difference in the difficulty of solving the equation. It is an art to make the equations you formulate \"convex\". Course Resources Course Website: http://stanford.edu/class/ee364a/index.html Recordings: https://www.youtube.com/watch?v=VNON98dKjno&list=PLoCMsyE1cvdXeoqd1hGaMBsCAQQ6otUtO Textbook: Convex Optimization Assignments: refer to the course website Personal Resources All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPic/Standford_CVX101 - GitHub","title":"Standford EE364A: Convex Optimization"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/convex/#stanford-ee364a-convex-optimization","text":"","title":"Stanford EE364A: Convex Optimization"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/convex/#descriptions","text":"Offered by: Stanford Prerequisites: Python, Calculus, Linear Algebra, Probability Theory, Numerical Analysis Programming Languages: Python Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 150 hours Professor Stephen Boyd is a great expert in the field of convex optimization and his textbook Convex Optimization has been adopted by many prestigious universities. His team has also developed a programming framework for solving common convex optimization problems in Python, Julia, and other popular programming languages, and its homework assignments also use this programming framework to solve real-life convex optimization problems. In practice, you will deeply understand that for the same problem, a small change in the modeling process can make a world of difference in the difficulty of solving the equation. It is an art to make the equations you formulate \"convex\".","title":"Descriptions"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/convex/#course-resources","text":"Course Website: http://stanford.edu/class/ee364a/index.html Recordings: https://www.youtube.com/watch?v=VNON98dKjno&list=PLoCMsyE1cvdXeoqd1hGaMBsCAQQ6otUtO Textbook: Convex Optimization Assignments: refer to the course website","title":"Course Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/convex/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPic/Standford_CVX101 - GitHub","title":"Personal Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/numerical/","text":"MIT18.330 : Introduction to numerical analysis Descriptions Offered by: MIT Prerequisites:Calculus, Linear Algebra, Probability theory Programming Languages: Julia Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 150 hours While the computational power of computers has been helping people to push boundaries of science, there is a natural barrier between the discrete nature of computers and this continuous world, and how to use discrete representations to estimate and approximate those mathematically continuous concepts is an important theme in numerical analysis. This course will explore various numerical analysis methods in the areas of floating-point representation, equation solving, linear algebra, calculus, and differential equations, allowing you to understand (1) how to design estimation (2) how to estimate errors (3) how to implement algorithms in Julia. There are also plenty of programming assignments to practice these ideas. The designers of this course have also written an open source textbook for this course (see the link below) with plenty of Julia examples. Course Resources Course Website: https://github.com/mitmath/18330 Textbook: https://fncbook.github.io/fnc/frontmatter.html Assignments: 10 problem sets Personal Resources All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPic/MIT18.330 - GitHub","title":"MIT18.330: Introduction to numerical analysis"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/numerical/#mit18330-introduction-to-numerical-analysis","text":"","title":"MIT18.330 : Introduction to numerical analysis"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/numerical/#descriptions","text":"Offered by: MIT Prerequisites:Calculus, Linear Algebra, Probability theory Programming Languages: Julia Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 150 hours While the computational power of computers has been helping people to push boundaries of science, there is a natural barrier between the discrete nature of computers and this continuous world, and how to use discrete representations to estimate and approximate those mathematically continuous concepts is an important theme in numerical analysis. This course will explore various numerical analysis methods in the areas of floating-point representation, equation solving, linear algebra, calculus, and differential equations, allowing you to understand (1) how to design estimation (2) how to estimate errors (3) how to implement algorithms in Julia. There are also plenty of programming assignments to practice these ideas. The designers of this course have also written an open source textbook for this course (see the link below) with plenty of Julia examples.","title":"Descriptions"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/numerical/#course-resources","text":"Course Website: https://github.com/mitmath/18330 Textbook: https://fncbook.github.io/fnc/frontmatter.html Assignments: 10 problem sets","title":"Course Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/numerical/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPic/MIT18.330 - GitHub","title":"Personal Resources"},{"location":"en/%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/15445/","text":"CMU 15-445: Database Systems \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aCMU \u5148\u4fee\u8981\u6c42\uff1aC++\uff0c\u6570\u636e\u7ed3\u6784\u4e0e\u7b97\u6cd5 \u7f16\u7a0b\u8bed\u8a00\uff1aC++ 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\u6839\u636e\u8c13\u8bcd\u66f4\u65b0\u8ba1\u5212\u8282\u70b9\u8f93\u51fa\u7684\u5143\u7ec4\u7edf\u8ba1\u4fe1\u606f\u3002 \u5269\u4f59 Assignment \u548c Challenges \u53ef\u4ee5\u67e5\u770b\u8bfe\u7a0b\u4ecb\u7ecd\uff0c\u63a8\u8350\u4f7f\u7528 IDEA \u6253\u5f00\u5de5\u7a0b\uff0cMaven \u6784\u5efa\uff0c\u6ce8\u610f\u65e5\u5fd7\u76f8\u5173\u914d\u7f6e\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://courses.cms.caltech.edu/cs122/ \u8bfe\u7a0b\u4ee3\u7801\uff1a https://gitlab.caltech.edu/cs122-19wi \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a7 Assignments + 2 Challenges","title":"Caltech CS122: Database System Implementation"},{"location":"en/%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/CS122/#caltech-cs-122-database-system-implementation","text":"","title":"Caltech CS 122: Database System Implementation"},{"location":"en/%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/CS122/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aCaltech 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\u53ef\u4ee5\u67e5\u770b\u8bfe\u7a0b\u4ecb\u7ecd\uff0c\u63a8\u8350\u4f7f\u7528 IDEA \u6253\u5f00\u5de5\u7a0b\uff0cMaven \u6784\u5efa\uff0c\u6ce8\u610f\u65e5\u5fd7\u76f8\u5173\u914d\u7f6e\u3002","title":"Assignment3"},{"location":"en/%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/CS122/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://courses.cms.caltech.edu/cs122/ \u8bfe\u7a0b\u4ee3\u7801\uff1a https://gitlab.caltech.edu/cs122-19wi \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a7 Assignments + 2 Challenges","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/CS186/","text":"UCB CS186: Introduction to Database System Descriptions Offered by: UC Berkeley Prerequisites: CS61A, CS61B, CS61C Programming Languages: Java Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 150 hours How to write SQL queries? How are SQL commands disassembled, optimized, and transformed into on-disk query commands step by step? How to implement a high-concurrency database? How to implement database failure recovery? What is NoSQL? This course elaborates on the internal details of relational databases. Besides the theoretical knowledge, you will use Java to implement a real relational database that supports SQL concurrent query, B+ tree index, and failure recovery. From a practical point of view, you will have the opportunity to write SQL queries and NoSQL queries in course projects, which is very helpful for building full-stack projects. Course Resources Course Website: https://cs186berkeley.net/ Recordings: https://www.youtube.com/playlist?list=PLYp4IGUhNFmw8USiYMJvCUjZe79fvyYge Assignments: https://cs186.gitbook.io/project/ Personal Resources All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/CS186 - GitHub .","title":"UCB CS186: Introduction to Database System"},{"location":"en/%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/CS186/#ucb-cs186-introduction-to-database-system","text":"","title":"UCB CS186: Introduction to Database System"},{"location":"en/%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/CS186/#descriptions","text":"Offered by: UC Berkeley Prerequisites: CS61A, CS61B, CS61C Programming Languages: Java Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 150 hours How to write SQL queries? How are SQL commands disassembled, optimized, and transformed into on-disk query commands step by step? How to implement a high-concurrency database? How to implement database failure recovery? What is NoSQL? This course elaborates on the internal details of relational databases. Besides the theoretical knowledge, you will use Java to implement a real relational database that supports SQL concurrent query, B+ tree index, and failure recovery. From a practical point of view, you will have the opportunity to write SQL queries and NoSQL queries in course projects, which is very helpful for building full-stack projects.","title":"Descriptions"},{"location":"en/%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/CS186/#course-resources","text":"Course Website: https://cs186berkeley.net/ Recordings: https://www.youtube.com/playlist?list=PLYp4IGUhNFmw8USiYMJvCUjZe79fvyYge Assignments: https://cs186.gitbook.io/project/","title":"Course Resources"},{"location":"en/%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/CS186/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/CS186 - GitHub .","title":"Personal Resources"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6/Data100/","text":"UCB Data100: Principles and Techniques of Data Science Description Offered by: UC Berkeley Prerequisites: CS61A\uff0cLinear Algebra Programming Languages: Python Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 80 hours This is Berkeley's introductory course in data science, covering the basics of data cleaning, feature extraction, data visualization, machine learning and inference, as well as common data science tools such as Pandas, Numpy, and Matplotlib. The course is also rich in interesting programming assignments, which is one of the highlights of the course. Resources Course Website: https://ds100.org/fa21/ Records: refer to the course website Textbook: https://www.textbook.ds100.org/intro.html Assignments: refer to the course website","title":"UCB Data100: Principles and Techniques of Data Science"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6/Data100/#ucb-data100-principles-and-techniques-of-data-science","text":"","title":"UCB Data100: Principles and Techniques of Data Science"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6/Data100/#description","text":"Offered by: UC Berkeley Prerequisites: CS61A\uff0cLinear Algebra Programming Languages: Python Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 80 hours This is Berkeley's introductory course in data science, covering the basics of data cleaning, feature extraction, data visualization, machine learning and inference, as well as common data science tools such as Pandas, Numpy, and Matplotlib. The course is also rich in interesting programming assignments, which is one of the highlights of the course.","title":"Description"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6/Data100/#resources","text":"Course Website: https://ds100.org/fa21/ Records: refer to the course website Textbook: https://www.textbook.ds100.org/intro.html Assignments: refer to the course website","title":"Resources"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/Algo/","text":"Coursera: Algorithms I & II Descriptions Offered by: Princeton Prerequisites: CS61A Programming Languages: Java Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 60 hours This is the highest rated algorithms course on Coursera , and Robert Sedgewick has the magic to make even the most complex algorithms incredibly easy to understand. To be honest, the KMP and network flow algorithms that I have been struggling with for years were made clear to me in this course, and I can even write derivations and proofs for both of them two years later. Do you feel that you forget the algorithms quickly after learning them? I think the key to fully grasping an algorithm lies in understanding the three points as follows: Why should do this? (Correctness derivation, or the essence of the entire algorithm.) How to implement it? (Talk is cheap. Show me the code.) How to use it to solve practical problems? (Bridge the gap between theory and real life.) The composition of this course covers the three points above very well. Watching the course videos and reading the professor's textbook will help you understand the essence of the algorithm and allow you to tell others why the algorithm should look like this in very simple and vivid terms. After understanding the algorithms, you can read the professor's code implementation of all the data structures and algorithms taught in the course. Note that these codes are not demos, but production-ready, time-efficient implementations. They have extensive annotations and comments, and the modularization is also quite good. I learned a lot by just reading the codes. Finally, the most exciting part of the course is the 10 high-quality projects, all with real-world backgrounds, rich test cases, and an automated scoring system (code style is also a part of the scoring). You'll get a taste of algorithms in real life. Course Resources Course Website: Algorithm I , Algorithm II Recordings: Coursera: Algorithm I , Coursera: lgorithm II , CUvids: Algorithms, 4th Edition Textbooks: Algorithms, 4th Edition Assignments: 10 Projects, the course website has specific requirements Personal Resources All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/Princeton-Algorithm - GitHub .","title":"Coursera: Algorithms I & II"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/Algo/#coursera-algorithms-i-ii","text":"","title":"Coursera: Algorithms I & II"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/Algo/#descriptions","text":"Offered by: Princeton Prerequisites: CS61A Programming Languages: Java Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 60 hours This is the highest rated algorithms course on Coursera , and Robert Sedgewick has the magic to make even the most complex algorithms incredibly easy to understand. To be honest, the KMP and network flow algorithms that I have been struggling with for years were made clear to me in this course, and I can even write derivations and proofs for both of them two years later. Do you feel that you forget the algorithms quickly after learning them? I think the key to fully grasping an algorithm lies in understanding the three points as follows: Why should do this? (Correctness derivation, or the essence of the entire algorithm.) How to implement it? (Talk is cheap. Show me the code.) How to use it to solve practical problems? (Bridge the gap between theory and real life.) The composition of this course covers the three points above very well. Watching the course videos and reading the professor's textbook will help you understand the essence of the algorithm and allow you to tell others why the algorithm should look like this in very simple and vivid terms. After understanding the algorithms, you can read the professor's code implementation of all the data structures and algorithms taught in the course. Note that these codes are not demos, but production-ready, time-efficient implementations. They have extensive annotations and comments, and the modularization is also quite good. I learned a lot by just reading the codes. Finally, the most exciting part of the course is the 10 high-quality projects, all with real-world backgrounds, rich test cases, and an automated scoring system (code style is also a part of the scoring). You'll get a taste of algorithms in real life.","title":"Descriptions"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/Algo/#course-resources","text":"Course Website: Algorithm I , Algorithm II Recordings: Coursera: Algorithm I , Coursera: lgorithm II , CUvids: Algorithms, 4th Edition Textbooks: Algorithms, 4th Edition Assignments: 10 Projects, the course website has specific requirements","title":"Course Resources"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/Algo/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/Princeton-Algorithm - GitHub .","title":"Personal Resources"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS170/","text":"CS170: Efficient Algorithms and Intractable Problems Descriptions Offered by: UC Berkeley Prerequisites: CS61B, CS70 Programming Languages: LaTeX Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 60 hours This is Berkeley's algorithm design and analysis course. It focuses on the theoretical foundations and complexity analysis of algorithms, covering Divide-and-Conquer, Graph Algorithms, Shortest Paths, Spanning Trees, Greedy Algorithms, Dynamic programming, Union Finds, Linear Programming, Network Flows, NP-Completeness, Randomized Algorithms, Hashing, etc. The textbook for this course is well written and very suitable as a reference book. In addition, this class has written assignments and is recommended to use LaTeX. You can take this opportunity to practice your LaTeX skills. Course Resources Course Website: https://cs170.org/ Recordings: https://www.youtube.com/playlist?list=PLnocShPlK-Ft-o7NInBDw18be86dNaxlT Recordings: refer to the course website Assignments: refer to the course website Personal Resources All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/UCB-CS170 - GitHub","title":"UCB CS170: Efficient Algorithms and Intractable Problems"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS170/#cs170-efficient-algorithms-and-intractable-problems","text":"","title":"CS170: Efficient Algorithms and Intractable Problems"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS170/#descriptions","text":"Offered by: UC Berkeley Prerequisites: CS61B, CS70 Programming Languages: LaTeX Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 60 hours This is Berkeley's algorithm design and analysis course. It focuses on the theoretical foundations and complexity analysis of algorithms, covering Divide-and-Conquer, Graph Algorithms, Shortest Paths, Spanning Trees, Greedy Algorithms, Dynamic programming, Union Finds, Linear Programming, Network Flows, NP-Completeness, Randomized Algorithms, Hashing, etc. The textbook for this course is well written and very suitable as a reference book. In addition, this class has written assignments and is recommended to use LaTeX. You can take this opportunity to practice your LaTeX skills.","title":"Descriptions"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS170/#course-resources","text":"Course Website: https://cs170.org/ Recordings: https://www.youtube.com/playlist?list=PLnocShPlK-Ft-o7NInBDw18be86dNaxlT Recordings: refer to the course website Assignments: refer to the course website","title":"Course Resources"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS170/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/UCB-CS170 - GitHub","title":"Personal Resources"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS61B/","text":"CS61B: Data Structures and Algorithms Descriptions Offered by: UC Berkeley Prerequisites: CS61A Programming Languages: Java Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 60 hours It is the second course of UC Berkeley's CS61 series. It mainly focuses on the design of data structures and algorithms as well as giving students the opportunity to be exposed to thousands of lines of engineering code and gain a preliminary understanding of software engineering through Java. I took the version for 2018 Spring. Josh Hug, the instructor, generously made the autograder open-source. You can use gradescope invitation code published on the website for free and easily test your implementation. All programming assignments in this course are done in Java. Students without Java experience don't have to worry. There will be detailed tutorials in the course from the configuration of IDEA to the core syntax and features of Java. The quality of homework in this class is also unparalleled. The 14 labs will allow you to implement most of the data structures mentioned in the class by yourself, and the 10 homework will allow you to use data structures and algorithms to solve practical problems. In addition, there are 3 projects that give you the opportunity to be exposed to thousands of lines of engineering code and enhance your Java skills in practice. Resources Course Resources Course Website: https://sp18.datastructur.es/ Recordings: refer to the course website Textbook: None Assignments: Slightly different every year. In the spring semester of 2018, there are 14 Labs, 10 Homeworks and 3 Projects. Please refer to the course website for specific requirements. Personal resources All resources and homework implementations used by @PKUFlyingPig in this course are summarized in PKUFlyingPig/CS61B - GitHub .","title":"UCB CS61B: Data Structures and Algorithms"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS61B/#cs61b-data-structures-and-algorithms","text":"","title":"CS61B: Data Structures and Algorithms"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS61B/#descriptions","text":"Offered by: UC Berkeley Prerequisites: CS61A Programming Languages: Java Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 60 hours It is the second course of UC Berkeley's CS61 series. It mainly focuses on the design of data structures and algorithms as well as giving students the opportunity to be exposed to thousands of lines of engineering code and gain a preliminary understanding of software engineering through Java. I took the version for 2018 Spring. Josh Hug, the instructor, generously made the autograder open-source. You can use gradescope invitation code published on the website for free and easily test your implementation. All programming assignments in this course are done in Java. Students without Java experience don't have to worry. There will be detailed tutorials in the course from the configuration of IDEA to the core syntax and features of Java. The quality of homework in this class is also unparalleled. The 14 labs will allow you to implement most of the data structures mentioned in the class by yourself, and the 10 homework will allow you to use data structures and algorithms to solve practical problems. In addition, there are 3 projects that give you the opportunity to be exposed to thousands of lines of engineering code and enhance your Java skills in practice.","title":"Descriptions"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS61B/#resources","text":"","title":"Resources"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS61B/#course-resources","text":"Course Website: https://sp18.datastructur.es/ Recordings: refer to the course website Textbook: None Assignments: Slightly different every year. In the spring semester of 2018, there are 14 Labs, 10 Homeworks and 3 Projects. Please refer to the course website for specific requirements.","title":"Course Resources"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS61B/#personal-resources","text":"All resources and homework implementations used by @PKUFlyingPig in this course are summarized in PKUFlyingPig/CS61B - GitHub .","title":"Personal resources"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/CS189/","text":"CS189: Introduction to Machine Learning \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS188, CS70 \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 \u8fd9\u95e8\u8bfe\u6211\u6ca1\u6709\u7cfb\u7edf\u4e0a\u8fc7\uff0c\u53ea\u662f\u628a\u5b83\u7684\u8bfe\u7a0b notes \u4f5c\u4e3a\u5de5\u5177\u4e66\u67e5\u9605\u3002\u4e0d\u8fc7\u4ece\u8bfe\u7a0b\u7f51\u7ad9\u4e0a\u6765\u770b\uff0c\u5b83\u6bd4 CS229 \u597d\u7684\u662f\u5f00\u6e90\u4e86\u6240\u6709 homework \u7684\u4ee3\u7801\u4ee5\u53ca gradescope \u7684 autograder\u3002\u540c\u6837\uff0c\u8fd9\u95e8\u8bfe\u8bb2\u5f97\u76f8\u5f53\u7406\u8bba\u4e14\u6df1\u5165\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://www.eecs189.org/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.youtube.com/playlist?list=PLOOm2AoWIPEyZazQVnIcaK2KnezpGZV-X \u8bfe\u7a0b\u6559\u6750\uff1a https://www.eecs189.org/ \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://www.eecs189.org/","title":"UCB CS189: Introduction to Machine Learning"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/CS189/#cs189-introduction-to-machine-learning","text":"","title":"CS189: Introduction to Machine Learning"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/CS189/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS188, CS70 \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 \u8fd9\u95e8\u8bfe\u6211\u6ca1\u6709\u7cfb\u7edf\u4e0a\u8fc7\uff0c\u53ea\u662f\u628a\u5b83\u7684\u8bfe\u7a0b notes \u4f5c\u4e3a\u5de5\u5177\u4e66\u67e5\u9605\u3002\u4e0d\u8fc7\u4ece\u8bfe\u7a0b\u7f51\u7ad9\u4e0a\u6765\u770b\uff0c\u5b83\u6bd4 CS229 \u597d\u7684\u662f\u5f00\u6e90\u4e86\u6240\u6709 homework \u7684\u4ee3\u7801\u4ee5\u53ca gradescope \u7684 autograder\u3002\u540c\u6837\uff0c\u8fd9\u95e8\u8bfe\u8bb2\u5f97\u76f8\u5f53\u7406\u8bba\u4e14\u6df1\u5165\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/CS189/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a https://www.eecs189.org/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.youtube.com/playlist?list=PLOOm2AoWIPEyZazQVnIcaK2KnezpGZV-X \u8bfe\u7a0b\u6559\u6750\uff1a https://www.eecs189.org/ \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://www.eecs189.org/","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/CS229/","text":"CS229: Machine Learning \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u9ad8\u6570\uff0c\u6982\u7387\u8bba\uff0cPython\uff0c\u9700\u8981\u8f83\u6df1\u539a\u7684\u6570\u5b66\u529f\u5e95 \u7f16\u7a0b\u8bed\u8a00\uff1a\u65e0 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 \u540c\u6837\u662f\u5434\u6069\u8fbe\u8bb2\u6388\uff0c\u4f46\u662f\u8fd9\u662f\u4e00\u95e8\u7814\u7a76\u751f\u8bfe\u7a0b\uff0c\u6240\u4ee5\u66f4\u504f\u91cd\u6570\u5b66\u7406\u8bba\uff0c\u4e0d\u6ee1\u8db3\u4e8e\u8c03\u5305\u800c\u60f3\u6df1\u5165\u7406\u89e3\u7b97\u6cd5\u672c\u8d28\uff0c\u6216\u8005\u6709\u5fd7\u4e8e\u4ece\u4e8b\u673a\u5668\u5b66\u4e60\u7406\u8bba\u7814\u7a76\u7684\u540c\u5b66\u53ef\u4ee5\u5b66\u4e60\u8fd9\u95e8\u8bfe\u7a0b\u3002\u8bfe\u7a0b\u7f51\u7ad9\u4e0a\u63d0\u4f9b\u4e86\u6240\u6709\u7684\u8bfe\u7a0b notes\uff0c\u5199\u5f97\u975e\u5e38\u4e13\u4e1a\u4e14\u7406\u8bba\uff0c\u9700\u8981\u4e00\u5b9a\u7684\u6570\u5b66\u529f\u5e95\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://cs229.stanford.edu/syllabus.html \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1JE411w7Ub \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0\uff0c\u8bfe\u7a0b notes \u5199\u5f97\u975e\u5e38\u597d \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u4e0d\u5bf9\u516c\u4f17\u5f00\u653e \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS229 - GitHub \u4e2d\u3002","title":"Stanford CS229: Machine Learning"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/CS229/#cs229-machine-learning","text":"","title":"CS229: Machine Learning"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/CS229/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u9ad8\u6570\uff0c\u6982\u7387\u8bba\uff0cPython\uff0c\u9700\u8981\u8f83\u6df1\u539a\u7684\u6570\u5b66\u529f\u5e95 \u7f16\u7a0b\u8bed\u8a00\uff1a\u65e0 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 \u540c\u6837\u662f\u5434\u6069\u8fbe\u8bb2\u6388\uff0c\u4f46\u662f\u8fd9\u662f\u4e00\u95e8\u7814\u7a76\u751f\u8bfe\u7a0b\uff0c\u6240\u4ee5\u66f4\u504f\u91cd\u6570\u5b66\u7406\u8bba\uff0c\u4e0d\u6ee1\u8db3\u4e8e\u8c03\u5305\u800c\u60f3\u6df1\u5165\u7406\u89e3\u7b97\u6cd5\u672c\u8d28\uff0c\u6216\u8005\u6709\u5fd7\u4e8e\u4ece\u4e8b\u673a\u5668\u5b66\u4e60\u7406\u8bba\u7814\u7a76\u7684\u540c\u5b66\u53ef\u4ee5\u5b66\u4e60\u8fd9\u95e8\u8bfe\u7a0b\u3002\u8bfe\u7a0b\u7f51\u7ad9\u4e0a\u63d0\u4f9b\u4e86\u6240\u6709\u7684\u8bfe\u7a0b notes\uff0c\u5199\u5f97\u975e\u5e38\u4e13\u4e1a\u4e14\u7406\u8bba\uff0c\u9700\u8981\u4e00\u5b9a\u7684\u6570\u5b66\u529f\u5e95\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/CS229/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://cs229.stanford.edu/syllabus.html \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1JE411w7Ub \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0\uff0c\u8bfe\u7a0b notes \u5199\u5f97\u975e\u5e38\u597d \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u4e0d\u5bf9\u516c\u4f17\u5f00\u653e","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/CS229/#_3","text":"@PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS229 - GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/ML/","text":"Coursera: Machine Learning Descriptions Offered by: Stanford Prerequisites: entry level of AI and proficient in Python Programming Languages: Python Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 100 hours When it comes to Andrew Ng, no one in the AI community should be unaware of him. He is one of the founders of the famous online education platform Coursera , and also a famous professor at Stanford. This introductory machine learning course must be one of his famous works (the other is his deep learning course), and has hundreds of thousands of learners on Coursera (note that these are people who paid for the certificate, which costs several hundred dollars), and the number of nonpaying learners should be far more than that. The class is extremely friendly to novices, and Andrew has the ability to make machine learning as straightforward as 1+1=2. You'll learn about linear regression, logistic regression, support vector machines, unsupervised learning, dimensionality reduction, anomaly detection, and recommender systems, etc. and solidify your understanding with hands-on programming. The quality of the assignments needs no word to say. With detailed code frameworks and practical background, you can use what you've learned to solve real problems. Of course, as a public mooc, the difficulty of this course has been deliberately lowered, and many mathematical derivations are skimmed over. If you are interested in machine learning theory and want to investigate the mathematical theory behind these algorithms, you can refer to CS229 and CS189 . Course Resources Course Website: https://www.coursera.org/learn/machine-learning Recordings: refer to the course website Textbook: None Assignments: refer to the course website Personal Resources My implementation is lost in system reinstallation. However, the course is so famous that you can easily find related resoures online. Also, course material is available on Coursera.","title":"Coursera: Machine Learning"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/ML/#coursera-machine-learning","text":"","title":"Coursera: Machine Learning"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/ML/#descriptions","text":"Offered by: Stanford Prerequisites: entry level of AI and proficient in Python Programming Languages: Python Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 100 hours When it comes to Andrew Ng, no one in the AI community should be unaware of him. He is one of the founders of the famous online education platform Coursera , and also a famous professor at Stanford. This introductory machine learning course must be one of his famous works (the other is his deep learning course), and has hundreds of thousands of learners on Coursera (note that these are people who paid for the certificate, which costs several hundred dollars), and the number of nonpaying learners should be far more than that. The class is extremely friendly to novices, and Andrew has the ability to make machine learning as straightforward as 1+1=2. You'll learn about linear regression, logistic regression, support vector machines, unsupervised learning, dimensionality reduction, anomaly detection, and recommender systems, etc. and solidify your understanding with hands-on programming. The quality of the assignments needs no word to say. With detailed code frameworks and practical background, you can use what you've learned to solve real problems. Of course, as a public mooc, the difficulty of this course has been deliberately lowered, and many mathematical derivations are skimmed over. If you are interested in machine learning theory and want to investigate the mathematical theory behind these algorithms, you can refer to CS229 and CS189 .","title":"Descriptions"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/ML/#course-resources","text":"Course Website: https://www.coursera.org/learn/machine-learning Recordings: refer to the course website Textbook: None Assignments: refer to the course website","title":"Course Resources"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/ML/#personal-resources","text":"My implementation is lost in system reinstallation. However, the course is so famous that you can easily find related resoures online. Also, course material is available on Coursera.","title":"Personal Resources"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%B3%BB%E7%BB%9F/CMU10-414/","text":"CMU 10-414/714: Deep Learning Systems \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aCMU \u5148\u4fee\u8981\u6c42\uff1a\u7cfb\u7edf\u5165\u95e8(eg.15-213)\u3001\u6df1\u5ea6\u5b66\u4e60\u5165\u95e8\u3001\u57fa\u672c\u7684\u6570\u5b66\u77e5\u8bc6 \u7f16\u7a0b\u8bed\u8a00\uff1aN/A\uff08\u636e\u8bfe\u7a0b\u4e3b\u9875\uff0c\u8981\u6c42\u719f\u6089Python\u3001C/C++\uff09 \u8bfe\u7a0b\u96be\u5ea6\uff1aN/A \u9884\u8ba1\u5b66\u65f6\uff1aN/A \u8fd9\u662f CMU 2022\u5e74\u79cb\u5b63\u5b66\u671f\u5f00\u8bbe\u7684\u4e00\u95e8\u65b0\u8bfe\uff0c\u805a\u7126\u4e8e\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u7684\u5177\u4f53\u5b9e\u73b0\uff0c\u8bfe\u7a0b Project \u4f1a\u5b9e\u73b0\u4e00\u4e2a\u8ff7\u4f60\u7684\u7c7b\u4f3c\u4e8e Pytorch 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\u8fd9\u95e8\u8bfe\u662f\u673a\u5668\u5b66\u4e60\u7f16\u8bd1\u9886\u57df\u7684\u9876\u5c16\u5b66\u8005\u9648\u5929\u5947\u57282022\u5e74\u6691\u671f\u5f00\u8bbe\u7684\u4e00\u95e8\u5728\u7ebf\u8bfe\u7a0b\u3002\u5176\u5b9e\u673a\u5668\u5b66\u4e60\u7f16\u8bd1\u65e0\u8bba\u5728\u5de5\u4e1a\u754c\u8fd8\u662f\u5b66\u672f\u754c\u4ecd\u7136\u662f\u4e00\u4e2a\u975e\u5e38\u524d\u6cbf\u4e14\u5feb\u901f\u66f4\u8fed\u7684\u9886\u57df\uff0c\u56fd\u5185\u5916\u6b64\u524d\u8fd8\u6ca1\u6709\u4e3a\u8fd9\u4e2a\u65b9\u5411\u4e13\u95e8\u5f00\u8bbe\u7684\u76f8\u5173\u8bfe\u7a0b\u3002\u56e0\u6b64\u5982\u679c\u5bf9\u673a\u5668\u5b66\u4e60\u7f16\u8bd1\u611f\u5174\u8da3\u60f3\u6709\u4e2a\u5168\u8c8c\u6027\u7684\u611f\u77e5\u7684\u8bdd\uff0c\u53ef\u4ee5\u5b66\u4e60\u4e00\u4e0b\u8fd9\u95e8\u8bfe\u3002 \u672c\u8bfe\u7a0b\u4e3b\u8981\u4ee5 Apache TVM 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Compilation"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%B3%BB%E7%BB%9F/MLC/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aBilibili \u5927\u5b66 \u5148\u4fee\u8981\u6c42\uff1a\u673a\u5668\u5b66\u4e60/\u6df1\u5ea6\u5b66\u4e60\u57fa\u7840 \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a30\u5c0f\u65f6 \u8fd9\u95e8\u8bfe\u662f\u673a\u5668\u5b66\u4e60\u7f16\u8bd1\u9886\u57df\u7684\u9876\u5c16\u5b66\u8005\u9648\u5929\u5947\u57282022\u5e74\u6691\u671f\u5f00\u8bbe\u7684\u4e00\u95e8\u5728\u7ebf\u8bfe\u7a0b\u3002\u5176\u5b9e\u673a\u5668\u5b66\u4e60\u7f16\u8bd1\u65e0\u8bba\u5728\u5de5\u4e1a\u754c\u8fd8\u662f\u5b66\u672f\u754c\u4ecd\u7136\u662f\u4e00\u4e2a\u975e\u5e38\u524d\u6cbf\u4e14\u5feb\u901f\u66f4\u8fed\u7684\u9886\u57df\uff0c\u56fd\u5185\u5916\u6b64\u524d\u8fd8\u6ca1\u6709\u4e3a\u8fd9\u4e2a\u65b9\u5411\u4e13\u95e8\u5f00\u8bbe\u7684\u76f8\u5173\u8bfe\u7a0b\u3002\u56e0\u6b64\u5982\u679c\u5bf9\u673a\u5668\u5b66\u4e60\u7f16\u8bd1\u611f\u5174\u8da3\u60f3\u6709\u4e2a\u5168\u8c8c\u6027\u7684\u611f\u77e5\u7684\u8bdd\uff0c\u53ef\u4ee5\u5b66\u4e60\u4e00\u4e0b\u8fd9\u95e8\u8bfe\u3002 \u672c\u8bfe\u7a0b\u4e3b\u8981\u4ee5 Apache TVM 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\u76f8\u5173\u7684\u7f16\u7a0b\u5f00\u53d1\u7684\u8bdd\uff0c\u8fd9\u95e8\u8bfe\u6709\u4e30\u5bcc\u4e14\u89c4\u8303\u7684\u4ee3\u7801\u793a\u4f8b\u4ee5\u4f9b\u53c2\u8003\u3002 \u6240\u6709\u7684\u8bfe\u7a0b\u8d44\u6e90\u5168\u90e8\u5f00\u6e90\u5e76\u4e14\u6709\u4e2d\u6587\u548c\u82f1\u6587\u4e24\u4e2a\u7248\u672c\uff0cB\u7ad9\u548c\u6cb9\u7ba1\u5206\u522b\u6709\u4e2d\u6587\u548c\u82f1\u6587\u7684\u8bfe\u7a0b\u5f55\u5f71\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%B3%BB%E7%BB%9F/MLC/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a https://mlc.ai/summer22-zh/ \u8bfe\u7a0b\u89c6\u9891\uff1a Bilibili \u8bfe\u7a0b\u7b14\u8bb0\uff1a https://mlc.ai/zh/index.html \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://github.com/mlc-ai/notebooks/blob/main/assignment","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CMU10-708/","text":"CMU 10-708: Probabilistic Graphical Models \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aCMU \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Reinforcement Learning \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://sailinglab.github.io/pgm-spring-2019/ \u8fd9\u4e2a\u7f51\u7ad9\u5305\u542b\u4e86\u6240\u6709\u7684\u8d44\u6e90\uff1aslides, nots, video, homework, project \u8fd9\u95e8\u8bfe\u7a0b\u662f CMU \u7684\u56fe\u6a21\u578b\u57fa\u7840 + \u8fdb\u9636\u8bfe\uff0c\u6388\u8bfe\u8001\u5e08\u4e3a Eric P. Xing\uff0c\u6db5\u76d6\u4e86\u56fe\u6a21\u578b\u57fa\u7840\uff0c\u4e0e\u795e\u7ecf\u7f51\u7edc\u7684\u7ed3\u5408\uff0c\u5728\u5f3a\u5316\u5b66\u4e60\u4e2d\u7684\u5e94\u7528\uff0c\u4ee5\u53ca\u975e\u53c2\u6570\u65b9\u6cd5\u3002\u76f8\u5f53\u786c\u6838","title":"CMU 10-708: Probabilistic Graphical Models"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CMU10-708/#cmu-10-708-probabilistic-graphical-models","text":"","title":"CMU 10-708: Probabilistic Graphical Models"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CMU10-708/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aCMU \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Reinforcement Learning \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://sailinglab.github.io/pgm-spring-2019/ \u8fd9\u4e2a\u7f51\u7ad9\u5305\u542b\u4e86\u6240\u6709\u7684\u8d44\u6e90\uff1aslides, nots, video, homework, project \u8fd9\u95e8\u8bfe\u7a0b\u662f CMU \u7684\u56fe\u6a21\u578b\u57fa\u7840 + \u8fdb\u9636\u8bfe\uff0c\u6388\u8bfe\u8001\u5e08\u4e3a Eric P. Xing\uff0c\u6db5\u76d6\u4e86\u56fe\u6a21\u578b\u57fa\u7840\uff0c\u4e0e\u795e\u7ecf\u7f51\u7edc\u7684\u7ed3\u5408\uff0c\u5728\u5f3a\u5316\u5b66\u4e60\u4e2d\u7684\u5e94\u7528\uff0c\u4ee5\u53ca\u975e\u53c2\u6570\u65b9\u6cd5\u3002\u76f8\u5f53\u786c\u6838","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CS229M/","text":"STATS214 / CS229M: Machine Learning Theory \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Statistics \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/stats214/ \u7ecf\u5178\u5b66\u4e60\u7406\u8bba + \u6700\u65b0\u6df1\u5ea6\u5b66\u4e60\u7406\u8bba\uff0c\u975e\u5e38\u786c\u6838\u3002\u6388\u8bfe\u8001\u5e08\u4e4b\u524d\u662f Percy Liang\uff0c\u73b0\u5728\u662f Tengyu Ma","title":"Stanford STATS214 / CS229M: Machine Learning Theory"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CS229M/#stats214-cs229m-machine-learning-theory","text":"","title":"STATS214 / CS229M: Machine Learning Theory"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CS229M/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Statistics \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/stats214/ \u7ecf\u5178\u5b66\u4e60\u7406\u8bba + \u6700\u65b0\u6df1\u5ea6\u5b66\u4e60\u7406\u8bba\uff0c\u975e\u5e38\u786c\u6838\u3002\u6388\u8bfe\u8001\u5e08\u4e4b\u524d\u662f Percy Liang\uff0c\u73b0\u5728\u662f Tengyu Ma","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STA4273/","text":"STA 4273 Winter 2021: Minimizing Expectations \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aU Toronto \u5148\u4fee\u8981\u6c42\uff1aBayesian Inference, Reinforcement Learning \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://www.cs.toronto.edu/~cmaddis/courses/sta4273_w21/ \u8fd9\u662f\u4e00\u95e8\u8f83\u4e3a\u8fdb\u9636\u7684 Ph.D. \u7814\u7a76\u8bfe\u7a0b\uff0c\u6838\u5fc3\u5185\u5bb9\u662f inference \u548c control \u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u6388\u8bfe\u8001\u5e08\u4e3a Chris Maddison (AlphaGo founding member, NeurIPS 14 best paper)","title":"U Toronto STA 4273 Winter 2021: Minimizing Expectations"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STA4273/#sta-4273-winter-2021-minimizing-expectations","text":"","title":"STA 4273 Winter 2021: Minimizing Expectations"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STA4273/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aU Toronto \u5148\u4fee\u8981\u6c42\uff1aBayesian Inference, Reinforcement Learning \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://www.cs.toronto.edu/~cmaddis/courses/sta4273_w21/ \u8fd9\u662f\u4e00\u95e8\u8f83\u4e3a\u8fdb\u9636\u7684 Ph.D. \u7814\u7a76\u8bfe\u7a0b\uff0c\u6838\u5fc3\u5185\u5bb9\u662f inference \u548c control \u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u6388\u8bfe\u8001\u5e08\u4e3a Chris Maddison (AlphaGo founding member, NeurIPS 14 best paper)","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STAT8201/","text":"Columbia STAT 8201: Deep Generative Models \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aColumbia University \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Graphical Models \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://stat.columbia.edu/~cunningham/teaching/GR8201/ \u8fd9\u95e8\u8bfe\u662f\u4e00\u95e8 PhD \u8ba8\u8bba\u73ed\uff0c\u6bcf\u5468\u7684\u5185\u5bb9\u662f\u5c55\u793a + \u8ba8\u8bba\u8bba\u6587\uff0c\u6388\u8bfe\u8001\u5e08\u662f John Cunningham\u3002Deep Generative Models \uff08\u6df1\u5ea6\u751f\u6210\u6a21\u578b\uff09 \u662f\u56fe\u6a21\u578b\u4e0e\u795e\u7ecf\u7f51\u7edc\u7684\u7ed3\u5408\uff0c\u4e5f\u662f\u73b0\u4ee3\u673a\u5668\u5b66\u4e60\u6700\u91cd\u8981\u7684\u65b9\u5411\u4e4b\u4e00","title":"Columbia STAT 8201: Deep Generative Models"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STAT8201/#columbia-stat-8201-deep-generative-models","text":"","title":"Columbia STAT 8201: Deep Generative Models"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STAT8201/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aColumbia University \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Graphical Models \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://stat.columbia.edu/~cunningham/teaching/GR8201/ \u8fd9\u95e8\u8bfe\u662f\u4e00\u95e8 PhD \u8ba8\u8bba\u73ed\uff0c\u6bcf\u5468\u7684\u5185\u5bb9\u662f\u5c55\u793a + \u8ba8\u8bba\u8bba\u6587\uff0c\u6388\u8bfe\u8001\u5e08\u662f John Cunningham\u3002Deep Generative Models \uff08\u6df1\u5ea6\u751f\u6210\u6a21\u578b\uff09 \u662f\u56fe\u6a21\u578b\u4e0e\u795e\u7ecf\u7f51\u7edc\u7684\u7ed3\u5408\uff0c\u4e5f\u662f\u73b0\u4ee3\u673a\u5668\u5b66\u4e60\u6700\u91cd\u8981\u7684\u65b9\u5411\u4e4b\u4e00","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/","text":"\u673a\u5668\u5b66\u4e60\u8fdb\u9636 \u6b64\u8def\u7ebf\u56fe\u9002\u7528\u4e8e\u5df2\u7ecf\u5b66\u8fc7\u4e86\u57fa\u7840\u673a\u5668\u5b66\u4e60 (ML, NLP, CV, RL) \u7684\u540c\u5b66 (\u9ad8\u5e74\u7ea7\u672c\u79d1\u751f\u6216\u4f4e\u5e74\u7ea7\u7814\u7a76\u751f)\uff0c\u5df2\u7ecf\u53d1\u8868\u8fc7\u81f3\u5c11\u4e00\u7bc7\u9876\u4f1a\u8bba\u6587 (NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, CVPR, ICCV) \u60f3\u8981\u8d70\u673a\u5668\u5b66\u4e60\u79d1\u7814\u8def\u7ebf\u7684\u9009\u624b\u3002 \u6b64\u8def\u7ebf\u7684\u76ee\u6807\u662f\u4e3a\u8bfb\u61c2\u4e0e\u53d1\u8868\u673a\u5668\u5b66\u4e60\u9876\u4f1a\u8bba\u6587\u6253\u4e0b\u7406\u8bba\u57fa\u7840\uff0c\u7279\u522b\u662f Probabilistic Methods \u8fd9\u4e2a track \u4e0b\u7684\u6587\u7ae0 \u673a\u5668\u5b66\u4e60\u8fdb\u9636\u53ef\u80fd\u5b58\u5728\u591a\u79cd\u4e0d\u540c\u7684\u5b66\u4e60\u8def\u7ebf\uff0c\u6b64\u8def\u7ebf\u53ea\u80fd\u4ee3\u8868\u4f5c\u8005 Yao Fu \u6240\u7406\u89e3\u7684\u6700\u4f73\u8def\u5f84\uff0c\u4fa7\u91cd\u4e8e\u8d1d\u53f6\u65af\u5b66\u6d3e\u4e0b\u7684\u6982\u7387\u5efa\u6a21\u65b9\u6cd5\uff0c\u4e5f\u4f1a\u6d89\u53ca\u5230\u5404\u9879\u76f8\u5173\u5b66\u79d1\u7684\u4ea4\u53c9\u77e5\u8bc6\u3002 \u5fc5\u8bfb\u6559\u6750 PRML: Pattern Recognition and Machine Learning. Christopher Bishop \u7ecf\u5178\u8d1d\u53f6\u65af\u5b66\u6d3e\u6559\u6750 AoS: All of Statistics. Larry Wasserman \u7ecf\u5178\u9891\u7387\u5b66\u6d3e\u6559\u6750 \u6240\u4ee5\u8fd9\u4e24\u672c\u4e66\u521a\u597d\u76f8\u8f85\u76f8\u6210 \u5b57\u5178 MLAPP: Machine Learning: A Probabilistic Perspective. Kevin Murphy Convex Optimization. Stephen Boyd and Lieven Vandenberghe \u8fdb\u9636\u4e66\u7c4d W&J: Graphical Models, Exponential Families, and Variational Inference. Martin Wainwright and Michael Jordan Theory of Point Estimation. E. L. Lehmann and George Casella \u5982\u4f55\u9605\u8bfb Guidelines \u5fc5\u8bfb\u6559\u6750\u5c31\u662f\u4e00\u5b9a\u8981\u8bfb\u7684\u6559\u6750 \u5b57\u5178\u7684\u610f\u601d\u662f\uff0c\u4e00\u822c\u60c5\u51b5\u4e0b\u4e0d\u7ba1\u5b83\uff0c\u4f46\u5f53\u9047\u5230\u4e86\u4e0d\u61c2\u7684\u6982\u5ff5\u7684\u65f6\u5019\uff0c\u5c31\u53bb\u5b57\u5178\u91cc\u9762\u67e5\uff08\u800c\u4e0d\u662f\u7ef4\u57fa\u767e\u79d1\uff09 \u8fdb\u9636\u4e66\u7c4d\u5148\u4e0d\u8bfb\uff0c\u5148\u8bfb\u5b8c\u5fc5\u8bfb\u4e66\u7c4d\u3002\u5fc5\u8bfb\u4e66\u7c4d\u4e00\u822c\u90fd\u662f\u8981\u524d\u524d\u540e\u540e\u53cd\u590d\u770b\u8fc7 N \u904d\u624d\u7b97\u8bfb\u5b8c \u8bfb\u7684\u8fc7\u7a0b\u4e2d\uff0c\u6700\u91cd\u8981\u7684\u8bfb\u6cd5\u5c31\u662f\u5bf9\u6bd4\u9605\u8bfb (contrastive-comparative reading)\uff1a\u540c\u65f6\u6253\u5f00\u4e24\u672c\u4e66\u8bb2\u540c\u4e00\u4e3b\u9898\u7684\u7ae0\u8282\uff0c\u7136\u540e\u5bf9\u6bd4\u76f8\u540c\u70b9\u548c\u4e0d\u540c\u70b9\u548c\u8054\u7cfb \u8bfb\u7684\u8fc7\u7a0b\u4e2d\uff0c\u5c3d\u91cf\u53bb\u56de\u60f3\u4e4b\u524d\u8bfb\u8fc7\u7684\u8bba\u6587\uff0c\u6bd4\u8f83\u8bba\u6587\u548c\u6559\u6750\u7684\u76f8\u540c\u70b9\u4e0e\u4e0d\u540c\u70b9 \u57fa\u7840\u8def\u5f84 \u5148\u8bfb AoS \u7b2c\u516d\u7ae0: Models, Statistical Inference and Learning\uff0c\u8fd9\u4e00\u90e8\u5206\u662f\u6700\u57fa\u7840\u7684\u79d1\u666e \u7136\u540e\u8bfb PRML \u7b2c 10, 11 \u7ae0 \u7b2c 10 \u7ae0\u7684\u5185\u5bb9\u662f Variational Inference, \u7b2c 11 \u7ae0\u7684\u5185\u5bb9\u662f MCMC, \u8fd9\u4e24\u79cd\u65b9\u6cd5\u662f\u8d1d\u53f6\u65af\u63a8\u65ad\u7684\u4e24\u6761\u6700\u4e3b\u8981\u8def\u7ebf \u5982\u679c\u5728\u8bfb PRML \u7684\u8fc7\u7a0b\u4e2d\u53d1\u73b0\u6709\u4efb\u4f55\u4e0d\u61c2\u7684\u540d\u8bcd\uff0c\u5c31\u53bb\u7ffb\u524d\u9762\u7684\u7ae0\u8282\u3002\u5f88\u5927\u6982\u7387\u80fd\u591f\u5728\u7b2c 3\uff0c4 \u7ae0\u627e\u5230\u76f8\u5bf9\u5e94\u7684\u5b9a\u4e49\uff1b\u5982\u679c\u627e\u4e0d\u5230\u6216\u8005\u4e0d\u591f\u8be6\u7ec6\uff0c\u5c31\u53bb\u67e5 MLAPP AoS \u7b2c 8 \u7ae0 (Parametric Inference) \u548c\u7b2c 11 \u7ae0 (Bayesian Inference) \u4e5f\u53ef\u4ee5\u4f5c\u4e3a\u53c2\u8003\u3002\u6700\u597d\u7684\u65b9\u6cd5\u662f\u591a\u672c\u4e66\u5bf9\u6bd4\u9605\u8bfb\uff0c\u6d41\u7a0b\u5982\u4e0b \u5047\u8bbe\u6211\u5728\u8bfb PRML \u7b2c 10 \u7ae0\u7684\u65f6\u5019\u53d1\u73b0\u4e86\u4e00\u4e2a\u4e0d\u61c2\u7684\u8bcd\uff1aposterior inference \u4e8e\u662f\u6211\u5f80\u524d\u7ffb\uff0c\u7ffb\u5230\u4e86\u7b2c 3 \u7ae0 (Linear Model for Regression)\uff0c\u770b\u5230\u4e86\u6700\u7b80\u5355\u7684 posterior \u7136\u540e\u6211\u63a5\u7740\u7ffb AoS\uff0c\u7ffb\u5230\u4e86\u7b2c 11 \u7ae0\uff0c\u4e5f\u6709\u5bf9 posterior \u7684\u63cf\u8ff0 \u7136\u540e\u6211\u5bf9\u6bd4 PRML \u7b2c 10 \u7ae0\uff0c\u7b2c 3 \u7ae0\uff0cAoS \u7b2c 11 \u7ae0\uff0c\u4e09\u5904\u4e0d\u540c\u5730\u65b9\u5bf9 posterior \u7684\u89e3\u8bfb\uff0c\u6bd4\u8f83\u5176\u76f8\u540c\u70b9\u548c\u4e0d\u540c\u70b9\u548c\u8054\u7cfb \u8bfb\u5b8c PRML \u7b2c 10 \u548c 11 \u7ae0\u4e4b\u540e\uff0c\u63a5\u7740\u8bfb AoS \u7b2c 24 \u7ae0 (Simulation Methods)\uff0c\u7136\u540e\u628a\u5b83\u548c PRML \u7b2c 11 \u7ae0\u5bf9\u6bd4\u9605\u8bfb -- \u8fd9\u4fe9\u90fd\u662f\u8bb2 MCMC \u5982\u679c\u5230\u6b64\u5904\u53d1\u73b0\u8fd8\u6709\u57fa\u7840\u6982\u5ff5\u8bfb\u4e0d\u61c2\uff0c\u5c31\u56de\u5230 PRML \u7b2c 3 \u7ae0\uff0c\u628a\u5b83\u548c AoS \u7b2c 11 \u7ae0\u5bf9\u6bd4\u9605\u8bfb Again\uff0c\u5bf9\u6bd4\u9605\u8bfb\u975e\u5e38\u91cd\u8981\uff0c\u4e00\u5b9a\u8981\u628a\u4e0d\u540c\u672c\u4e66\u7684\u7c7b\u4f3c\u5185\u5bb9\u540c\u65f6\u6446\u5728\u9762\u524d\u76f8\u4e92\u5bf9\u6bd4\uff0c\u8fd9\u6837\u53ef\u4ee5\u663e\u8457\u589e\u5f3a\u8bb0\u5fc6 \u7136\u540e\u8bfb PRML \u7b2c 13 \u7ae0\uff08\u8df3\u8fc7\u7b2c 12 \u7ae0\uff09\uff0c\u8fd9\u4e00\u7ae0\u53ef\u4ee5\u548c MLAPP \u7684\u7b2c 17, 18 \u7ae0\u5bf9\u6bd4\u9605\u8bfb MLAPP \u7b2c 17 \u7ae0\u662f PRML \u7b2c 13.2 \u7ae0\u7684\u8be6\u7ec6\u7248\uff0c\u4e3b\u8981\u8bb2 HMM MLAPP \u7b2c 18 \u7ae0\u662f PRML \u7b2c 13.3 \u7ae0\u7684\u8be6\u7ec6\u7248\uff0c\u4e3b\u8981\u8bb2 LDS \u8bfb\u5b8c PRML \u7b2c 13 \u7ae0\u4e4b\u540e\uff0c\u518d\u53bb\u8bfb PRML \u7b2c 8 \u7ae0 (Graphical Models) -- \u6b64\u65f6\u8fd9\u90e8\u5206\u5e94\u8be5\u4f1a\u8bfb\u5f97\u5f88\u8f7b\u677e \u4ee5\u4e0a\u7684\u5185\u5bb9\u53ef\u4ee5\u8fdb\u4e00\u6b65\u5bf9\u7167 CMU 10-708 PGM \u8bfe\u7a0b\u6750\u6599 \u5230\u76ee\u524d\u4e3a\u6b62\uff0c\u5e94\u8be5\u80fd\u591f\u638c\u63e1 \u6982\u7387\u6a21\u578b\u7684\u57fa\u7840\u5b9a\u4e49 \u7cbe\u51c6\u63a8\u65ad - Sum-Product \u8fd1\u4f3c\u63a8\u65ad - MCMC \u8fd1\u4f3c\u63a8\u65ad - VI \u7136\u540e\u5c31\u53ef\u4ee5\u53bb\u505a\u66f4\u8fdb\u9636\u7684\u5185\u5bb9","title":"\u8fdb\u9636\u8def\u7ebf\u56fe"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_1","text":"\u6b64\u8def\u7ebf\u56fe\u9002\u7528\u4e8e\u5df2\u7ecf\u5b66\u8fc7\u4e86\u57fa\u7840\u673a\u5668\u5b66\u4e60 (ML, NLP, CV, RL) \u7684\u540c\u5b66 (\u9ad8\u5e74\u7ea7\u672c\u79d1\u751f\u6216\u4f4e\u5e74\u7ea7\u7814\u7a76\u751f)\uff0c\u5df2\u7ecf\u53d1\u8868\u8fc7\u81f3\u5c11\u4e00\u7bc7\u9876\u4f1a\u8bba\u6587 (NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, CVPR, ICCV) \u60f3\u8981\u8d70\u673a\u5668\u5b66\u4e60\u79d1\u7814\u8def\u7ebf\u7684\u9009\u624b\u3002 \u6b64\u8def\u7ebf\u7684\u76ee\u6807\u662f\u4e3a\u8bfb\u61c2\u4e0e\u53d1\u8868\u673a\u5668\u5b66\u4e60\u9876\u4f1a\u8bba\u6587\u6253\u4e0b\u7406\u8bba\u57fa\u7840\uff0c\u7279\u522b\u662f Probabilistic Methods \u8fd9\u4e2a track \u4e0b\u7684\u6587\u7ae0 \u673a\u5668\u5b66\u4e60\u8fdb\u9636\u53ef\u80fd\u5b58\u5728\u591a\u79cd\u4e0d\u540c\u7684\u5b66\u4e60\u8def\u7ebf\uff0c\u6b64\u8def\u7ebf\u53ea\u80fd\u4ee3\u8868\u4f5c\u8005 Yao Fu \u6240\u7406\u89e3\u7684\u6700\u4f73\u8def\u5f84\uff0c\u4fa7\u91cd\u4e8e\u8d1d\u53f6\u65af\u5b66\u6d3e\u4e0b\u7684\u6982\u7387\u5efa\u6a21\u65b9\u6cd5\uff0c\u4e5f\u4f1a\u6d89\u53ca\u5230\u5404\u9879\u76f8\u5173\u5b66\u79d1\u7684\u4ea4\u53c9\u77e5\u8bc6\u3002","title":"\u673a\u5668\u5b66\u4e60\u8fdb\u9636"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_2","text":"PRML: Pattern Recognition and Machine Learning. Christopher Bishop \u7ecf\u5178\u8d1d\u53f6\u65af\u5b66\u6d3e\u6559\u6750 AoS: All of Statistics. Larry Wasserman \u7ecf\u5178\u9891\u7387\u5b66\u6d3e\u6559\u6750 \u6240\u4ee5\u8fd9\u4e24\u672c\u4e66\u521a\u597d\u76f8\u8f85\u76f8\u6210","title":"\u5fc5\u8bfb\u6559\u6750"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_3","text":"MLAPP: Machine Learning: A Probabilistic Perspective. Kevin Murphy Convex Optimization. Stephen Boyd and Lieven Vandenberghe","title":"\u5b57\u5178"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_4","text":"W&J: Graphical Models, Exponential Families, and Variational Inference. Martin Wainwright and Michael Jordan Theory of Point Estimation. E. L. Lehmann and George Casella","title":"\u8fdb\u9636\u4e66\u7c4d"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_5","text":"","title":"\u5982\u4f55\u9605\u8bfb"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#guidelines","text":"\u5fc5\u8bfb\u6559\u6750\u5c31\u662f\u4e00\u5b9a\u8981\u8bfb\u7684\u6559\u6750 \u5b57\u5178\u7684\u610f\u601d\u662f\uff0c\u4e00\u822c\u60c5\u51b5\u4e0b\u4e0d\u7ba1\u5b83\uff0c\u4f46\u5f53\u9047\u5230\u4e86\u4e0d\u61c2\u7684\u6982\u5ff5\u7684\u65f6\u5019\uff0c\u5c31\u53bb\u5b57\u5178\u91cc\u9762\u67e5\uff08\u800c\u4e0d\u662f\u7ef4\u57fa\u767e\u79d1\uff09 \u8fdb\u9636\u4e66\u7c4d\u5148\u4e0d\u8bfb\uff0c\u5148\u8bfb\u5b8c\u5fc5\u8bfb\u4e66\u7c4d\u3002\u5fc5\u8bfb\u4e66\u7c4d\u4e00\u822c\u90fd\u662f\u8981\u524d\u524d\u540e\u540e\u53cd\u590d\u770b\u8fc7 N \u904d\u624d\u7b97\u8bfb\u5b8c \u8bfb\u7684\u8fc7\u7a0b\u4e2d\uff0c\u6700\u91cd\u8981\u7684\u8bfb\u6cd5\u5c31\u662f\u5bf9\u6bd4\u9605\u8bfb (contrastive-comparative reading)\uff1a\u540c\u65f6\u6253\u5f00\u4e24\u672c\u4e66\u8bb2\u540c\u4e00\u4e3b\u9898\u7684\u7ae0\u8282\uff0c\u7136\u540e\u5bf9\u6bd4\u76f8\u540c\u70b9\u548c\u4e0d\u540c\u70b9\u548c\u8054\u7cfb \u8bfb\u7684\u8fc7\u7a0b\u4e2d\uff0c\u5c3d\u91cf\u53bb\u56de\u60f3\u4e4b\u524d\u8bfb\u8fc7\u7684\u8bba\u6587\uff0c\u6bd4\u8f83\u8bba\u6587\u548c\u6559\u6750\u7684\u76f8\u540c\u70b9\u4e0e\u4e0d\u540c\u70b9","title":"Guidelines"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_6","text":"\u5148\u8bfb AoS \u7b2c\u516d\u7ae0: Models, Statistical Inference and Learning\uff0c\u8fd9\u4e00\u90e8\u5206\u662f\u6700\u57fa\u7840\u7684\u79d1\u666e \u7136\u540e\u8bfb PRML \u7b2c 10, 11 \u7ae0 \u7b2c 10 \u7ae0\u7684\u5185\u5bb9\u662f Variational Inference, \u7b2c 11 \u7ae0\u7684\u5185\u5bb9\u662f MCMC, \u8fd9\u4e24\u79cd\u65b9\u6cd5\u662f\u8d1d\u53f6\u65af\u63a8\u65ad\u7684\u4e24\u6761\u6700\u4e3b\u8981\u8def\u7ebf \u5982\u679c\u5728\u8bfb PRML \u7684\u8fc7\u7a0b\u4e2d\u53d1\u73b0\u6709\u4efb\u4f55\u4e0d\u61c2\u7684\u540d\u8bcd\uff0c\u5c31\u53bb\u7ffb\u524d\u9762\u7684\u7ae0\u8282\u3002\u5f88\u5927\u6982\u7387\u80fd\u591f\u5728\u7b2c 3\uff0c4 \u7ae0\u627e\u5230\u76f8\u5bf9\u5e94\u7684\u5b9a\u4e49\uff1b\u5982\u679c\u627e\u4e0d\u5230\u6216\u8005\u4e0d\u591f\u8be6\u7ec6\uff0c\u5c31\u53bb\u67e5 MLAPP AoS \u7b2c 8 \u7ae0 (Parametric Inference) \u548c\u7b2c 11 \u7ae0 (Bayesian Inference) \u4e5f\u53ef\u4ee5\u4f5c\u4e3a\u53c2\u8003\u3002\u6700\u597d\u7684\u65b9\u6cd5\u662f\u591a\u672c\u4e66\u5bf9\u6bd4\u9605\u8bfb\uff0c\u6d41\u7a0b\u5982\u4e0b \u5047\u8bbe\u6211\u5728\u8bfb PRML \u7b2c 10 \u7ae0\u7684\u65f6\u5019\u53d1\u73b0\u4e86\u4e00\u4e2a\u4e0d\u61c2\u7684\u8bcd\uff1aposterior inference \u4e8e\u662f\u6211\u5f80\u524d\u7ffb\uff0c\u7ffb\u5230\u4e86\u7b2c 3 \u7ae0 (Linear Model for Regression)\uff0c\u770b\u5230\u4e86\u6700\u7b80\u5355\u7684 posterior \u7136\u540e\u6211\u63a5\u7740\u7ffb AoS\uff0c\u7ffb\u5230\u4e86\u7b2c 11 \u7ae0\uff0c\u4e5f\u6709\u5bf9 posterior \u7684\u63cf\u8ff0 \u7136\u540e\u6211\u5bf9\u6bd4 PRML \u7b2c 10 \u7ae0\uff0c\u7b2c 3 \u7ae0\uff0cAoS \u7b2c 11 \u7ae0\uff0c\u4e09\u5904\u4e0d\u540c\u5730\u65b9\u5bf9 posterior \u7684\u89e3\u8bfb\uff0c\u6bd4\u8f83\u5176\u76f8\u540c\u70b9\u548c\u4e0d\u540c\u70b9\u548c\u8054\u7cfb \u8bfb\u5b8c PRML \u7b2c 10 \u548c 11 \u7ae0\u4e4b\u540e\uff0c\u63a5\u7740\u8bfb AoS \u7b2c 24 \u7ae0 (Simulation Methods)\uff0c\u7136\u540e\u628a\u5b83\u548c PRML \u7b2c 11 \u7ae0\u5bf9\u6bd4\u9605\u8bfb -- \u8fd9\u4fe9\u90fd\u662f\u8bb2 MCMC \u5982\u679c\u5230\u6b64\u5904\u53d1\u73b0\u8fd8\u6709\u57fa\u7840\u6982\u5ff5\u8bfb\u4e0d\u61c2\uff0c\u5c31\u56de\u5230 PRML \u7b2c 3 \u7ae0\uff0c\u628a\u5b83\u548c AoS \u7b2c 11 \u7ae0\u5bf9\u6bd4\u9605\u8bfb Again\uff0c\u5bf9\u6bd4\u9605\u8bfb\u975e\u5e38\u91cd\u8981\uff0c\u4e00\u5b9a\u8981\u628a\u4e0d\u540c\u672c\u4e66\u7684\u7c7b\u4f3c\u5185\u5bb9\u540c\u65f6\u6446\u5728\u9762\u524d\u76f8\u4e92\u5bf9\u6bd4\uff0c\u8fd9\u6837\u53ef\u4ee5\u663e\u8457\u589e\u5f3a\u8bb0\u5fc6 \u7136\u540e\u8bfb PRML \u7b2c 13 \u7ae0\uff08\u8df3\u8fc7\u7b2c 12 \u7ae0\uff09\uff0c\u8fd9\u4e00\u7ae0\u53ef\u4ee5\u548c MLAPP \u7684\u7b2c 17, 18 \u7ae0\u5bf9\u6bd4\u9605\u8bfb MLAPP \u7b2c 17 \u7ae0\u662f PRML \u7b2c 13.2 \u7ae0\u7684\u8be6\u7ec6\u7248\uff0c\u4e3b\u8981\u8bb2 HMM MLAPP \u7b2c 18 \u7ae0\u662f PRML \u7b2c 13.3 \u7ae0\u7684\u8be6\u7ec6\u7248\uff0c\u4e3b\u8981\u8bb2 LDS \u8bfb\u5b8c PRML \u7b2c 13 \u7ae0\u4e4b\u540e\uff0c\u518d\u53bb\u8bfb PRML \u7b2c 8 \u7ae0 (Graphical Models) -- \u6b64\u65f6\u8fd9\u90e8\u5206\u5e94\u8be5\u4f1a\u8bfb\u5f97\u5f88\u8f7b\u677e \u4ee5\u4e0a\u7684\u5185\u5bb9\u53ef\u4ee5\u8fdb\u4e00\u6b65\u5bf9\u7167 CMU 10-708 PGM \u8bfe\u7a0b\u6750\u6599 \u5230\u76ee\u524d\u4e3a\u6b62\uff0c\u5e94\u8be5\u80fd\u591f\u638c\u63e1 \u6982\u7387\u6a21\u578b\u7684\u57fa\u7840\u5b9a\u4e49 \u7cbe\u51c6\u63a8\u65ad - Sum-Product \u8fd1\u4f3c\u63a8\u65ad - MCMC \u8fd1\u4f3c\u63a8\u65ad - VI \u7136\u540e\u5c31\u53ef\u4ee5\u53bb\u505a\u66f4\u8fdb\u9636\u7684\u5185\u5bb9","title":"\u57fa\u7840\u8def\u5f84"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224n/","text":"CS224n: Natural Language Processing \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6df1\u5ea6\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 Stanford \u7684 NLP \u5165\u95e8\u8bfe\u7a0b\uff0c\u7531\u81ea\u7136\u8bed\u8a00\u5904\u7406\u9886\u57df\u7684\u5de8\u4f6c Chris Manning \u9886\u8854\u6559\u6388\uff08word2vec \u7b97\u6cd5\u7684\u5f00\u521b\u8005\uff09\u3002\u5185\u5bb9\u8986\u76d6\u4e86\u8bcd\u5411\u91cf\u3001RNN\u3001LSTM\u3001Seq2Seq \u6a21\u578b\u3001\u673a\u5668\u7ffb\u8bd1\u3001\u6ce8\u610f\u529b\u673a\u5236\u3001Transformer \u7b49\u7b49 NLP \u9886\u57df\u7684\u6838\u5fc3\u77e5\u8bc6\u70b9\u3002 5 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a\u96be\u5ea6\u5faa\u5e8f\u6e10\u8fdb\uff0c\u5206\u522b\u662f\u8bcd\u5411\u91cf\u3001word2vec \u7b97\u6cd5\u3001Dependency parsing\u3001\u673a\u5668\u7ffb\u8bd1\u4ee5\u53ca Transformer \u7684 fine-tune\u3002 \u6700\u7ec8\u7684\u5927\u4f5c\u4e1a\u662f\u5728 Stanford \u8457\u540d\u7684 SQuAD \u6570\u636e\u96c6\u4e0a\u8bad\u7ec3 QA \u6a21\u578b\uff0c\u6709\u5b66\u751f\u7684\u5927\u4f5c\u4e1a\u751a\u81f3\u76f4\u63a5\u53d1\u8868\u4e86\u9876\u4f1a\u8bba\u6587\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs224n/index.html \u8bfe\u7a0b\u89c6\u9891\uff1aB \u7ad9\u641c\u7d22 CS224n \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a http://web.stanford.edu/class/cs224n/index.html \uff0c5 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a + 1 \u4e2a Final Project \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS224n - GitHub \u4e2d\u3002","title":"Stanford CS224n: Natural Language Processing"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224n/#cs224n-natural-language-processing","text":"","title":"CS224n: Natural Language Processing"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224n/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6df1\u5ea6\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 Stanford \u7684 NLP \u5165\u95e8\u8bfe\u7a0b\uff0c\u7531\u81ea\u7136\u8bed\u8a00\u5904\u7406\u9886\u57df\u7684\u5de8\u4f6c Chris Manning \u9886\u8854\u6559\u6388\uff08word2vec \u7b97\u6cd5\u7684\u5f00\u521b\u8005\uff09\u3002\u5185\u5bb9\u8986\u76d6\u4e86\u8bcd\u5411\u91cf\u3001RNN\u3001LSTM\u3001Seq2Seq \u6a21\u578b\u3001\u673a\u5668\u7ffb\u8bd1\u3001\u6ce8\u610f\u529b\u673a\u5236\u3001Transformer \u7b49\u7b49 NLP \u9886\u57df\u7684\u6838\u5fc3\u77e5\u8bc6\u70b9\u3002 5 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a\u96be\u5ea6\u5faa\u5e8f\u6e10\u8fdb\uff0c\u5206\u522b\u662f\u8bcd\u5411\u91cf\u3001word2vec \u7b97\u6cd5\u3001Dependency parsing\u3001\u673a\u5668\u7ffb\u8bd1\u4ee5\u53ca Transformer \u7684 fine-tune\u3002 \u6700\u7ec8\u7684\u5927\u4f5c\u4e1a\u662f\u5728 Stanford \u8457\u540d\u7684 SQuAD \u6570\u636e\u96c6\u4e0a\u8bad\u7ec3 QA \u6a21\u578b\uff0c\u6709\u5b66\u751f\u7684\u5927\u4f5c\u4e1a\u751a\u81f3\u76f4\u63a5\u53d1\u8868\u4e86\u9876\u4f1a\u8bba\u6587\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224n/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs224n/index.html \u8bfe\u7a0b\u89c6\u9891\uff1aB \u7ad9\u641c\u7d22 CS224n \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a http://web.stanford.edu/class/cs224n/index.html \uff0c5 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a + 1 \u4e2a Final Project","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224n/#_3","text":"@PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS224n - GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224w/","text":"CS224w: Machine Learning with Graphs \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6df1\u5ea6\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython, LaTeX \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 Stanford \u7684\u56fe\u795e\u7ecf\u7f51\u7edc\u5165\u95e8\u8bfe\uff0c\u8fd9\u95e8\u8bfe\u6211\u6ca1\u6709\u4e0a\u8fc7\uff0c\u4f46\u4f17\u591a\u505a GNN \u7684\u670b\u53cb\u90fd\u5411\u6211\u529b\u8350\u8fc7\u8fd9\u95e8\u8bfe\uff0c\u60f3\u5fc5 Stanford \u7684\u8bfe\u8d28\u91cf\u8fd8\u662f\u4e00\u5982\u65e2\u5f80\u5730\u6709\u4fdd\u8bc1\u7684\u3002\u53e6\u5916\u5c31\u662f\u8fd9\u95e8\u8bfe\u7684\u6388\u8bfe\u8001\u5e08\u975e\u5e38\u5e74\u8f7b\u5e05\u6c14:) \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs224w/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.youtube.com/watch?v=JAB_plj2rbA \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a http://web.stanford.edu/class/cs224w/ \uff0c6 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a\uff0c3 \u4e2a LaTeX \u4e66\u9762\u4f5c\u4e1a","title":"Stanford CS224w: Machine Learning with Graphs"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224w/#cs224w-machine-learning-with-graphs","text":"","title":"CS224w: Machine Learning with Graphs"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224w/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6df1\u5ea6\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython, LaTeX \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 Stanford \u7684\u56fe\u795e\u7ecf\u7f51\u7edc\u5165\u95e8\u8bfe\uff0c\u8fd9\u95e8\u8bfe\u6211\u6ca1\u6709\u4e0a\u8fc7\uff0c\u4f46\u4f17\u591a\u505a GNN \u7684\u670b\u53cb\u90fd\u5411\u6211\u529b\u8350\u8fc7\u8fd9\u95e8\u8bfe\uff0c\u60f3\u5fc5 Stanford \u7684\u8bfe\u8d28\u91cf\u8fd8\u662f\u4e00\u5982\u65e2\u5f80\u5730\u6709\u4fdd\u8bc1\u7684\u3002\u53e6\u5916\u5c31\u662f\u8fd9\u95e8\u8bfe\u7684\u6388\u8bfe\u8001\u5e08\u975e\u5e38\u5e74\u8f7b\u5e05\u6c14:)","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224w/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs224w/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.youtube.com/watch?v=JAB_plj2rbA \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a http://web.stanford.edu/class/cs224w/ \uff0c6 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a\uff0c3 \u4e2a LaTeX \u4e66\u9762\u4f5c\u4e1a","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS230/","text":"Coursera: Deep Learning \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u673a\u5668\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 \u5434\u6069\u8fbe\u5728 Coursera \u5f00\u8bbe\u7684\u53e6\u4e00\u95e8\u7f51\u7ea2\u8bfe\u7a0b\uff0c\u5b66\u4e60\u8005\u65e0\u6570\uff0c\u582a\u79f0\u5723\u7ecf\u7ea7\u7684\u6df1\u5ea6\u5b66\u4e60\u5165\u95e8\u8bfe\u3002\u6df1\u5165\u6d45\u51fa\u7684\u8bb2\u89e3\uff0c\u773c\u82b1\u7f2d\u4e71\u7684 Project\u3002\u4ece\u6700\u57fa\u7840\u7684\u795e\u7ecf\u7f51\u7edc\uff0c\u5230 CNN, RNN\uff0c\u518d\u5230\u6700\u8fd1\u5927\u70ed\u7684 Transformer\u3002\u5b66\u5b8c\u8fd9\u95e8\u8bfe\uff0c\u4f60\u5c06\u521d\u6b65\u638c\u63e1\u6df1\u5ea6\u5b66\u4e60\u9886\u57df\u5fc5\u5907\u7684\u77e5\u8bc6\u548c\u6280\u80fd\uff0c\u5e76\u4e14\u53ef\u4ee5\u5728 Kaggle \u4e2d\u53c2\u52a0\u81ea\u5df1\u611f\u5174\u8da3\u7684\u6bd4\u8d5b\uff0c\u5728\u5b9e\u8df5\u4e2d\u953b\u70bc\u81ea\u5df1\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://www.coursera.org/specializations/deep-learning \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.coursera.org/specializations/deep-learning \uff0cB\u7ad9\u6709\u642c\u8fd0 \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://www.coursera.org/specializations/deep-learning","title":"Coursera: Deep Learning"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS230/#coursera-deep-learning","text":"","title":"Coursera: Deep Learning"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS230/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u673a\u5668\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 \u5434\u6069\u8fbe\u5728 Coursera 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https://www.coursera.org/specializations/deep-learning \uff0cB\u7ad9\u6709\u642c\u8fd0 \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://www.coursera.org/specializations/deep-learning","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS231/","text":"CS231n: CNN for Visual Recognition \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u673a\u5668\u5b66\u4e60\u57fa\u7840 \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 Stanford \u7684 CV \u5165\u95e8\u8bfe\uff0c\u7531\u8ba1\u7b97\u673a\u9886\u57df\u7684\u5de8\u4f6c\u674e\u98de\u98de\u9662\u58eb\u9886\u8854\u6559\u6388\uff08CV \u9886\u57df\u5212\u65f6\u4ee3\u7684\u8457\u540d\u6570\u636e\u96c6 ImageNet 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Regression\u3001Classification\u3001CNN\u3001Self-Attention\u3001Transformer\u3001GAN\u3001BERT\u3001Anomaly Detection\u3001Explainable AI\u3001Attack\u3001Adaptation\u3001 RL\u3001Compression\u3001Life-Long Learning \u4ee5\u53ca Meta Learning\u3002\u53ef\u8c13\u662f\u5305\u7f57\u4e07\u8c61\uff0c\u80fd\u8ba9\u5b66\u751f\u5bf9\u4e8e\u6df1\u5ea6\u5b66\u4e60\u7684\u7edd\u5927\u591a\u6570\u9886\u57df\u90fd\u6709\u4e00\u5b9a\u4e86\u89e3\uff0c\u4ece\u800c\u53ef\u4ee5\u8fdb\u4e00\u6b65\u9009\u62e9\u60f3\u8981\u6df1\u5165\u7684\u65b9\u5411\u8fdb\u884c\u5b66\u4e60\u3002 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\uff0c\u6bcf\u8282\u8bfe\u7684\u94fe\u63a5\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://speech.ee.ntu.edu.tw/~hylee/ml/2021-spring.html \uff0c15 \u4e2a lab\uff0c\u51e0\u4e4e\u8986\u76d6\u4e86\u4e3b\u6d41\u6df1\u5ea6\u5b66\u4e60\u7684\u6240\u6709\u9886\u57df","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/EE16/","text":"UCB EE16A&B: Designing Information Devices and Systems I&II Descriptions Offered by: UC Berkeley Prerequisites: None Programming Languages: Python Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 150 hours This introductory class for freshmen majoring in electronics at UC Berkeley teaches the fundamentals of circuitry. Through a variety of hands-on labs, students will experience collecting information from the environment through sensors and analyzing it to make predictions. Due to the COVID-19, all labs have remote online version, making them ideal for self-study. Course Resources Course Website: EE16A , EE16B Recordings: EE16A , EE16B Textbooks: EE16A , EE16B Assignments: EE16A , EE16B Personal Resources All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/EE16A - GitHub .","title":"EE16A&B: Designing Information Devices and Systems I&II"},{"location":"en/%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/EE16/#ucb-ee16ab-designing-information-devices-and-systems-iii","text":"","title":"UCB EE16A&B: Designing Information Devices and Systems I&II"},{"location":"en/%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/EE16/#descriptions","text":"Offered by: UC Berkeley Prerequisites: None Programming Languages: Python Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 150 hours This introductory class for freshmen majoring in electronics at UC Berkeley teaches the fundamentals of circuitry. Through a variety of hands-on labs, students will experience collecting information from the environment through sensors and analyzing it to make predictions. Due to the COVID-19, all labs have remote online version, making them ideal for self-study.","title":"Descriptions"},{"location":"en/%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/EE16/#course-resources","text":"Course Website: EE16A , EE16B Recordings: EE16A , EE16B Textbooks: EE16A , EE16B Assignments: EE16A , EE16B","title":"Course Resources"},{"location":"en/%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/EE16/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/EE16A - GitHub .","title":"Personal Resources"},{"location":"en/%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/Signals_and_Systems_AVO/","text":"MIT 6.007 Signals and Systems Descriptions Offered by: MIT Prerequisites: Calculus, Linear Algebra Programming Languages: Matlab Preferred Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: 50-70 hours The name of the instructor: Prof. Alan V. Oppenheim Okay, enough reason to take this class. Course Resources Course Website: https://ocw.mit.edu/resources/res-6-007-signals-and-systems-spring-2011/index.htm Recordings: https://www.bilibili.com/video/BV1CZ4y1j7hs Textbooks: Signals and Systems, 2nd Edition Assignments: https://ocw.mit.edu/resources/res-6-007-signals-and-systems-spring-2011/assignments","title":"MIT 6.007 Signals and Systems"},{"location":"en/%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/Signals_and_Systems_AVO/#mit-6007-signals-and-systems","text":"","title":"MIT 6.007 Signals and Systems"},{"location":"en/%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/Signals_and_Systems_AVO/#descriptions","text":"Offered by: MIT Prerequisites: Calculus, Linear Algebra Programming Languages: Matlab Preferred Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: 50-70 hours The name of the instructor: Prof. Alan V. Oppenheim Okay, enough reason to take this class.","title":"Descriptions"},{"location":"en/%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/Signals_and_Systems_AVO/#course-resources","text":"Course Website: https://ocw.mit.edu/resources/res-6-007-signals-and-systems-spring-2011/index.htm Recordings: https://www.bilibili.com/video/BV1CZ4y1j7hs Textbooks: Signals and Systems, 2nd Edition Assignments: https://ocw.mit.edu/resources/res-6-007-signals-and-systems-spring-2011/assignments","title":"Course Resources"},{"location":"en/%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/signal/","text":"UCB EE120: Signal and Systems Descriptions Offered by: UC Berkeley Prerequisites: CS61A, CS70, Calculus, Linear Algebra Programming Languages: Python Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 100 hours The highlight of this course is the six exciting labs that will allow you to use signals and systems theory to solve practical problems in Python. For example, in lab3 you will implement the FFT algorithm and compare the performance with Numpy's official implementation. In lab4 you will infer the heart rate by processing the video of fingers. Lab5 is the most awesome one where you will reduce the noise in the photos taken by the Hubble telescope to recover the brilliant and bright starry sky. In lab6 you will build a feedback system to stabilize the pole on the cart. Course Resources Course Website: https://inst.eecs.berkeley.edu/~ee120/fa19/ Recordings: refer to the course website Assignments: refer to the course website Personal Resources All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/UCB-EE120 - GitHub","title":"UCB EE120 : Signal and Systems"},{"location":"en/%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/signal/#ucb-ee120-signal-and-systems","text":"","title":"UCB EE120: Signal and Systems"},{"location":"en/%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/signal/#descriptions","text":"Offered by: UC Berkeley Prerequisites: CS61A, CS70, Calculus, Linear Algebra Programming Languages: Python Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 100 hours The highlight of this course is the six exciting labs that will allow you to use signals and systems theory to solve practical problems in Python. For example, in lab3 you will implement the FFT algorithm and compare the performance with Numpy's official implementation. In lab4 you will infer the heart rate by processing the video of fingers. Lab5 is the most awesome one where you will reduce the noise in the photos taken by the Hubble telescope to recover the brilliant and bright starry sky. In lab6 you will build a feedback system to stabilize the pole on the cart.","title":"Descriptions"},{"location":"en/%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/signal/#course-resources","text":"Course Website: https://inst.eecs.berkeley.edu/~ee120/fa19/ Recordings: refer to the course website Assignments: refer to the course website","title":"Course Resources"},{"location":"en/%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/signal/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/UCB-EE120 - GitHub","title":"Personal Resources"},{"location":"en/%E7%A8%8B%E5%BA%8F%E8%AF%AD%E8%A8%80%E8%AE%BE%E8%AE%A1/CS242/","text":"","title":"CS242"},{"location":"en/%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/CS161/","text":"UCB CS161: Computer Security \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS61A, CS61B, CS61C \u7f16\u7a0b\u8bed\u8a00\uff1aC, Go \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 \u4f2f\u514b\u5229\u7684\u8ba1\u7b97\u673a\u7cfb\u7edf\u5b89\u5168\u8bfe\u7a0b\uff0c\u8bfe\u7a0b\u5185\u5bb9\u5206\u4e3a5\u4e2a\u90e8\u5206\uff1a Security principles: how to design a secure system Memory safety: buffer overflow attack Cryptography: symmetric encryption, asymmetric encryption, MAC, digital signature ......... Web: SQL-injection, XSS, XSRF ....... Networking: attacks for each layer \u8fd9\u95e8\u8bfe\u8ba9\u6211\u5370\u8c61\u6700\u4e3a\u6df1\u523b\u7684\u90e8\u5206\u662f Project2\uff0c\u8ba9\u4f60\u7528 Go \u8bed\u8a00\u8bbe\u8ba1\u548c\u5b9e\u73b0\u4e00\u4e2a\u5b89\u5168\u7684\u6587\u4ef6\u5206\u4eab\u7cfb\u7edf\u3002\u6211\u82b1\u4e86\u6574\u6574\u4e09\u5929\u624d\u5b8c\u6210\u4e86\u8fd9\u4e2a\u975e\u5e38\u8650\u7684 Project\uff0c\u603b\u4ee3\u7801\u91cf\u8d85\u8fc7 3k \u884c\u3002\u5728\u8fd9\u6837\u5bc6\u96c6\u578b\u7684\u5f00\u53d1\u8fc7\u7a0b\u4e2d\uff0c\u80fd\u6781\u5927\u5730\u953b\u70bc\u4f60\u8bbe\u8ba1\u548c\u5b9e\u73b0\u4e00\u4e2a\u5b89\u5168\u7cfb\u7edf\u7684\u80fd\u529b\u3002 2020 \u5e74\u590f\u5b63\u5b66\u671f\u7684\u7248\u672c\u5f00\u6e90\u4e86\u8bfe\u7a0b\u5f55\u5f71\uff0c\u5927\u5bb6\u53ef\u4ee5\u5728\u4e0b\u9762\u7684\u8bfe\u7a0b\u7f51\u7ad9\u94fe\u63a5\u91cc\u627e\u5230\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://su20.cs161.org/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a https://textbook.cs161.org/ \u8bfe\u7a0b\u4f5c\u4e1a\uff1a7 \u4e2a\u5728\u7ebf HW + 3 \u4e2a Lab + 3 \u4e2a Project \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/UCB-CS161 - GitHub \u4e2d\u3002","title":"UCB CS161: Computer Security"},{"location":"en/%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/CS161/#ucb-cs161-computer-security","text":"","title":"UCB CS161: Computer Security"},{"location":"en/%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/CS161/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS61A, CS61B, CS61C \u7f16\u7a0b\u8bed\u8a00\uff1aC, Go \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 \u4f2f\u514b\u5229\u7684\u8ba1\u7b97\u673a\u7cfb\u7edf\u5b89\u5168\u8bfe\u7a0b\uff0c\u8bfe\u7a0b\u5185\u5bb9\u5206\u4e3a5\u4e2a\u90e8\u5206\uff1a Security principles: how to design a secure system Memory safety: buffer overflow attack Cryptography: symmetric encryption, asymmetric encryption, MAC, digital signature ......... Web: SQL-injection, XSS, XSRF ....... Networking: attacks for each layer \u8fd9\u95e8\u8bfe\u8ba9\u6211\u5370\u8c61\u6700\u4e3a\u6df1\u523b\u7684\u90e8\u5206\u662f Project2\uff0c\u8ba9\u4f60\u7528 Go \u8bed\u8a00\u8bbe\u8ba1\u548c\u5b9e\u73b0\u4e00\u4e2a\u5b89\u5168\u7684\u6587\u4ef6\u5206\u4eab\u7cfb\u7edf\u3002\u6211\u82b1\u4e86\u6574\u6574\u4e09\u5929\u624d\u5b8c\u6210\u4e86\u8fd9\u4e2a\u975e\u5e38\u8650\u7684 Project\uff0c\u603b\u4ee3\u7801\u91cf\u8d85\u8fc7 3k \u884c\u3002\u5728\u8fd9\u6837\u5bc6\u96c6\u578b\u7684\u5f00\u53d1\u8fc7\u7a0b\u4e2d\uff0c\u80fd\u6781\u5927\u5730\u953b\u70bc\u4f60\u8bbe\u8ba1\u548c\u5b9e\u73b0\u4e00\u4e2a\u5b89\u5168\u7cfb\u7edf\u7684\u80fd\u529b\u3002 2020 \u5e74\u590f\u5b63\u5b66\u671f\u7684\u7248\u672c\u5f00\u6e90\u4e86\u8bfe\u7a0b\u5f55\u5f71\uff0c\u5927\u5bb6\u53ef\u4ee5\u5728\u4e0b\u9762\u7684\u8bfe\u7a0b\u7f51\u7ad9\u94fe\u63a5\u91cc\u627e\u5230\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/CS161/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a https://su20.cs161.org/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a https://textbook.cs161.org/ \u8bfe\u7a0b\u4f5c\u4e1a\uff1a7 \u4e2a\u5728\u7ebf HW + 3 \u4e2a Lab + 3 \u4e2a Project","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/CS161/#_3","text":"@PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/UCB-CS161 - GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/MIT6.858/","text":"MIT 6.858: Computer System Security \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784\uff0c\u5bf9\u8ba1\u7b97\u673a\u7cfb\u7edf\u6709\u521d\u6b65\u4e86\u89e3 \u7f16\u7a0b\u8bed\u8a00\uff1aC, Python \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 MIT \u7684\u8ba1\u7b97\u673a\u7cfb\u7edf\u5b89\u5168\u8bfe\u7a0b\uff0c\u5b9e\u9a8c\u73af\u5883\u662f\u4e00\u4e2a Web Application Zoobar\u3002\u5b66\u751f\u5b66\u4e60\u653b\u9632\u6280\u672f\u5e76\u5e94\u7528\u4e8e\u8be5 Web Application\u3002 Lab 1: you will explore the zoobar web application, and use buffer overflow attacks to break its security properties. Lab 2: you will improve the zoobar web application by using privilege separation, so that if one component is compromised, the adversary doesn't get control over the whole web application. Lab 3: you will build a program analysis tool based on symbolic execution to find bugs in Python code such as the zoobar web application. Lab 4: you will improve the zoobar application against browser attacks. \u8fd9\u4e2a\u8bfe\u6211\u4e3b\u8981\u662f\u505a\u4e86 Lab 3\u3002Lab 3 \u662f\u901a\u8fc7\u6df7\u5408\u7b26\u53f7\u6267\u884c\u6765\u904d\u5386\u7a0b\u5e8f\u7684\u6240\u6709\u5206\u652f\uff0c\u7406\u89e3\u4e86\u7b26\u53f7\u6267\u884c\u7684\u601d\u60f3\u540e Lab \u5e76\u4e0d\u96be\u505a\u3002\u8fd9\u4e2a Lab \u76f4\u89c2\u5c55\u793a\u7b26\u53f7\u6267\u884c\u8fd9\u79cd\u6280\u672f\u7684\u4f7f\u7528\u65b9\u6cd5\u3002 \u8fd9\u4e2a\u8bfe\u7684 Final Project \u662f\u5b9e\u73b0 SecFS \uff0c\u4e00\u4e2a\u8fdc\u7aef\u6587\u4ef6\u7cfb\u7edf\uff0c\u9762\u5bf9\u5b8c\u5168\u4e0d\u53ef\u4fe1\u7684\u670d\u52a1\u5668\uff0c\u63d0\u4f9b\u673a\u5bc6\u6027\u548c\u5b8c\u6574\u6027\u3002\u53c2\u8003\u8bba\u6587\u4e3a SUNDR \u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://css.csail.mit.edu/6.858/2022/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a4 \u4e2a Lab + Final Project / Lab5","title":"MIT 6.858: Computer System Security"},{"location":"en/%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/MIT6.858/#mit-6858-computer-system-security","text":"","title":"MIT 6.858: Computer System Security"},{"location":"en/%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/MIT6.858/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784\uff0c\u5bf9\u8ba1\u7b97\u673a\u7cfb\u7edf\u6709\u521d\u6b65\u4e86\u89e3 \u7f16\u7a0b\u8bed\u8a00\uff1aC, Python \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 MIT \u7684\u8ba1\u7b97\u673a\u7cfb\u7edf\u5b89\u5168\u8bfe\u7a0b\uff0c\u5b9e\u9a8c\u73af\u5883\u662f\u4e00\u4e2a Web Application Zoobar\u3002\u5b66\u751f\u5b66\u4e60\u653b\u9632\u6280\u672f\u5e76\u5e94\u7528\u4e8e\u8be5 Web Application\u3002 Lab 1: you will explore the zoobar web application, and use buffer overflow attacks to break its security properties. Lab 2: you will improve the zoobar web application by using privilege separation, so that if one component is compromised, the adversary doesn't get control over the whole web application. Lab 3: you will build a program analysis tool based on symbolic execution to find bugs in Python code such as the zoobar web application. Lab 4: you will improve the zoobar application against browser attacks. \u8fd9\u4e2a\u8bfe\u6211\u4e3b\u8981\u662f\u505a\u4e86 Lab 3\u3002Lab 3 \u662f\u901a\u8fc7\u6df7\u5408\u7b26\u53f7\u6267\u884c\u6765\u904d\u5386\u7a0b\u5e8f\u7684\u6240\u6709\u5206\u652f\uff0c\u7406\u89e3\u4e86\u7b26\u53f7\u6267\u884c\u7684\u601d\u60f3\u540e Lab \u5e76\u4e0d\u96be\u505a\u3002\u8fd9\u4e2a Lab \u76f4\u89c2\u5c55\u793a\u7b26\u53f7\u6267\u884c\u8fd9\u79cd\u6280\u672f\u7684\u4f7f\u7528\u65b9\u6cd5\u3002 \u8fd9\u4e2a\u8bfe\u7684 Final Project \u662f\u5b9e\u73b0 SecFS \uff0c\u4e00\u4e2a\u8fdc\u7aef\u6587\u4ef6\u7cfb\u7edf\uff0c\u9762\u5bf9\u5b8c\u5168\u4e0d\u53ef\u4fe1\u7684\u670d\u52a1\u5668\uff0c\u63d0\u4f9b\u673a\u5bc6\u6027\u548c\u5b8c\u6574\u6027\u3002\u53c2\u8003\u8bba\u6587\u4e3a SUNDR \u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/MIT6.858/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://css.csail.mit.edu/6.858/2022/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a4 \u4e2a Lab + Final Project / Lab5","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106B_CS106X/","text":"Stanford CS106B/X: Programming Abstractions in C++ Descriptions Offered by: Stanford Prerequisites: CS50/CS106A/CS61A or equivalent Programming Languages: C++ Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: 50-70 hours CS106B/X are advanced programming courses at Stanford. CS106X is more difficult and in-depth than CS106B, but the main content is similar. Based on programming assignments in C++ language, students will develop the ability to solve real-world problems through programming abstraction. It also covers some simple data structures and algorithms, but is generally not as systematic as a specialized data structures course. Resources Course Website: CS106B , CS106X Textbook: https://web.stanford.edu/class/cs106x/res/reader/CS106BX-Reader.pdf Recordings: https://www.bilibili.com/video/BV1G7411k7jG","title":"Stanford CS106B/X"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106B_CS106X/#stanford-cs106bx-programming-abstractions-in-c","text":"","title":"Stanford CS106B/X: Programming Abstractions in C++"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106B_CS106X/#descriptions","text":"Offered by: Stanford Prerequisites: CS50/CS106A/CS61A or equivalent Programming Languages: C++ Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: 50-70 hours CS106B/X are advanced programming courses at Stanford. CS106X is more difficult and in-depth than CS106B, but the main content is similar. Based on programming assignments in C++ language, students will develop the ability to solve real-world problems through programming abstraction. It also covers some simple data structures and algorithms, but is generally not as systematic as a specialized data structures course.","title":"Descriptions"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106B_CS106X/#resources","text":"Course Website: CS106B , CS106X Textbook: https://web.stanford.edu/class/cs106x/res/reader/CS106BX-Reader.pdf Recordings: https://www.bilibili.com/video/BV1G7411k7jG","title":"Resources"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106L/","text":"CS106L: Stanford C++ Programming Descriptions Offered by: Stanford Prerequisites: better if you are already proficient in a programming language Programming Languages: C++ Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 20 hours I've been writing C++ code since freshman year, and it wasn't until I finished this class that I realized the C++ code I was writing was probably just C + cin / cout . This class will dive into a lot of standard C++ features and syntax that will allow you to write quality C++ code. Techniques such as auto binding, uniform initialization, lambda function, move semantics, RAII, etc. have been used repeatedly in my coding career since then and are very useful. It is worth mentioning that in this class, you will implement a HashMap (similar to unordered_map in STL), which almost ties the whole course together and is a great test of coding skills. Especially after the implementation of iterator , I started to understand why Linus is so sarcastic about C/C++, because it is really hard to write correctly. In short, the course is not difficult but very informative which requires you to consolidate repeatedly in later practice. The reason why Stanford offers a single C++ programming class is that many of the subsequent CS courses' projects are based on C++. For example, CS144 Computer Networks and CS143 Compilers. Both of these courses are included in this book. Course Resources Course Website: http://web.stanford.edu/class/cs106l/ Recordings: https://www.youtube.com/channel/UCSqr6y-eaQT_qZJVUm_4QxQ/playlists Textbook: http://web.stanford.edu/class/cs106l/full_course_reader.pdf Assignment1 Download: https://github.com/snme/cs106L-assignment1 Assignment2 Download: https://github.com/snme/cs106L-assignment2 Assignments: refer to the course website Personal Resources All the resources and assignments used by @PKUFlyingPig are maintained in PKUFlyingPig/CS106L - GitHub .","title":"Stanford CS106L: Standard C++ Programming"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106L/#cs106l-stanford-c-programming","text":"","title":"CS106L: Stanford C++ Programming"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106L/#descriptions","text":"Offered by: Stanford Prerequisites: better if you are already proficient in a programming language Programming Languages: C++ Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 20 hours I've been writing C++ code since freshman year, and it wasn't until I finished this class that I realized the C++ code I was writing was probably just C + cin / cout . This class will dive into a lot of standard C++ features and syntax that will allow you to write quality C++ code. Techniques such as auto binding, uniform initialization, lambda function, move semantics, RAII, etc. have been used repeatedly in my coding career since then and are very useful. It is worth mentioning that in this class, you will implement a HashMap (similar to unordered_map in STL), which almost ties the whole course together and is a great test of coding skills. Especially after the implementation of iterator , I started to understand why Linus is so sarcastic about C/C++, because it is really hard to write correctly. In short, the course is not difficult but very informative which requires you to consolidate repeatedly in later practice. The reason why Stanford offers a single C++ programming class is that many of the subsequent CS courses' projects are based on C++. For example, CS144 Computer Networks and CS143 Compilers. Both of these courses are included in this book.","title":"Descriptions"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106L/#course-resources","text":"Course Website: http://web.stanford.edu/class/cs106l/ Recordings: https://www.youtube.com/channel/UCSqr6y-eaQT_qZJVUm_4QxQ/playlists Textbook: http://web.stanford.edu/class/cs106l/full_course_reader.pdf Assignment1 Download: https://github.com/snme/cs106L-assignment1 Assignment2 Download: https://github.com/snme/cs106L-assignment2 Assignments: refer to the course website","title":"Course Resources"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106L/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig are maintained in PKUFlyingPig/CS106L - GitHub .","title":"Personal Resources"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS110L/","text":"CS110L: Safety in Systems Programming Descriptions Offered by: Stanford Prerequisites: basic knowledge about programming and computer system Programming Languages: Rust Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 30 hours In this course, you will learn a fantastic language, Rust. If you have studied C and have some knowledge of systems programming, you should have heard about memory leaks and the danger of pointers, but C's high efficiency makes it impossible to be replaced by other higher-level languages with garbage collection such as Java in system-level programming. Whereas Rust aims to make up for C's lack of security while having competitive efficiency. Therefore, Rust was designed from a system programmer's point of view. By learning Rust, you will learn the principles to write safer and more elegant system code (e.g., operating systems, etc.). The latter part of this course focuses on the topic of concurrency, where you will systematically learn multi-processing, multi-threading, event-driven programming, and several other techniques. In the second project, you will compare the pros and cons of each method. Personally, I find the concept of \"futures\" in Rust fascinating and elegant, and mastering this idea will help you in your following systems-related courses. In addition, Tsinghua University's operating system lab, rCore is based on Rust. You can see the documentation for more details. Course Resources Course Website: https://reberhardt.com/cs110l/spring-2020/ Recordings: https://youtu.be/j7AQrtLevUE Textbook: None Assignments\uff1a6 Labs, 2 Projects, the course website has specific requirements. The projects are quite interesting where you will Implement a GDB-like debugger and a load balancer in Rust. Personal Resources All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/CS110L - GitHub","title":"Stanford CS110L: Safety in Systems Programming"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS110L/#cs110l-safety-in-systems-programming","text":"","title":"CS110L: Safety in Systems Programming"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS110L/#descriptions","text":"Offered by: Stanford Prerequisites: basic knowledge about programming and computer system Programming Languages: Rust Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 30 hours In this course, you will learn a fantastic language, Rust. If you have studied C and have some knowledge of systems programming, you should have heard about memory leaks and the danger of pointers, but C's high efficiency makes it impossible to be replaced by other higher-level languages with garbage collection such as Java in system-level programming. Whereas Rust aims to make up for C's lack of security while having competitive efficiency. Therefore, Rust was designed from a system programmer's point of view. By learning Rust, you will learn the principles to write safer and more elegant system code (e.g., operating systems, etc.). The latter part of this course focuses on the topic of concurrency, where you will systematically learn multi-processing, multi-threading, event-driven programming, and several other techniques. In the second project, you will compare the pros and cons of each method. Personally, I find the concept of \"futures\" in Rust fascinating and elegant, and mastering this idea will help you in your following systems-related courses. In addition, Tsinghua University's operating system lab, rCore is based on Rust. You can see the documentation for more details.","title":"Descriptions"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS110L/#course-resources","text":"Course Website: https://reberhardt.com/cs110l/spring-2020/ Recordings: https://youtu.be/j7AQrtLevUE Textbook: None Assignments\uff1a6 Labs, 2 Projects, the course website has specific requirements. The projects are quite interesting where you will Implement a GDB-like debugger and a load balancer in Rust.","title":"Course Resources"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS110L/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/CS110L - GitHub","title":"Personal Resources"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS50/","text":"CS50: This is CS50x Descriptions Offered by: Harvard Prerequisites: None Programming Languages: C, Python, SQL, HTML, CSS, JavaScript Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: 20 hours This course has been voted the most popular public course by Harvard students for many years. Professor Malan is very passionate in class. I still remember the scene where he tears up the Yellow pages to explain the dichotomy method. Since this is a university-wide public course, the contents are pretty friendly to beginners and even if you already have some programming experience, all the programming assignments are quite exciting and worth a try. Course Resources Course Website: https://cs50.harvard.edu/x/2022/ Recordings: https://cs50.harvard.edu/x/2022/ Assignments: https://cs50.harvard.edu/x/2022/","title":"Harvard CS50: This is CS50x"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS50/#cs50-this-is-cs50x","text":"","title":"CS50: This is CS50x"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS50/#descriptions","text":"Offered by: Harvard Prerequisites: None Programming Languages: C, Python, SQL, HTML, CSS, JavaScript Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: 20 hours This course has been voted the most popular public course by Harvard students for many years. Professor Malan is very passionate in class. I still remember the scene where he tears up the Yellow pages to explain the dichotomy method. Since this is a university-wide public course, the contents are pretty friendly to beginners and even if you already have some programming experience, all the programming assignments are quite exciting and worth a try.","title":"Descriptions"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS50/#course-resources","text":"Course Website: https://cs50.harvard.edu/x/2022/ Recordings: https://cs50.harvard.edu/x/2022/ Assignments: https://cs50.harvard.edu/x/2022/","title":"Course Resources"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS61A/","text":"CS61A: Structure and Interpretation of Computer Programs Descriptions Offered by: UC Berkeley Prerequisites: None Programming Languages: Python, Scheme, SQL Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: 50 hours This is the first course in the Berkeley CS61 series, and my introductory course to Python. The CS61 series is composed of introductory courses to the CS major at Berkeley, where CS61A: Emphasizes abstraction and equips students to use programs to solve real-world problems without focusing on the underlying hardware details. CS61B: Focuses on algorithms and data structures and the construction of large-scale programs, where students combine knowledge of algorithms and data structures with the Java language to build large-scale projects at the thousand-line code level (such as a simple Google Maps, a two-dimensional version of Minecraft). CS61C: Focusing on computer architecture, students will understand how high-level languages (e.g. C) are converted step-by-step into machine-understandable bit strings and executed on CPUs. Students will learn about the RISC-V architecture and implement a CPU on their own by using Logism. CS61B and CS61C are both included in this guidebook. Going back to CS61A, you will note that this is not just a programming language class, but goes deeper into the principles of program construction and operation. Finally you will implement an interpreter for Scheme in Python in Project 4. In addition, abstraction will be a major theme in this class, as you will learn about functional programming, data abstraction, object orientation, etc. to make your code more readable and modular. Of course, learning a programming language is also a big part of this course. You will master three programming languages, Python, Scheme, and SQL, and in learning and comparing them, you will be equiped with the ability to quickly master a new programming language. Note: If you have no prior programming experience at all, getting started with CS61A requires a relatively high level of learning ability and self-discipline. To avoid the frustration of a struggling experience, you may choose a more friendly introductory programming course at first. For example, CS10 at Berkeley or CS50 at Harvard are both good choices. Course Resources Course Website: https://inst.eecs.berkeley.edu/~cs61a/su20/ Recordings: refer to the course website Textbook: http://composingprograms.com/ Assignments: refer to the course website Personal Resources All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/CS61A - GitHub","title":"UCB CS61A: Structure and Interpretation of Computer Programs"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS61A/#cs61a-structure-and-interpretation-of-computer-programs","text":"","title":"CS61A: Structure and Interpretation of Computer Programs"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS61A/#descriptions","text":"Offered by: UC Berkeley Prerequisites: None Programming Languages: Python, Scheme, SQL Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: 50 hours This is the first course in the Berkeley CS61 series, and my introductory course to Python. The CS61 series is composed of introductory courses to the CS major at Berkeley, where CS61A: Emphasizes abstraction and equips students to use programs to solve real-world problems without focusing on the underlying hardware details. CS61B: Focuses on algorithms and data structures and the construction of large-scale programs, where students combine knowledge of algorithms and data structures with the Java language to build large-scale projects at the thousand-line code level (such as a simple Google Maps, a two-dimensional version of Minecraft). CS61C: Focusing on computer architecture, students will understand how high-level languages (e.g. C) are converted step-by-step into machine-understandable bit strings and executed on CPUs. Students will learn about the RISC-V architecture and implement a CPU on their own by using Logism. CS61B and CS61C are both included in this guidebook. Going back to CS61A, you will note that this is not just a programming language class, but goes deeper into the principles of program construction and operation. Finally you will implement an interpreter for Scheme in Python in Project 4. In addition, abstraction will be a major theme in this class, as you will learn about functional programming, data abstraction, object orientation, etc. to make your code more readable and modular. Of course, learning a programming language is also a big part of this course. You will master three programming languages, Python, Scheme, and SQL, and in learning and comparing them, you will be equiped with the ability to quickly master a new programming language. Note: If you have no prior programming experience at all, getting started with CS61A requires a relatively high level of learning ability and self-discipline. To avoid the frustration of a struggling experience, you may choose a more friendly introductory programming course at first. For example, CS10 at Berkeley or CS50 at Harvard are both good choices.","title":"Descriptions"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS61A/#course-resources","text":"Course Website: https://inst.eecs.berkeley.edu/~cs61a/su20/ Recordings: refer to the course website Textbook: http://composingprograms.com/ Assignments: refer to the course website","title":"Course Resources"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS61A/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/CS61A - GitHub","title":"Personal Resources"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/Duke-Coursera-Intro-C/","text":"Introductory C Programming Specialization Descriptions Offered by: Duke Prerequisites: None Programming Languages: C Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 110 hours This is an excellent course which I benefited a lot from. The course teaches fundamental concepts such as frame, stack memory, heap memory, etc. There are great programming assignments to deepen and reinforce your understanding of the hardest part in C, like pointers. The course provides excellent practice in GDB, Valgrind, and the assignments will cover some basic Git exercises. The course instructor recommends using Emacs for homework, so it's a good opportunity to learn Emacs. If you already know how to use Vim, I suggest you use Evil. This way you don't lose the editing capabilities of Vim, and you get to experience the power of Emacs. Having both Emacs and Vim in your kit will increase your efficiency considerably. Emacs' org-mode, smooth integration of GDB, etc., are convenient for developers. It may require payment, but I think it's worth it. Although this is an introductory course, it has both breadth and depth. Course Resources Course Website: https://www.coursera.org/specializations/c-programming Recordings: refer to the course website Textbook: refer to the course website Assignments: refer to the course website Personal Resources All the resources and assignments used by in this course are maintained in Duke Coursera Intro C . Several assignments have not been completed so far for time reasons.","title":"Duke University: Introductory C Programming Specialization"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/Duke-Coursera-Intro-C/#introductory-c-programming-specialization","text":"","title":"Introductory C Programming Specialization"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/Duke-Coursera-Intro-C/#descriptions","text":"Offered by: Duke Prerequisites: None Programming Languages: C Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 110 hours This is an excellent course which I benefited a lot from. The course teaches fundamental concepts such as frame, stack memory, heap memory, etc. There are great programming assignments to deepen and reinforce your understanding of the hardest part in C, like pointers. The course provides excellent practice in GDB, Valgrind, and the assignments will cover some basic Git exercises. The course instructor recommends using Emacs for homework, so it's a good opportunity to learn Emacs. If you already know how to use Vim, I suggest you use Evil. This way you don't lose the editing capabilities of Vim, and you get to experience the power of Emacs. Having both Emacs and Vim in your kit will increase your efficiency considerably. Emacs' org-mode, smooth integration of GDB, etc., are convenient for developers. It may require payment, but I think it's worth it. Although this is an introductory course, it has both breadth and depth.","title":"Descriptions"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/Duke-Coursera-Intro-C/#course-resources","text":"Course Website: https://www.coursera.org/specializations/c-programming Recordings: refer to the course website Textbook: refer to the course website Assignments: refer to the course website","title":"Course Resources"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/Duke-Coursera-Intro-C/#personal-resources","text":"All the resources and assignments used by in this course are maintained in Duke Coursera Intro C . Several assignments have not been completed so far for time reasons.","title":"Personal Resources"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/MIT-Missing-Semester/","text":"MIT: The Missing Semester of Your CS Education Descriptions Offered by: MIT Prerequisites: None Programming Languages: Shell Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: 10 hours Just as the course name indicated, this course will teach the missing things in the university courses. It will cover shell programming, git, vim editor, tmux, ssh, sed, awk and even how to beautify your terminal. Trust me, this will be your first step to become a hacker! Resources Homepage: https://missing.csail.mit.edu/ Records: https://www.youtube.com/playlist?list=PLyzOVJj3bHQuloKGG59rS43e29ro7I57J Assignments: Some exercises after each lecture.","title":"MIT-Missing-Semester"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/MIT-Missing-Semester/#mit-the-missing-semester-of-your-cs-education","text":"","title":"MIT: The Missing Semester of Your CS Education"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/MIT-Missing-Semester/#descriptions","text":"Offered by: MIT Prerequisites: None Programming Languages: Shell Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: 10 hours Just as the course name indicated, this course will teach the missing things in the university courses. It will cover shell programming, git, vim editor, tmux, ssh, sed, awk and even how to beautify your terminal. Trust me, this will be your first step to become a hacker!","title":"Descriptions"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/MIT-Missing-Semester/#resources","text":"Homepage: https://missing.csail.mit.edu/ Records: https://www.youtube.com/playlist?list=PLyzOVJj3bHQuloKGG59rS43e29ro7I57J Assignments: Some exercises after each lecture.","title":"Resources"},{"location":"en/%E7%BC%96%E8%AF%91%E5%8E%9F%E7%90%86/6035/","text":"","title":"6035"},{"location":"en/%E7%BC%96%E8%AF%91%E5%8E%9F%E7%90%86/CS143/","text":"Stanford CS143: Compilers \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784 \u7f16\u7a0b\u8bed\u8a00\uff1aJava \u6216 C++ \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 \u65af\u5766\u798f\u7684\u7f16\u8bd1\u539f\u7406\u8bfe\u7a0b\uff0c\u8bbe\u8ba1\u8005\u5f00\u53d1\u4e86\u4e00\u4e2a 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\u4e2a\u7f16\u7a0b\u4f5c\u4e1a\u5e26\u4f60\u5b9e\u73b0\u4e00\u4e2a\u7f16\u8bd1\u5668 \u8d44\u6e90\u6c47\u603b @skyzluo \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 skyzluo/CS143-Compilers-Stanford - GitHub \u4e2d\u3002","title":"Stanford CS143: Compilers"},{"location":"en/%E7%BC%96%E8%AF%91%E5%8E%9F%E7%90%86/CS143/#stanford-cs143-compilers","text":"","title":"Stanford CS143: Compilers"},{"location":"en/%E7%BC%96%E8%AF%91%E5%8E%9F%E7%90%86/CS143/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784 \u7f16\u7a0b\u8bed\u8a00\uff1aJava \u6216 C++ \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 \u65af\u5766\u798f\u7684\u7f16\u8bd1\u539f\u7406\u8bfe\u7a0b\uff0c\u8bbe\u8ba1\u8005\u5f00\u53d1\u4e86\u4e00\u4e2a 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\u4e2d\u3002\u5bf9\u4e8e\u4f5c\u4e1a\u7684\u5177\u4f53\u5b9e\u73b0\uff0c\u5728\u77e5\u4e4e\u4e0a\u6709\u5f88\u591a\u76f8\u5173\u6587\u7ae0\u8fdb\u884c\u4e86\u7ec6\u81f4\u8bb2\u89e3\u53ef\u4ee5\u53c2\u8003\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E5%9B%BE%E5%BD%A2%E5%AD%A6/GAMES202/","text":"GAMES202 \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUCSB \u5148\u4fee\u8981\u6c42\uff1a\u7ebf\u6027\u4ee3\u6570\uff0c\u9ad8\u7b49\u6570\u5b66\uff0cC++\uff0cGAMES101 \u7f16\u7a0b\u8bed\u8a00\uff1aC++ \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a60 \u5c0f\u65f6 \u5b98\u65b9\u4ecb\u7ecd: \u672c\u8bfe\u7a0b\u5c06\u5168\u9762\u5730\u4ecb\u7ecd\u73b0\u4ee3\u5b9e\u65f6\u6e32\u67d3\u4e2d\u7684\u5173\u952e\u95ee\u9898\u4e0e\u89e3\u51b3\u65b9\u6cd5\u3002\u7531\u4e8e\u5b9e\u65f6\u6e32\u67d3 (>30 FPS) 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\u9664\u4e86\u6700\u65b0\u6700\u5168\u7684\u5185\u5bb9\u4e4b\u5916\uff0c\u672c\u8bfe\u7a0b\u4e0e\u5176\u5b83\u4efb\u4f55\u5b9e\u65f6\u6e32\u67d3\u7684\u6559\u7a0b\u8fd8\u6709\u4e00\u4e2a\u91cd\u8981\u7684\u533a\u522b\uff0c\u90a3\u5c31\u662f\u672c\u8bfe\u7a0b\u4e0d\u4f1a\u8bb2\u6388\u4efb\u4f55\u4e0e\u6e38\u620f\u5f15\u64ce\u7684\u4f7f\u7528\u76f8\u5173\u7684\u5185\u5bb9\uff0c\u5e76\u4e14\u4e0d\u4f1a\u7279\u522b\u5f3a\u8c03\u5177\u4f53\u7684\u7740\u8272\u5668\u5b9e\u73b0\u6280\u672f\uff0c\u800c\u4e3b\u8981\u8bb2\u6388\u5b9e\u65f6\u6e32\u67d3\u80cc\u540e\u7684\u79d1\u5b66\u4e0e\u77e5\u8bc6\u3002\u672c\u8bfe\u7a0b\u7684\u76ee\u6807\u662f\u5728\u4f60\u5b66\u4e60\u5b8c\u8fd9\u95e8\u8bfe\u7684\u65f6\u5019\uff0c\u4f60\u5c06\u6709\u6df1\u539a\u7684\u529f\u5e95\u53bb\u5f00\u53d1\u4e00\u4e2a\u5c5e\u4e8e\u4f60\u81ea\u5df1\u7684\u5b9e\u65f6\u6e32\u67d3\u5f15\u64ce\u3002 \u4f5c\u4e3a GAMES101 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\u7f16\u7a0b\u8bed\u8a00\uff1aC++ \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a60 \u5c0f\u65f6 \u5b98\u65b9\u4ecb\u7ecd: \u672c\u8bfe\u7a0b\u5c06\u5168\u9762\u5730\u4ecb\u7ecd\u73b0\u4ee3\u5b9e\u65f6\u6e32\u67d3\u4e2d\u7684\u5173\u952e\u95ee\u9898\u4e0e\u89e3\u51b3\u65b9\u6cd5\u3002\u7531\u4e8e\u5b9e\u65f6\u6e32\u67d3 (>30 FPS) \u5bf9\u901f\u5ea6\u8981\u6c42\u6781\u9ad8\uff0c\u56e0\u6b64\u672c\u8bfe\u7a0b\u7684\u5173\u6ce8\u70b9\u5c06\u662f\u5728\u82db\u523b\u7684\u65f6\u95f4\u9650\u5236\u4e0b\uff0c\u4eba\u4eec\u5982\u4f55\u6253\u7834\u901f\u5ea6\u4e0e\u8d28\u91cf\u4e4b\u95f4\u7684\u6743\u8861\uff0c\u540c\u65f6\u4fdd\u8bc1\u5b9e\u65f6\u7684\u9ad8\u901f\u5ea6\u4e0e\u7167\u7247\u7ea7\u7684\u771f\u5b9e\u611f\u3002 \u672c\u8bfe\u7a0b\u5c06\u4ee5\u4e13\u9898\u7684\u5f62\u5f0f\u5448\u73b0\uff0c\u8bfe\u7a0b\u5185\u5bb9\u4f1a\u8986\u76d6\u5b66\u672f\u754c\u4e0e\u5de5\u4e1a\u754c\u7684\u524d\u6cbf\u5185\u5bb9\uff0c\u5305\u62ec\uff1a\uff081\uff09\u5b9e\u65f6\u8f6f\u9634\u5f71\u7684\u6e32\u67d3\uff1b\uff082\uff09\u73af\u5883\u5149\u7167\uff1b\uff083\uff09\u57fa\u4e8e\u9884\u8ba1\u7b97\u6216\u65e0\u9884\u8ba1\u7b97\u7684\u5168\u5c40\u5149\u7167\uff1b\uff084\uff09\u57fa\u4e8e\u7269\u7406\u7684\u7740\u8272\u6a21\u578b\u4e0e\u65b9\u6cd5\uff1b\uff085\uff09\u5b9e\u65f6\u5149\u7ebf\u8ffd\u8e2a\uff1b\uff086\uff09\u6297\u952f\u9f7f\u4e0e\u8d85\u91c7\u6837\uff1b\u4ee5\u53ca\u4e00\u4e9b\u5e38\u89c1\u7684\u52a0\u901f\u65b9\u5f0f\u7b49\u7b49\u3002 \u9664\u4e86\u6700\u65b0\u6700\u5168\u7684\u5185\u5bb9\u4e4b\u5916\uff0c\u672c\u8bfe\u7a0b\u4e0e\u5176\u5b83\u4efb\u4f55\u5b9e\u65f6\u6e32\u67d3\u7684\u6559\u7a0b\u8fd8\u6709\u4e00\u4e2a\u91cd\u8981\u7684\u533a\u522b\uff0c\u90a3\u5c31\u662f\u672c\u8bfe\u7a0b\u4e0d\u4f1a\u8bb2\u6388\u4efb\u4f55\u4e0e\u6e38\u620f\u5f15\u64ce\u7684\u4f7f\u7528\u76f8\u5173\u7684\u5185\u5bb9\uff0c\u5e76\u4e14\u4e0d\u4f1a\u7279\u522b\u5f3a\u8c03\u5177\u4f53\u7684\u7740\u8272\u5668\u5b9e\u73b0\u6280\u672f\uff0c\u800c\u4e3b\u8981\u8bb2\u6388\u5b9e\u65f6\u6e32\u67d3\u80cc\u540e\u7684\u79d1\u5b66\u4e0e\u77e5\u8bc6\u3002\u672c\u8bfe\u7a0b\u7684\u76ee\u6807\u662f\u5728\u4f60\u5b66\u4e60\u5b8c\u8fd9\u95e8\u8bfe\u7684\u65f6\u5019\uff0c\u4f60\u5c06\u6709\u6df1\u539a\u7684\u529f\u5e95\u53bb\u5f00\u53d1\u4e00\u4e2a\u5c5e\u4e8e\u4f60\u81ea\u5df1\u7684\u5b9e\u65f6\u6e32\u67d3\u5f15\u64ce\u3002 \u4f5c\u4e3a GAMES101 \u7684\u8fdb\u9636\u8bfe\u7a0b\uff0c\u96be\u5ea6\u6709\u4e00\u5b9a\u7684\u63d0\u5347\uff0c\u4f46\u4e0d\u4f1a\u5f88\u5927\uff0c\u76f8\u4fe1\u5b8c\u6210\u4e86 GAMES101 \u7684\u540c\u5b66\u90fd\u6709\u80fd\u529b\u5b8c\u6210\u8fd9\u95e8\u8bfe\u7a0b\u3002\u6bcf\u4e2a project \u4ee3\u7801\u91cf\u90fd\u4e0d\u4f1a\u5f88\u591a\uff0c\u4f46\u662f\u90fd\u9700\u8981\u4e00\u5b9a\u7684\u601d\u8003\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E5%9B%BE%E5%BD%A2%E5%AD%A6/GAMES202/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a GAMES202 \u8bfe\u7a0b\u89c6\u9891\uff1a bilibili \u8bfe\u7a0b\u6559\u6750\uff1aReal-Time Rendering, 4th edition. \u8bfe\u7a0b\u4f5c\u4e1a\uff1a 5\u4e2aproject","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/CS144/","text":"CS144: Computer Network Introduction Offered by: Stanford Prerequisites: Computer System Fundamentals, CS106L Programming Language: C++ Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 100 hours One of the lecturers of this course is Professor Nick McKeown , a giant in the field of Networking. At the end of each chapter of MOOC, he will interview an executive in the industry or an expert in the academia, which can certainly broaden your horizons. In the projects, you will use C++ to build the entire TCP/IP protocol stack, the IP router, and the ARP protocol step by step from scratch. Finally, you will replace Linux Kernel's protocol stack with your own and use socket programming to communicate with your classmates, which is really amazing and exciting. Resources Course Website: https://cs144.github.io/ Video: https://www.youtube.com/watch?v=r2WZNaFyrbQ&list=PL6RdenZrxrw9inR-IJv-erlOKRHjymxMN Textbook: None Assignments: refer to the course website Reference PKUFlyingPig Lexssama's Blogs huangrt01 kiprey \u5eb7\u5b87PL's Blog doraemonzzz ViXbob's libsponge \u5403\u7740\u571f\u8c46\u5750\u5730\u94c1\u7684\u535a\u5ba2 Smith \u661f\u9065\u89c1 EIMadrigal Joey","title":"Stanford CS144: Computer Network"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/CS144/#cs144-computer-network","text":"","title":"CS144: Computer Network"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/CS144/#introduction","text":"Offered by: Stanford Prerequisites: Computer System Fundamentals, CS106L Programming Language: C++ Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 100 hours One of the lecturers of this course is Professor Nick McKeown , a giant in the field of Networking. At the end of each chapter of MOOC, he will interview an executive in the industry or an expert in the academia, which can certainly broaden your horizons. In the projects, you will use C++ to build the entire TCP/IP protocol stack, the IP router, and the ARP protocol step by step from scratch. Finally, you will replace Linux Kernel's protocol stack with your own and use socket programming to communicate with your classmates, which is really amazing and exciting.","title":"Introduction"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/CS144/#resources","text":"Course Website: https://cs144.github.io/ Video: https://www.youtube.com/watch?v=r2WZNaFyrbQ&list=PL6RdenZrxrw9inR-IJv-erlOKRHjymxMN Textbook: None Assignments: refer to the course website","title":"Resources"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/CS144/#reference","text":"PKUFlyingPig Lexssama's Blogs huangrt01 kiprey \u5eb7\u5b87PL's Blog doraemonzzz ViXbob's libsponge \u5403\u7740\u571f\u8c46\u5750\u5730\u94c1\u7684\u535a\u5ba2 Smith \u661f\u9065\u89c1 EIMadrigal Joey","title":"Reference"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/topdown/","text":"Computer Networking: A Top-Down Approach Descriptions Offered by: UMass Prerequisites: basic knowledge about computer system Programming Languages: None Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 40 hours Computer Networking: A Top-Down Approach is a classic textbook in the field of computer networking. The two authors, Jim Kurose and Keith Ross, have carefully crafted a course website to support the textbook, with lecture recordings, interactive online questions, and WireShark labs for network packet analysis. The only pity is that this course doesn't have hardcore programming assignments, and Stanford's CS144 makes up for that. 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The two authors, Jim Kurose and Keith Ross, have carefully crafted a course website to support the textbook, with lecture recordings, interactive online questions, and WireShark labs for network packet analysis. The only pity is that this course doesn't have hardcore programming assignments, and Stanford's CS144 makes up for that.","title":"Descriptions"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/topdown/#course-resources","text":"Course Website: https://gaia.cs.umass.edu/kurose_ross/index.php Recordings: https://gaia.cs.umass.edu/kurose_ross/lectures.php Textbooks: Computer Networking: A Top-Down Approach Assignments: https://gaia.cs.umass.edu/kurose_ross/wireshark.php","title":"Course Resources"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/topdown/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/Computer-Network-A-Top-Down-Approach - GitHub .","title":"Personal Resources"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/topdown_ustc/","text":"USTC Computer Networking:A Top-Down Approach \u8bfe\u7a0b\u7b80\u4ecb 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you are already proficient in a programming language Programming Languages: Java Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 100 hours The goal of this course is for you to learn how to write high quality code, and what is meant by high quality is to meet the following three targets: Safe from bugs. Correctness (correct behavior right now) and defensiveness (correct behavior in the future) are required in any software we build. Easy to understand. The code has to communicate to future programmers who need to understand it and make changes in it (fixing bugs or adding new features). That future programmer might be you, months or years from now. You\u2019ll be surprised how much you forget if you don\u2019t write it down, and how much it helps your own future self to have a good design. Ready for change. Software always changes. Some designs make it easy to make changes; others require throwing away and rewriting a lot of code. To achieve this, the instructors write a book explaining many of the core principles of software construction and valuable lessons learned from the past. The book covers many practical topics such as how to write comments and specifications, how to design abstract data structures, and many parallel programming caveats. 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Pull Request\uff0c\u4e5f\u6b22\u8fce\u548c\u6211\u90ae\u4ef6\u8054\u7cfb\uff08 zhongyinmin@pku.edu.cn \uff09\u3002","title":"\u4f60\u4e5f\u60f3\u52a0\u5165\u5230\u8d21\u732e\u8005\u7684\u884c\u5217"},{"location":"#_8","text":"\u65b9\u6cd5\u53c2\u89c1\u4ed3\u5e93\u7684 README.md \u3002","title":"\u5173\u4e8e\u4ea4\u6d41\u7fa4\u7684\u5efa\u7acb"},{"location":"CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/","text":"\u4e00\u4e2a\u4ec5\u4f9b\u53c2\u8003\u7684 CS \u5b66\u4e60\u89c4\u5212 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\u6570\u5b66\u57fa\u7840 \u5fae\u79ef\u5206\u4e0e\u7ebf\u6027\u4ee3\u6570 \u4f5c\u4e3a\u5927\u4e00\u65b0\u751f\uff0c\u5b66\u597d\u5fae\u79ef\u5206\u7ebf\u4ee3\u662f\u548c\u5199\u4ee3\u7801\u81f3\u5c11\u540c\u7b49\u91cd\u8981\u7684\u4e8b\u60c5\uff0c\u76f8\u4fe1\u5df2\u7ecf\u6709\u65e0\u6570\u7684\u524d\u4eba\u7ecf\u9a8c\u63d0\u5230\u8fc7\u8fd9\u4e00\u70b9\uff0c\u4f46\u6211\u8fd8\u662f\u8981\u4e0d\u538c\u5176\u70e6\u5730\u518d\u5f3a\u8c03\u4e00\u904d\uff1a\u5b66\u597d\u5fae\u79ef\u5206\u7ebf\u4ee3\u771f\u7684\u5f88\u91cd\u8981\uff01\u4f60\u4e5f\u8bb8\u4f1a\u5410\u69fd\u8fd9\u4e9b\u4e1c\u897f\u5c82\u4e0d\u662f\u8003\u5b8c\u5c31\u5fd8\uff0c\u90a3\u6211\u89c9\u5f97\u4f60\u662f\u5e76\u6ca1\u6709\u628a\u63e1\u4f4f\u5b83\u4eec\u672c\u8d28\uff0c\u5bf9\u5b83\u4eec\u7684\u7406\u89e3\u8fd8\u6ca1\u6709\u8fbe\u5230\u523b\u9aa8\u94ed\u5fc3\u7684\u7a0b\u5ea6\u3002\u5982\u679c\u89c9\u5f97\u8001\u5e08\u8bfe\u4e0a\u8bb2\u7684\u5185\u5bb9\u6666\u6da9\u96be\u61c2\uff0c\u4e0d\u59a8\u53c2\u8003 MIT \u7684 Calculus Course \u548c 18.06: Linear Algebra \u7684\u8bfe\u7a0b notes\uff0c\u81f3\u5c11\u4e8e\u6211\u800c\u8a00\uff0c\u5b83\u5e2e\u52a9\u6211\u6df1\u523b\u7406\u89e3\u4e86\u5fae\u79ef\u5206\u548c\u7ebf\u6027\u4ee3\u6570\u7684\u8bb8\u591a\u672c\u8d28\u3002\u987a\u9053\u518d\u5b89\u5229\u4e00\u4e2a\u6cb9\u7ba1\u6570\u5b66\u7f51\u7ea2 3Blue1Brown \uff0c\u4ed6\u7684\u9891\u9053\u6709\u5f88\u591a\u7528\u751f\u52a8\u5f62\u8c61\u7684\u52a8\u753b\u9610\u91ca\u6570\u5b66\u672c\u8d28\u5185\u6838\u7684\u89c6\u9891\uff0c\u517c\u5177\u6df1\u5ea6\u548c\u5e7f\u5ea6\uff0c\u8d28\u91cf\u975e\u5e38\u9ad8\u3002 \u4fe1\u606f\u8bba\u5165\u95e8 \u4f5c\u4e3a\u8ba1\u7b97\u673a\u7cfb\u7684\u5b66\u751f\uff0c\u53ca\u65e9\u4e86\u89e3\u4e00\u4e9b\u4fe1\u606f\u8bba\u7684\u57fa\u7840\u77e5\u8bc6\uff0c\u6211\u89c9\u5f97\u662f\u5927\u6709\u88e8\u76ca\u7684\u3002\u4f46\u5927\u591a\u4fe1\u606f\u8bba\u8bfe\u7a0b\u90fd\u9762\u5411\u9ad8\u5e74\u7ea7\u672c\u79d1\u751f\u751a\u81f3\u7814\u7a76\u751f\uff0c\u5bf9\u65b0\u624b\u6781\u4e0d\u53cb\u597d\u3002\u800c MIT \u7684 6.050J: Information theory and Entropy \u8fd9\u95e8\u8bfe\u6b63\u662f\u4e3a\u5927\u4e00\u65b0\u751f\u91cf\u8eab\u5b9a\u5236\u7684\uff0c\u51e0\u4e4e\u6ca1\u6709\u5148\u4fee\u8981\u6c42\uff0c\u6db5\u76d6\u4e86\u7f16\u7801\u3001\u538b\u7f29\u3001\u901a\u4fe1\u3001\u4fe1\u606f\u71b5\u7b49\u7b49\u5185\u5bb9\uff0c\u975e\u5e38\u6709\u8da3\u3002 \u6570\u5b66\u8fdb\u9636 \u79bb\u6563\u6570\u5b66\u4e0e\u6982\u7387\u8bba \u96c6\u5408\u8bba\u3001\u56fe\u8bba\u3001\u6982\u7387\u8bba\u7b49\u7b49\u662f\u7b97\u6cd5\u63a8\u5bfc\u4e0e\u8bc1\u660e\u7684\u91cd\u8981\u5de5\u5177\uff0c\u4e5f\u662f\u540e\u7eed\u9ad8\u9636\u6570\u5b66\u8bfe\u7a0b\u7684\u57fa\u7840\u3002\u4f46\u6211\u89c9\u5f97\u8fd9\u7c7b\u8bfe\u7a0b\u7684\u8bb2\u6388\u5f88\u5bb9\u6613\u843d\u5165\u7406\u8bba\u5316\u4e0e\u5f62\u5f0f\u5316\u7684\u7aa0\u81fc\uff0c\u8ba9\u8bfe\u5802\u6210\u4e3a\u5b9a\u7406\u7ed3\u8bba\u7684\u5806\u780c\uff0c\u800c\u65e0\u6cd5\u4f7f\u5b66\u751f\u6df1\u523b\u628a\u63e1\u7406\u8bba\u7684\u672c\u8d28\uff0c\u8fdb\u800c\u9020\u6210\u5b66\u4e86\u5c31\u80cc\uff0c\u8003\u4e86\u5c31\u5fd8\u7684\u602a\u5708\u3002\u5982\u679c\u80fd\u5728\u7406\u8bba\u6559\u5b66\u4e2d\u7a7f\u63d2\u7b97\u6cd5\u8fd0\u7528\u5b9e\u4f8b\uff0c\u5b66\u751f\u5728\u62d3\u5c55\u7b97\u6cd5\u77e5\u8bc6\u7684\u540c\u65f6\u4e5f\u80fd\u7aa5\u89c1\u7406\u8bba\u7684\u529b\u91cf\u548c\u9b45\u529b\u3002 UCB CS70 : discrete Math and probability theory \u548c UCB CS126 : Probability theory \u662f UC Berkeley \u7684\u6982\u7387\u8bba\u8bfe\u7a0b\uff0c\u524d\u8005\u8986\u76d6\u4e86\u79bb\u6563\u6570\u5b66\u548c\u6982\u7387\u8bba\u57fa\u7840\uff0c\u540e\u8005\u5219\u6d89\u53ca\u968f\u673a\u8fc7\u7a0b\u4ee5\u53ca\u6df1\u5165\u7684\u7406\u8bba\u5185\u5bb9\u3002\u4e24\u8005\u90fd\u975e\u5e38\u6ce8\u91cd\u7406\u8bba\u548c\u5b9e\u8df5\u7684\u7ed3\u5408\uff0c\u6709\u4e30\u5bcc\u7684\u7b97\u6cd5\u5b9e\u9645\u8fd0\u7528\u5b9e\u4f8b\uff0c\u540e\u8005\u8fd8\u6709\u5927\u91cf\u7684 Python \u7f16\u7a0b\u4f5c\u4e1a\u6765\u8ba9\u5b66\u751f\u8fd0\u7528\u6982\u7387\u8bba\u7684\u77e5\u8bc6\u89e3\u51b3\u5b9e\u9645\u95ee\u9898\u3002 \u6570\u503c\u5206\u6790 \u4f5c\u4e3a\u8ba1\u7b97\u673a\u7cfb\u7684\u5b66\u751f\uff0c\u57f9\u517b\u8ba1\u7b97\u601d\u7ef4\u662f\u5f88\u91cd\u8981\u7684\uff0c\u5b9e\u9645\u95ee\u9898\u7684\u5efa\u6a21\u3001\u79bb\u6563\u5316\uff0c\u8ba1\u7b97\u673a\u7684\u6a21\u62df\u3001\u5206\u6790\uff0c\u662f\u4e00\u9879\u5f88\u91cd\u8981\u7684\u80fd\u529b\u3002\u800c\u8fd9\u4e24\u5e74\u5f00\u59cb\u98ce\u9761\u7684\uff0c\u7531 MIT \u6253\u9020\u7684 Julia \u7f16\u7a0b\u8bed\u8a00\u4ee5\u5176 C \u4e00\u6837\u7684\u901f\u5ea6\u548c Python \u4e00\u6837\u53cb\u597d\u7684\u8bed\u6cd5\u5728\u6570\u503c\u8ba1\u7b97\u9886\u57df\u6709\u4e00\u7edf\u5929\u4e0b\u4e4b\u52bf\uff0cMIT \u7684\u8bb8\u591a\u6570\u5b66\u8bfe\u7a0b\u4e5f\u5f00\u59cb\u7528 Julia \u4f5c\u4e3a\u6559\u5b66\u5de5\u5177\uff0c\u628a\u8270\u6df1\u7684\u6570\u5b66\u7406\u8bba\u7528\u76f4\u89c2\u6e05\u6670\u7684\u4ee3\u7801\u5c55\u793a\u51fa\u6765\u3002 ComputationalThinking \u662f MIT \u5f00\u8bbe\u7684\u4e00\u95e8\u8ba1\u7b97\u601d\u7ef4\u5165\u95e8\u8bfe\uff0c\u6240\u6709\u8bfe\u7a0b\u5185\u5bb9\u5168\u90e8\u5f00\u6e90\uff0c\u53ef\u4ee5\u5728\u8bfe\u7a0b\u7f51\u7ad9\u76f4\u63a5\u8bbf\u95ee\u3002\u8fd9\u95e8\u8bfe\u5229\u7528 Julia \u7f16\u7a0b\u8bed\u8a00\uff0c\u5728\u56fe\u50cf\u5904\u7406\u3001\u793e\u4f1a\u79d1\u5b66\u4e0e\u6570\u636e\u79d1\u5b66\u3001\u6c14\u5019\u5b66\u5efa\u6a21\u4e09\u4e2a topic \u4e0b\u5e26\u9886\u5b66\u751f\u7406\u89e3\u7b97\u6cd5\u3001\u6570\u5b66\u5efa\u6a21\u3001\u6570\u636e\u5206\u6790\u3001\u4ea4\u4e92\u8bbe\u8ba1\u3001\u56fe\u4f8b\u5c55\u793a\uff0c\u8ba9\u5b66\u751f\u4f53\u9a8c\u8ba1\u7b97\u4e0e\u79d1\u5b66\u7684\u7f8e\u5999\u7ed3\u5408\u3002\u5185\u5bb9\u867d\u7136\u4e0d\u96be\uff0c\u4f46\u7ed9\u6211\u6700\u6df1\u523b\u7684\u611f\u53d7\u5c31\u662f\uff0c\u79d1\u5b66\u7684\u9b45\u529b\u5e76\u4e0d\u662f\u6545\u5f04\u7384\u865a\u7684\u8270\u6df1\u7406\u8bba\uff0c\u4e0d\u662f\u8bd8\u5c48\u8071\u7259\u7684\u672f\u8bed\u884c\u8bdd\uff0c\u800c\u662f\u7528\u76f4\u89c2\u751f\u52a8\u7684\u6848\u4f8b\uff0c\u7528\u7b80\u7ec3\u6df1\u523b\u7684\u8bed\u8a00\uff0c\u8ba9\u6bcf\u4e2a\u666e\u901a\u4eba\u90fd\u80fd\u7406\u89e3\u3002 \u4e0a\u5b8c\u4e0a\u9762\u7684\u4f53\u9a8c\u8bfe\u4e4b\u540e\uff0c\u5982\u679c\u610f\u72b9\u672a\u5c3d\u7684\u8bdd\uff0c\u4e0d\u59a8\u8bd5\u8bd5 MIT \u7684 18.330 : Introduction to numerical analysis \uff0c\u8fd9\u95e8\u8bfe\u7684\u7f16\u7a0b\u4f5c\u4e1a\u540c\u6837\u4f1a\u7528 Julia \u7f16\u7a0b\u8bed\u8a00\uff0c\u4e0d\u8fc7\u96be\u5ea6\u548c\u6df1\u5ea6\u4e0a\u90fd\u4e0a\u4e86\u4e00\u4e2a\u53f0\u9636\u3002\u5185\u5bb9\u6d89\u53ca\u4e86\u6d6e\u70b9\u7f16\u7801\u3001Root finding\u3001\u7ebf\u6027\u7cfb\u7edf\u3001\u5fae\u5206\u65b9\u7a0b\u7b49\u7b49\u65b9\u9762\uff0c\u6574\u95e8\u8bfe\u7684\u4e3b\u65e8\u5c31\u662f\u8ba9\u4f60\u5229\u7528\u79bb\u6563\u5316\u7684\u8ba1\u7b97\u673a\u8868\u793a\u53bb\u4f30\u8ba1\u548c\u903c\u8fd1\u4e00\u4e2a\u6570\u5b66\u4e0a\u8fde\u7eed\u7684\u6982\u5ff5\u3002\u8fd9\u95e8\u8bfe\u7684\u6559\u6388\u8fd8\u4e13\u95e8\u64b0\u5199\u4e86\u4e00\u672c\u914d\u5957\u7684\u5f00\u6e90\u6559\u6750 Fundamentals of Numerical Computation \uff0c\u91cc\u9762\u9644\u6709\u4e30\u5bcc\u7684 Julia \u4ee3\u7801\u5b9e\u4f8b\u548c\u4e25\u8c28\u7684\u516c\u5f0f\u63a8\u5bfc\u3002 \u5982\u679c\u4f60\u8fd8\u610f\u72b9\u672a\u5c3d\u7684\u8bdd\uff0c\u8fd8\u6709 MIT \u7684\u6570\u503c\u5206\u6790\u7814\u7a76\u751f\u8bfe\u7a0b 18.335: Introduction to numerical method \u4f9b\u4f60\u53c2\u8003\u3002 \u5fae\u5206\u65b9\u7a0b \u5982\u679c\u4e16\u95f4\u4e07\u7269\u7684\u8fd0\u52a8\u53d1\u5c55\u90fd\u80fd\u7528\u65b9\u7a0b\u6765\u523b\u753b\u548c\u63cf\u8ff0\uff0c\u8fd9\u662f\u4e00\u4ef6\u591a\u4e48\u9177\u7684\u4e8b\u60c5\u5440\uff01\u867d\u7136\u51e0\u4e4e\u4efb\u4f55\u4e00\u6240\u5b66\u6821\u7684 CS \u57f9\u517b\u65b9\u6848\u4e2d\u90fd\u6ca1\u6709\u5fae\u5206\u65b9\u7a0b\u76f8\u5173\u7684\u5fc5\u4fee\u8bfe\u7a0b\uff0c\u4f46\u6211\u8fd8\u662f\u89c9\u5f97\u638c\u63e1\u5b83\u4f1a\u8d4b\u4e88\u4f60\u4e00\u4e2a\u65b0\u7684\u89c6\u89d2\u6765\u5ba1\u89c6\u8fd9\u4e2a\u4e16\u754c\u3002 \u7531\u4e8e\u5fae\u5206\u65b9\u7a0b\u4e2d\u5f80\u5f80\u4f1a\u7528\u5230\u5f88\u591a\u590d\u53d8\u51fd\u6570\u7684\u77e5\u8bc6\uff0c\u6240\u4ee5\u5927\u5bb6\u53ef\u4ee5\u53c2\u8003 MIT18.04: Complex variables functions \u7684\u8bfe\u7a0b notes \u6765\u8865\u9f50\u5148\u4fee\u77e5\u8bc6\u3002 MIT18.03: differential equations \u4e3b\u8981\u8986\u76d6\u4e86\u5e38\u5fae\u5206\u65b9\u7a0b\u7684\u6c42\u89e3\uff0c\u5728\u6b64\u57fa\u7840\u4e4b\u4e0a MIT18.152: Partial differential equations \u5219\u4f1a\u6df1\u5165\u504f\u5fae\u5206\u65b9\u7a0b\u7684\u5efa\u6a21\u4e0e\u6c42\u89e3\u3002\u638c\u63e1\u4e86\u5fae\u5206\u65b9\u7a0b\u8fd9\u4e00\u6709\u529b\u5de5\u5177\uff0c\u76f8\u4fe1\u5bf9\u4e8e\u4f60\u7684\u5b9e\u9645\u95ee\u9898\u7684\u5efa\u6a21\u80fd\u529b\u4ee5\u53ca\u4ece\u4f17\u591a\u566a\u58f0\u53d8\u91cf\u4e2d\u628a\u63e1\u672c\u8d28\u7684\u76f4\u89c9\u90fd\u4f1a\u6709\u5f88\u5927\u5e2e\u52a9\u3002 \u6570\u5b66\u9ad8\u9636 \u4f5c\u4e3a\u8ba1\u7b97\u673a\u7cfb\u7684\u5b66\u751f\uff0c\u6211\u7ecf\u5e38\u542c\u5230\u6570\u5b66\u65e0\u7528\u8bba\u7684\u8bba\u65ad\uff0c\u5bf9\u6b64\u6211\u4e0d\u6562\u82df\u540c\u4f46\u4e5f\u65e0\u6743\u53cd\u5bf9\uff0c\u4f46\u82e5\u51e1\u4e8b\u90fd\u786c\u8981\u4e89\u51fa\u4e2a\u6709\u7528\u548c\u65e0\u7528\u7684\u533a\u522b\u6765\uff0c\u5012\u4e5f\u7740\u5b9e\u65e0\u8da3\uff0c\u56e0\u6b64\u4e0b\u9762\u8fd9\u4e9b\u9762\u5411\u9ad8\u5e74\u7ea7\u751a\u81f3\u7814\u7a76\u751f\u7684\u6570\u5b66\u8bfe\u7a0b\uff0c\u5927\u5bb6\u6309\u5174\u8da3\u81ea\u53d6\u6240\u9700\u3002 \u51f8\u4f18\u5316 Standford EE364A: Convex Optimization \u4fe1\u606f\u8bba MIT6.441: Information Theory \u5e94\u7528\u7edf\u8ba1\u5b66 MIT18.650: Statistics for Applications \u521d\u7b49\u6570\u8bba MIT18.781: Theory of Numbers \u5bc6\u7801\u5b66 Standford CS255: Cryptography \u7f16\u7a0b\u5165\u95e8 Languages are tools, you choose the right tool to do the right thing. Since there's no universally perfect tool, there's no universally perfect language. Shell MIT-Missing-Semester Python Harvard CS50: This is CS50x UCB CS61A: Structure and Interpretation of Computer Programs C++ Stanford CS106B/X: Programming Abstractions Stanford CS106L: Standard C++ Programming Rust Stanford CS110L: Safety in Systems Programming OCaml Cornell CS3110 textbook: Functional Programming in OCaml \u7535\u5b50\u57fa\u7840 \u7535\u8def\u57fa\u7840 \u4f5c\u4e3a\u8ba1\u7b97\u673a\u7cfb\u7684\u5b66\u751f\uff0c\u4e86\u89e3\u4e00\u4e9b\u57fa\u7840\u7684\u7535\u8def\u77e5\u8bc6\uff0c\u611f\u53d7\u4ece\u4f20\u611f\u5668\u6536\u96c6\u6570\u636e\u5230\u6570\u636e\u5206\u6790\u518d\u5230\u7b97\u6cd5\u9884\u6d4b\u6574\u6761\u6d41\u6c34\u7ebf\uff0c\u5bf9\u4e8e\u540e\u7eed\u77e5\u8bc6\u7684\u5b66\u4e60\u4ee5\u53ca\u8ba1\u7b97\u601d\u7ef4\u7684\u57f9\u517b\u8fd8\u662f\u5f88\u6709\u5e2e\u52a9\u7684\u3002 EE16A&B: Designing Information Devices and Systems I&II \u662f\u4f2f\u514b\u5229 EE \u5b66\u751f\u7684\u5927\u4e00\u5165\u95e8\u8bfe\uff0c\u5176\u4e2d EE16A \u6ce8\u91cd\u901a\u8fc7\u7535\u8def\u4ece\u5b9e\u9645\u73af\u5883\u4e2d\u6536\u96c6\u548c\u5206\u6790\u6570\u636e\uff0c\u800c EE16B \u5219\u4fa7\u91cd\u4ece\u8fd9\u4e9b\u6536\u96c6\u5230\u7684\u6570\u636e\u8fdb\u884c\u5206\u6790\u5e76\u505a\u51fa\u9884\u6d4b\u884c\u4e3a\u3002 \u4fe1\u53f7\u4e0e\u7cfb\u7edf \u4fe1\u53f7\u4e0e\u7cfb\u7edf\u662f\u4e00\u95e8\u6211\u89c9\u5f97\u975e\u5e38\u503c\u5f97\u4e00\u4e0a\u7684\u8bfe\uff0c\u6700\u521d\u5b66\u5b83\u53ea\u662f\u4e3a\u4e86\u6ee1\u8db3\u6211\u5bf9\u5085\u91cc\u53f6\u53d8\u6362\u7684\u597d\u5947\uff0c\u4f46\u5b66\u5b8c\u4e4b\u540e\u6211\u624d\u4e0d\u7981\u611f\u53f9\uff0c\u5085\u7acb\u53f6\u53d8\u6362\u7ed9\u6211\u63d0\u4f9b\u4e86\u4e00\u4e2a\u5168\u65b0\u7684\u89c6\u89d2\u53bb\u770b\u5f85\u8fd9\u4e2a\u4e16\u754c\uff0c\u5c31\u5982\u540c\u5fae\u5206\u65b9\u7a0b\u4e00\u6837\uff0c\u8ba9\u4f60\u6c89\u6d78\u5728\u7528\u6570\u5b66\u53bb\u7cbe\u786e\u63cf\u7ed8\u548c\u523b\u753b\u8fd9\u4e2a\u4e16\u754c\u7684\u4f18\u96c5\u4e0e\u795e\u5947\u4e4b\u4e2d\u3002 MIT 6.003: signal and systems \u63d0\u4f9b\u4e86\u5168\u90e8\u7684\u8bfe\u7a0b\u5f55\u5f71\u3001\u4e66\u9762\u4f5c\u4e1a\u4ee5\u53ca\u7b54\u6848\u3002\u4e5f\u53ef\u4ee5\u53bb\u770b\u8fd9\u95e8\u8bfe\u7684 \u8fdc\u53e4\u7248\u672c \u800c UCB EE120: Signal and Systems \u5173\u4e8e\u5085\u7acb\u53f6\u53d8\u6362\u7684 notes \u5199\u5f97\u975e\u5e38\u597d\uff0c\u5e76\u4e14\u63d0\u4f9b\u4e866 \u4e2a\u975e\u5e38\u6709\u8da3\u7684 Python \u7f16\u7a0b\u4f5c\u4e1a\uff0c\u8ba9\u4f60\u5b9e\u8df5\u4e2d\u8fd0\u7528\u4fe1\u53f7\u4e0e\u7cfb\u7edf\u7684\u7406\u8bba\u4e0e\u7b97\u6cd5\u3002 \u6570\u636e\u7ed3\u6784\u4e0e\u7b97\u6cd5 \u7b97\u6cd5\u662f\u8ba1\u7b97\u673a\u79d1\u5b66\u7684\u6838\u5fc3\uff0c\u4e5f\u662f\u51e0\u4e4e\u4e00\u5207\u4e13\u4e1a\u8bfe\u7a0b\u7684\u57fa\u7840\u3002\u5982\u4f55\u5c06\u5b9e\u9645\u95ee\u9898\u901a\u8fc7\u6570\u5b66\u62bd\u8c61\u8f6c\u5316\u4e3a\u7b97\u6cd5\u95ee\u9898\uff0c\u5e76\u9009\u7528\u5408\u9002\u7684\u6570\u636e\u7ed3\u6784\u5728\u65f6\u95f4\u548c\u5185\u5b58\u5927\u5c0f\u7684\u9650\u5236\u4e0b\u5c06\u5176\u89e3\u51b3\u662f\u7b97\u6cd5\u8bfe\u7684\u6c38\u6052\u4e3b\u9898\u3002\u5982\u679c\u4f60\u53d7\u591f\u4e86\u8001\u5e08\u7684\u7167\u672c\u5ba3\u79d1\uff0c\u90a3\u4e48\u6211\u5f3a\u70c8\u63a8\u8350\u4f2f\u514b\u5229\u7684 UCB CS61B: Data Structures and Algorithms \u548c\u666e\u6797\u65af\u987f\u7684 Coursera: Algorithms I & II \uff0c\u8fd9\u4e24\u95e8\u8bfe\u7684\u90fd\u8bb2\u5f97\u6df1\u5165\u6d45\u51fa\u5e76\u4e14\u4f1a\u6709\u4e30\u5bcc\u4e14\u6709\u8da3\u7684\u7f16\u7a0b\u5b9e\u9a8c\u5c06\u7406\u8bba\u4e0e\u77e5\u8bc6\u7ed3\u5408\u8d77\u6765\u3002\u6b64\u5916\uff0c\u5bf9\u4e00\u4e9b\u66f4\u9ad8\u7ea7\u7684\u7b97\u6cd5\u4ee5\u53ca NP \u95ee\u9898\u611f\u5174\u8da3\u7684\u540c\u5b66\u53ef\u4ee5\u5b66\u4e60\u4f2f\u514b\u5229\u7684\u7b97\u6cd5\u8bbe\u8ba1\u4e0e\u5206\u6790\u8bfe\u7a0b UCB CS170: Efficient Algorithms and Intractable Problems \u3002 \u8f6f\u4ef6\u5de5\u7a0b \u5165\u95e8\u8bfe \u4e00\u4efd\u201c\u80fd\u8dd1\u201d\u7684\u4ee3\u7801\uff0c\u548c\u4e00\u4efd\u9ad8\u8d28\u91cf\u7684\u5de5\u4e1a\u7ea7\u4ee3\u7801\u662f\u6709\u672c\u8d28\u533a\u522b\u7684\u3002\u56e0\u6b64\u6211\u975e\u5e38\u63a8\u8350\u4f4e\u5e74\u7ea7\u7684\u540c\u5b66\u5b66\u4e60\u4e00\u4e0b MIT 6.031: Software Construction \u8fd9\u95e8\u8bfe\uff0c\u5b83\u4f1a\u4ee5 Java \u8bed\u8a00\u4e3a\u57fa\u7840\uff0c\u4ee5\u4e30\u5bcc\u7ec6\u81f4\u7684\u9605\u8bfb\u6750\u6599\u548c\u7cbe\u5fc3\u8bbe\u8ba1\u7684\u7f16\u7a0b\u7ec3\u4e60\u4f20\u6388\u5982\u4f55\u7f16\u5199 \u4e0d\u6613\u51fa bug\u3001\u7b80\u660e\u6613\u61c2\u3001\u6613\u4e8e\u7ef4\u62a4\u4fee\u6539 \u7684\u9ad8\u8d28\u91cf\u4ee3\u7801\u3002\u5927\u5230\u5b8f\u89c2\u6570\u636e\u7ed3\u6784\u8bbe\u8ba1\uff0c\u5c0f\u5230\u5982\u4f55\u5199\u6ce8\u91ca\uff0c\u9075\u5faa\u8fd9\u4e9b\u524d\u4eba\u603b\u7ed3\u7684\u7ec6\u8282\u548c\u7ecf\u9a8c\uff0c\u5bf9\u4e8e\u4f60\u6b64\u540e\u7684\u7f16\u7a0b\u751f\u6daf\u5927\u6709\u88e8\u76ca\u3002 \u4e13\u4e1a\u8bfe \u5f53\u7136\uff0c\u5982\u679c\u4f60\u60f3\u7cfb\u7edf\u6027\u5730\u4e0a\u4e00\u95e8\u8f6f\u4ef6\u5de5\u7a0b\u7684\u8bfe\u7a0b\uff0c\u90a3\u6211\u63a8\u8350\u7684\u662f\u4f2f\u514b\u5229\u7684 UCB CS169: software engineering \u3002\u4f46\u9700\u8981\u63d0\u9192\u7684\u662f\uff0c\u548c\u5927\u591a\u5b66\u6821\uff08\u5305\u62ec\u8d35\u6821\uff09\u7684\u8f6f\u4ef6\u5de5\u7a0b\u8bfe\u7a0b\u4e0d\u540c\uff0c\u8fd9\u95e8\u8bfe\u4e0d\u4f1a\u6d89\u53ca\u4f20\u7edf\u7684 design and document \u6a21\u5f0f\uff0c\u5373\u5f3a\u8c03\u5404\u79cd\u7c7b\u56fe\u3001\u6d41\u7a0b\u56fe\u53ca\u6587\u6863\u8bbe\u8ba1\uff0c\u800c\u662f\u91c7\u7528\u8fd1\u4e9b\u5e74\u6d41\u884c\u8d77\u6765\u7684\u5c0f\u56e2\u961f\u5feb\u901f\u8fed\u4ee3 Agile Develepment \u5f00\u53d1\u6a21\u5f0f\u4ee5\u53ca\u5229\u7528\u4e91\u5e73\u53f0\u7684 Software as a service \u670d\u52a1\u6a21\u5f0f\u3002 \u4f53\u7cfb\u7ed3\u6784 \u5165\u95e8\u8bfe \u4ece\u5c0f\u6211\u5c31\u4e00\u76f4\u542c\u8bf4\uff0c\u8ba1\u7b97\u673a\u7684\u4e16\u754c\u662f\u7531 01 \u6784\u6210\u7684\uff0c\u6211\u4e0d\u7406\u89e3\u4f46\u5927\u53d7\u9707\u64bc\u3002\u5982\u679c\u4f60\u7684\u5185\u5fc3\u4e5f\u6000\u6709\u8fd9\u4efd\u597d\u5947\uff0c\u4e0d\u59a8\u82b1\u4e00\u5230\u4e24\u4e2a\u6708\u7684\u65f6\u95f4\u5b66\u4e60 Coursera: Nand2Tetris \u8fd9\u95e8\u65e0\u95e8\u69db\u7684\u8ba1\u7b97\u673a\u8bfe\u7a0b\u3002\u8fd9\u95e8\u9ebb\u96c0\u867d\u5c0f\u4e94\u810f\u4ff1\u5168\u7684\u8bfe\u7a0b\u4f1a\u4ece 01 \u5f00\u59cb\u8ba9\u4f60\u4eb2\u624b\u9020\u51fa\u4e00\u53f0\u8ba1\u7b97\u673a\uff0c\u5e76\u5728\u4e0a\u9762\u8fd0\u884c\u4fc4\u7f57\u65af\u65b9\u5757\u5c0f\u6e38\u620f\u3002\u4e00\u95e8\u8bfe\u91cc\u6db5\u76d6\u4e86\u7f16\u8bd1\u3001\u865a\u62df\u673a\u3001\u6c47\u7f16\u3001\u4f53\u7cfb\u7ed3\u6784\u3001\u6570\u5b57\u7535\u8def\u3001\u903b\u8f91\u95e8\u7b49\u7b49\u4ece\u4e0a\u81f3\u4e0b\u3001\u4ece\u8f6f\u81f3\u786c\u7684\u5404\u7c7b\u77e5\u8bc6\uff0c\u975e\u5e38\u5168\u9762\u3002\u96be\u5ea6\u4e0a\u4e5f\u662f\u901a\u8fc7\u7cbe\u5fc3\u7684\u8bbe\u8ba1\uff0c\u7565\u53bb\u4e86\u4f17\u591a\u73b0\u4ee3\u8ba1\u7b97\u673a\u590d\u6742\u7684\u7ec6\u8282\uff0c\u63d0\u53d6\u51fa\u4e86\u6700\u6838\u5fc3\u672c\u8d28\u7684\u4e1c\u897f\uff0c\u529b\u56fe\u8ba9\u6bcf\u4e2a\u4eba\u90fd\u80fd\u7406\u89e3\u3002\u5728\u4f4e\u5e74\u7ea7\uff0c\u5982\u679c\u5c31\u80fd\u4ece\u5b8f\u89c2\u4e0a\u5efa\u7acb\u5bf9\u6574\u4e2a\u8ba1\u7b97\u673a\u4f53\u7cfb\u7684\u9e1f\u77b0\u56fe\uff0c\u662f\u5927\u6709\u88e8\u76ca\u7684\u3002 \u4e13\u4e1a\u8bfe \u5f53\u7136\uff0c\u5982\u679c\u60f3\u6df1\u5165\u73b0\u4ee3\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784\u7684\u590d\u6742\u7ec6\u8282\uff0c\u8fd8\u5f97\u4e0a\u4e00\u95e8\u5927\u5b66\u672c\u79d1\u96be\u5ea6\u7684\u8bfe\u7a0b UCB CS61C: Great Ideas in Computer Architecture \u3002UC Berkeley \u4f5c\u4e3a RISC-V \u67b6\u6784\u7684\u53d1\u6e90\u5730\uff0c\u5728\u4f53\u7cfb\u7ed3\u6784\u9886\u57df\u7b97\u5f97\u4e0a\u9996\u5c48\u4e00\u6307\u3002\u5176\u8bfe\u7a0b\u975e\u5e38\u6ce8\u91cd\u5b9e\u8df5\uff0c\u4f60\u4f1a\u5728 Project \u4e2d\u624b\u5199\u6c47\u7f16\u6784\u9020\u795e\u7ecf\u7f51\u7edc\uff0c\u4ece\u96f6\u5f00\u59cb\u642d\u5efa\u4e00\u4e2a CPU\uff0c\u8fd9\u4e9b\u5b9e\u8df5\u90fd\u4f1a\u8ba9\u4f60\u5bf9\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784\u6709\u66f4\u4e3a\u6df1\u5165\u7684\u7406\u89e3\uff0c\u800c\u4e0d\u662f\u4ec5\u505c\u7559\u4e8e\u201c\u53d6\u6307\u8bd1\u7801\u6267\u884c\u8bbf\u5b58\u5199\u56de\u201d\u7684\u5355\u8c03\u80cc\u8bf5\u91cc\u3002 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MIT6.033: System Engineering \u662f MIT \u7684\u7cfb\u7edf\u5165\u95e8\u8bfe\uff0c\u4e3b\u9898\u6d89\u53ca\u4e86\u64cd\u4f5c\u7cfb\u7edf\u3001\u7f51\u7edc\u3001\u5206\u5e03\u5f0f\u548c\u7cfb\u7edf\u5b89\u5168\uff0c\u9664\u4e86\u77e5\u8bc6\u70b9\u7684\u4f20\u6388\u5916\uff0c\u8fd9\u95e8\u8bfe\u8fd8\u4f1a\u8bb2\u6388\u4e00\u4e9b\u5199\u4f5c\u548c\u8868\u8fbe\u4e0a\u7684\u6280\u5de7\uff0c\u8ba9\u4f60\u5b66\u4f1a\u5982\u4f55\u8bbe\u8ba1\u5e76\u5411\u522b\u4eba\u4ecb\u7ecd\u548c\u5206\u6790\u81ea\u5df1\u7684\u7cfb\u7edf\u3002\u8fd9\u672c\u4e66\u914d\u5957\u7684\u6559\u6750 Principles of Computer System Design: An Introduction \u4e5f\u5199\u5f97\u975e\u5e38\u597d\uff0c\u63a8\u8350\u5927\u5bb6\u9605\u8bfb\u3002 CMU 15-213: Introduction to Computer System \u662f CMU 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\u5219\u4fa7\u91cd\u4ece\u8fd9\u4e9b\u6536\u96c6\u5230\u7684\u6570\u636e\u8fdb\u884c\u5206\u6790\u5e76\u505a\u51fa\u9884\u6d4b\u884c\u4e3a\u3002","title":"\u7535\u8def\u57fa\u7840"},{"location":"CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_22","text":"\u4fe1\u53f7\u4e0e\u7cfb\u7edf\u662f\u4e00\u95e8\u6211\u89c9\u5f97\u975e\u5e38\u503c\u5f97\u4e00\u4e0a\u7684\u8bfe\uff0c\u6700\u521d\u5b66\u5b83\u53ea\u662f\u4e3a\u4e86\u6ee1\u8db3\u6211\u5bf9\u5085\u91cc\u53f6\u53d8\u6362\u7684\u597d\u5947\uff0c\u4f46\u5b66\u5b8c\u4e4b\u540e\u6211\u624d\u4e0d\u7981\u611f\u53f9\uff0c\u5085\u7acb\u53f6\u53d8\u6362\u7ed9\u6211\u63d0\u4f9b\u4e86\u4e00\u4e2a\u5168\u65b0\u7684\u89c6\u89d2\u53bb\u770b\u5f85\u8fd9\u4e2a\u4e16\u754c\uff0c\u5c31\u5982\u540c\u5fae\u5206\u65b9\u7a0b\u4e00\u6837\uff0c\u8ba9\u4f60\u6c89\u6d78\u5728\u7528\u6570\u5b66\u53bb\u7cbe\u786e\u63cf\u7ed8\u548c\u523b\u753b\u8fd9\u4e2a\u4e16\u754c\u7684\u4f18\u96c5\u4e0e\u795e\u5947\u4e4b\u4e2d\u3002 MIT 6.003: signal and systems 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\u7684\u9ad8\u8d28\u91cf\u4ee3\u7801\u3002\u5927\u5230\u5b8f\u89c2\u6570\u636e\u7ed3\u6784\u8bbe\u8ba1\uff0c\u5c0f\u5230\u5982\u4f55\u5199\u6ce8\u91ca\uff0c\u9075\u5faa\u8fd9\u4e9b\u524d\u4eba\u603b\u7ed3\u7684\u7ec6\u8282\u548c\u7ecf\u9a8c\uff0c\u5bf9\u4e8e\u4f60\u6b64\u540e\u7684\u7f16\u7a0b\u751f\u6daf\u5927\u6709\u88e8\u76ca\u3002","title":"\u5165\u95e8\u8bfe"},{"location":"CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_26","text":"\u5f53\u7136\uff0c\u5982\u679c\u4f60\u60f3\u7cfb\u7edf\u6027\u5730\u4e0a\u4e00\u95e8\u8f6f\u4ef6\u5de5\u7a0b\u7684\u8bfe\u7a0b\uff0c\u90a3\u6211\u63a8\u8350\u7684\u662f\u4f2f\u514b\u5229\u7684 UCB CS169: software engineering \u3002\u4f46\u9700\u8981\u63d0\u9192\u7684\u662f\uff0c\u548c\u5927\u591a\u5b66\u6821\uff08\u5305\u62ec\u8d35\u6821\uff09\u7684\u8f6f\u4ef6\u5de5\u7a0b\u8bfe\u7a0b\u4e0d\u540c\uff0c\u8fd9\u95e8\u8bfe\u4e0d\u4f1a\u6d89\u53ca\u4f20\u7edf\u7684 design and document 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UCB CS61C: Great Ideas in Computer Architecture \u3002UC Berkeley \u4f5c\u4e3a RISC-V \u67b6\u6784\u7684\u53d1\u6e90\u5730\uff0c\u5728\u4f53\u7cfb\u7ed3\u6784\u9886\u57df\u7b97\u5f97\u4e0a\u9996\u5c48\u4e00\u6307\u3002\u5176\u8bfe\u7a0b\u975e\u5e38\u6ce8\u91cd\u5b9e\u8df5\uff0c\u4f60\u4f1a\u5728 Project \u4e2d\u624b\u5199\u6c47\u7f16\u6784\u9020\u795e\u7ecf\u7f51\u7edc\uff0c\u4ece\u96f6\u5f00\u59cb\u642d\u5efa\u4e00\u4e2a 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Systems: A Programmer's Perspective [ \u8c46\u74e3 ] Principles of Computer System Design: An Introduction [ \u8c46\u74e3 ] \u64cd\u4f5c\u7cfb\u7edf \u73b0\u4ee3\u64cd\u4f5c\u7cfb\u7edf: \u539f\u7406\u4e0e\u5b9e\u73b0 [ \u8c46\u74e3 ] Operating Systems: Three Easy Pieces [ \u8c46\u74e3 ] Modern Operating Systems [ \u8c46\u74e3 ] Operating Systems: Principles and Practice [ \u8c46\u74e3 ] \u8ba1\u7b97\u673a\u7f51\u7edc Computer Networks: A Systems Approach [ \u8c46\u74e3 ] Computer Networking: A Top-Down Approach [ \u8c46\u74e3 ] \u5206\u5e03\u5f0f\u7cfb\u7edf Patterns of Distributed System (Blog) Distributed Systems for Fun and Profit (Blog) Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems [ \u8c46\u74e3 ] \u6570\u636e\u5e93\u7cfb\u7edf Architecture of a Database System [ \u8c46\u74e3 ] Readings in Database Systems [ \u8c46\u74e3 ] Database System Concepts [ \u8c46\u74e3 ] \u7f16\u8bd1\u539f\u7406 Engineering a Compiler [ \u8c46\u74e3 ] Compilers: Principles, Techniques, and Tools [ \u8c46\u74e3 ] \u8ba1\u7b97\u673a\u7f16\u7a0b\u8bed\u8a00 \u8ba1\u7b97\u673a\u7a0b\u5e8f\u7684\u6784\u9020\u548c\u89e3\u91ca [ \u8c46\u74e3 ] Essentials of Programming Languages [ \u8c46\u74e3 ] Practical Foundations for Programming Languages [ \u8c46\u74e3 ] Software Foundations [ \u8c46\u74e3 ] [ \u5317\u5927\u76f8\u5173\u8bfe\u7a0b ] Types and Programming Languages [ \u8c46\u74e3 ] [ \u5317\u5927\u76f8\u5173\u8bfe\u7a0b ] \u4f53\u7cfb\u7ed3\u6784 \u8d85\u6807\u91cf\u5904\u7406\u5668\u8bbe\u8ba1: Superscalar RISC Processor Design [ \u8c46\u74e3 ] Computer Organization and Design RISC-V Edition [ \u8c46\u74e3 ] Computer Organization and Design: The Hardware/Software Interface [ \u8c46\u74e3 ] Computer Architecture: A Quantitative Approach [ \u8c46\u74e3 ] \u7406\u8bba\u8ba1\u7b97\u673a\u79d1\u5b66 Introduction to the Theory of Computation [ \u8c46\u74e3 ] \u5bc6\u7801\u5b66 Cryptography Engineering: Design Principles and Practical Applications [ \u8c46\u74e3 ] Introduction to Modern Cryptography [ \u8c46\u74e3 ] \u9006\u5411\u5de5\u7a0b \u9006\u5411\u5de5\u7a0b\u6838\u5fc3\u539f\u7406 [ \u8c46\u74e3 ] \u52a0\u5bc6\u4e0e\u89e3\u5bc6 [ \u8c46\u74e3 ] \u8ba1\u7b97\u673a\u56fe\u5f62\u5b66 Monte Carlo theory, methods and examples Advanced Global Illumination [ \u8c46\u74e3 ] Fundamentals of Computer Graphics [ \u8c46\u74e3 ] Fluid Simulation for Computer Graphics [ \u8c46\u74e3 ] Physically Based Rendering: From Theory To Implementation [ \u8c46\u74e3 ] Real-Time Rendering [ \u8c46\u74e3 ] \u6e38\u620f\u5f15\u64ce \u6e38\u620f\u7f16\u7a0b\u6a21\u5f0f: Game Programming Patterns [ \u8c46\u74e3 ] \u5b9e\u65f6\u78b0\u649e\u68c0\u6d4b\u7b97\u6cd5\u6280\u672f [ \u8c46\u74e3 ] Game AI Pro Series [ \u8c46\u74e3 ] Artificial Intelligence for Games [ \u8c46\u74e3 ] Game Engine Architecture [ \u8c46\u74e3 ] Game Programming Gems Series [ \u8c46\u74e3 ] \u8f6f\u4ef6\u5de5\u7a0b Software Engineering at Google [ \u8c46\u74e3 ] \u8bbe\u8ba1\u6a21\u5f0f \u8bbe\u8ba1\u6a21\u5f0f: \u53ef\u590d\u7528\u9762\u5411\u5bf9\u8c61\u8f6f\u4ef6\u7684\u57fa\u7840 [ \u8c46\u74e3 ] \u5927\u8bdd\u8bbe\u8ba1\u6a21\u5f0f [ \u8c46\u74e3 ] Head First \u8bbe\u8ba1\u6a21\u5f0f [ \u8c46\u74e3 ] \u6df1\u5ea6\u5b66\u4e60 \u52a8\u624b\u5b66\u6df1\u5ea6\u5b66\u4e60 [ \u8c46\u74e3 ] \u795e\u7ecf\u7f51\u7edc\u4e0e\u6df1\u5ea6\u5b66\u4e60 [ \u8c46\u74e3 ] \u6df1\u5ea6\u5b66\u4e60\u5165\u95e8 [ \u8c46\u74e3 ] \u7b80\u5355\u7c97\u66b4 TensorFlow 2 (Tutorial) Speech and Language Processing [ \u8c46\u74e3 ] \u8ba1\u7b97\u673a\u89c6\u89c9 Multiple View Geometry in Computer Vision [ \u8c46\u74e3 ] \u673a\u5668\u4eba Probabilistic Robotics [ \u8c46\u74e3 ] \u9762\u8bd5 \u5251\u6307 Offer\uff1a\u540d\u4f01\u9762\u8bd5\u5b98\u7cbe\u8bb2\u5178\u578b\u7f16\u7a0b\u9898 [ \u8c46\u74e3 ] Cracking The Coding Interview [ \u8c46\u74e3 ]","title":"\u597d\u4e66\u63a8\u8350"},{"location":"%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_1","text":"\u7531\u4e8e\u7248\u6743\u539f\u56e0\uff0c\u4e0b\u9762\u5217\u4e3e\u7684\u56fe\u4e66\u4e2d\u9664\u4e86\u5f00\u6e90\u8d44\u6e90\u63d0\u4f9b\u4e86\u94fe\u63a5\uff0c\u5176\u4ed6\u7684\u8d44\u6e90\u8bf7\u5927\u5bb6\u81ea\u884c\u901a\u8fc7 libgen \u6216 z-lib \u67e5\u627e\u3002","title":"\u597d\u4e66\u63a8\u8350"},{"location":"%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_2","text":"Free Programming Books : \u5f00\u6e90\u7f16\u7a0b\u4e66\u7c4d\u8d44\u6e90\u6c47\u603b CS Textbook Recommendations : \u8ba1\u7b97\u673a\u79d1\u5b66\u65b9\u5411\u63a8\u8350\u6559\u6750\u5217\u8868 C Book Guide and List : C\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868 C++ Book Guide and List : C++\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868 Python Book Guide and List : Python\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_3","text":"Computer Systems: A Programmer's Perspective [ \u8c46\u74e3 ] Principles of Computer System Design: An Introduction [ \u8c46\u74e3 ]","title":"\u7cfb\u7edf\u5165\u95e8"},{"location":"%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_4","text":"\u73b0\u4ee3\u64cd\u4f5c\u7cfb\u7edf: \u539f\u7406\u4e0e\u5b9e\u73b0 [ \u8c46\u74e3 ] Operating Systems: Three Easy Pieces [ \u8c46\u74e3 ] Modern Operating Systems [ \u8c46\u74e3 ] Operating Systems: Principles and Practice [ \u8c46\u74e3 ]","title":"\u64cd\u4f5c\u7cfb\u7edf"},{"location":"%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_5","text":"Computer Networks: A Systems Approach [ \u8c46\u74e3 ] Computer Networking: A Top-Down Approach [ \u8c46\u74e3 ]","title":"\u8ba1\u7b97\u673a\u7f51\u7edc"},{"location":"%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_6","text":"Patterns of Distributed System (Blog) Distributed Systems for Fun and Profit (Blog) Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems [ \u8c46\u74e3 ]","title":"\u5206\u5e03\u5f0f\u7cfb\u7edf"},{"location":"%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_7","text":"Architecture of a Database System [ \u8c46\u74e3 ] Readings in Database Systems [ \u8c46\u74e3 ] Database System Concepts [ \u8c46\u74e3 ]","title":"\u6570\u636e\u5e93\u7cfb\u7edf"},{"location":"%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_8","text":"Engineering a Compiler [ \u8c46\u74e3 ] Compilers: Principles, Techniques, and Tools [ \u8c46\u74e3 ]","title":"\u7f16\u8bd1\u539f\u7406"},{"location":"%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_9","text":"\u8ba1\u7b97\u673a\u7a0b\u5e8f\u7684\u6784\u9020\u548c\u89e3\u91ca [ \u8c46\u74e3 ] Essentials of Programming Languages [ \u8c46\u74e3 ] Practical Foundations for Programming Languages [ \u8c46\u74e3 ] Software Foundations [ \u8c46\u74e3 ] [ \u5317\u5927\u76f8\u5173\u8bfe\u7a0b ] Types and Programming Languages [ \u8c46\u74e3 ] [ \u5317\u5927\u76f8\u5173\u8bfe\u7a0b ]","title":"\u8ba1\u7b97\u673a\u7f16\u7a0b\u8bed\u8a00"},{"location":"%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_10","text":"\u8d85\u6807\u91cf\u5904\u7406\u5668\u8bbe\u8ba1: Superscalar RISC Processor Design [ \u8c46\u74e3 ] Computer Organization and Design RISC-V Edition [ \u8c46\u74e3 ] Computer Organization and Design: The Hardware/Software Interface [ \u8c46\u74e3 ] Computer Architecture: A Quantitative Approach [ \u8c46\u74e3 ]","title":"\u4f53\u7cfb\u7ed3\u6784"},{"location":"%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_11","text":"Introduction to the Theory of Computation [ \u8c46\u74e3 ]","title":"\u7406\u8bba\u8ba1\u7b97\u673a\u79d1\u5b66"},{"location":"%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_12","text":"Cryptography Engineering: Design Principles and Practical Applications [ \u8c46\u74e3 ] Introduction to Modern Cryptography [ \u8c46\u74e3 ]","title":"\u5bc6\u7801\u5b66"},{"location":"%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_13","text":"\u9006\u5411\u5de5\u7a0b\u6838\u5fc3\u539f\u7406 [ \u8c46\u74e3 ] \u52a0\u5bc6\u4e0e\u89e3\u5bc6 [ \u8c46\u74e3 ]","title":"\u9006\u5411\u5de5\u7a0b"},{"location":"%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_14","text":"Monte Carlo theory, methods and examples Advanced Global Illumination [ \u8c46\u74e3 ] Fundamentals of Computer Graphics [ \u8c46\u74e3 ] Fluid Simulation for Computer Graphics [ \u8c46\u74e3 ] Physically Based Rendering: From Theory To Implementation [ \u8c46\u74e3 ] Real-Time Rendering [ \u8c46\u74e3 ]","title":"\u8ba1\u7b97\u673a\u56fe\u5f62\u5b66"},{"location":"%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_15","text":"\u6e38\u620f\u7f16\u7a0b\u6a21\u5f0f: Game Programming Patterns [ \u8c46\u74e3 ] \u5b9e\u65f6\u78b0\u649e\u68c0\u6d4b\u7b97\u6cd5\u6280\u672f [ \u8c46\u74e3 ] Game AI Pro Series [ \u8c46\u74e3 ] Artificial Intelligence for Games [ \u8c46\u74e3 ] Game Engine Architecture [ \u8c46\u74e3 ] Game Programming Gems Series [ \u8c46\u74e3 ]","title":"\u6e38\u620f\u5f15\u64ce"},{"location":"%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_16","text":"Software Engineering at Google [ \u8c46\u74e3 ]","title":"\u8f6f\u4ef6\u5de5\u7a0b"},{"location":"%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_17","text":"\u8bbe\u8ba1\u6a21\u5f0f: \u53ef\u590d\u7528\u9762\u5411\u5bf9\u8c61\u8f6f\u4ef6\u7684\u57fa\u7840 [ \u8c46\u74e3 ] \u5927\u8bdd\u8bbe\u8ba1\u6a21\u5f0f [ \u8c46\u74e3 ] Head First \u8bbe\u8ba1\u6a21\u5f0f [ \u8c46\u74e3 ]","title":"\u8bbe\u8ba1\u6a21\u5f0f"},{"location":"%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_18","text":"\u52a8\u624b\u5b66\u6df1\u5ea6\u5b66\u4e60 [ \u8c46\u74e3 ] \u795e\u7ecf\u7f51\u7edc\u4e0e\u6df1\u5ea6\u5b66\u4e60 [ \u8c46\u74e3 ] \u6df1\u5ea6\u5b66\u4e60\u5165\u95e8 [ \u8c46\u74e3 ] \u7b80\u5355\u7c97\u66b4 TensorFlow 2 (Tutorial) Speech and Language Processing [ \u8c46\u74e3 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\u7684\u5b66\u4e60\u8d44\u6599\u6d69\u5982\u70df\u6d77\uff0c\u4f46\u638c\u63e1\u5b83\u6700\u597d\u7684\u65b9\u5f0f\u8fd8\u662f\u5c06\u5b83\u7528\u5728\u65e5\u5e38\u7684\u5f00\u53d1\u8fc7\u7a0b\u4e2d\uff0c\u800c\u4e0d\u662f\u4e00\u4e0a\u6765\u5c31\u53bb\u5b66\u5404\u79cd\u82b1\u91cc\u80e1\u54e8\u7684\u9ad8\u7ea7 Vim \u6280\u5de7\u3002\u4e2a\u4eba\u63a8\u8350\u7684\u5b66\u4e60\u8def\u7ebf\u5982\u4e0b\uff1a \u5148\u9605\u8bfb \u8fd9\u7bc7 tutorial \uff0c\u638c\u63e1\u57fa\u672c\u7684 Vim \u6982\u5ff5\u548c\u4f7f\u7528\u65b9\u5f0f\u3002 \u7528 Vim \u81ea\u5e26\u7684 vimtutor \u8fdb\u884c\u7ec3\u4e60\uff0c\u5b89\u88c5\u5b8c Vim \u4e4b\u540e\u76f4\u63a5\u5728\u547d\u4ee4\u884c\u91cc\u8f93\u5165 vimtutor \u5373\u53ef\u8fdb\u5165\u7ec3\u4e60\u7a0b\u5e8f\u3002 \u6700\u540e\u5c31\u662f\u5f3a\u8feb\u81ea\u5df1\u4f7f\u7528 Vim \u8fdb\u884c\u5f00\u53d1\uff0cIDE \u91cc\u53ef\u4ee5\u5b89\u88c5 Vim \u63d2\u4ef6\u3002 \u7b49\u4f60\u5b8c\u5168\u9002\u5e94 Vim \u4e4b\u540e\u65b0\u7684\u4e16\u754c\u4fbf\u5411\u4f60\u655e\u5f00\u4e86\u5927\u95e8\uff0c\u4f60\u53ef\u4ee5\u6309\u9700\u914d\u7f6e\u81ea\u5df1\u7684 Vim\uff08\u4fee\u6539 .vimrc \u6587\u4ef6\uff09\uff0c\u7f51\u4e0a\u6709\u6570\u4e0d\u80dc\u6570\u7684\u8d44\u6e90\u53ef\u4ee5\u501f\u9274\u3002 \u63a8\u8350\u53c2\u8003\u8d44\u6599 Neil, Drew. Practical Vim: Edit Text at the Speed of Thought. N.p., Pragmatic Bookshelf, 2015. Neil, Drew. Modern Vim: Craft Your Development Environment with Vim 8 and Neovim. United States, Pragmatic Bookshelf.","title":"Vim"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Vim/#vim","text":"","title":"Vim"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Vim/#vim_1","text":"\u5728\u6211\u770b\u6765 Vim \u7f16\u8f91\u5668\u6709\u5982\u4e0b\u7684\u597d\u5904\uff1a \u8ba9\u4f60\u7684\u6574\u4e2a\u5f00\u53d1\u8fc7\u7a0b\u624b\u6307\u4e0d\u9700\u8981\u79bb\u5f00\u952e\u76d8\uff0c\u800c\u4e14\u5149\u6807\u7684\u79fb\u52a8\u4e0d\u9700\u8981\u65b9\u5411\u952e\u4f7f\u5f97\u4f60\u7684\u624b\u6307\u4e00\u76f4\u5904\u5728\u6253\u5b57\u7684\u6700\u4f73\u4f4d\u7f6e\u3002 \u65b9\u4fbf\u7684\u6587\u4ef6\u5207\u6362\u4ee5\u53ca\u9762\u677f\u63a7\u5236\u53ef\u4ee5\u8ba9\u4f60\u540c\u65f6\u5f00\u53d1\u591a\u4efd\u6587\u4ef6\u751a\u81f3\u540c\u4e00\u4e2a\u6587\u4ef6\u7684\u4e0d\u540c\u4f4d\u7f6e\u3002 Vim \u7684\u5b8f\u64cd\u4f5c\u53ef\u4ee5\u6279\u91cf\u5316\u5904\u7406\u91cd\u590d\u64cd\u4f5c\uff08\u4f8b\u5982\u591a\u884c tab\uff0c\u6279\u91cf\u52a0\u53cc\u5f15\u53f7\u7b49\u7b49\uff09 Vim \u662f\u5f88\u591a\u670d\u52a1\u5668\u81ea\u5e26\u7684\u547d\u4ee4\u884c\u7f16\u8f91\u5668\uff0c\u5f53\u4f60\u901a\u8fc7 ssh \u8fde\u63a5\u8fdc\u7a0b\u670d\u52a1\u5668\u4e4b\u540e\uff0c\u7531\u4e8e\u6ca1\u6709\u56fe\u5f62\u754c\u9762\uff0c\u53ea\u80fd\u5728\u547d\u4ee4\u884c\u91cc\u8fdb\u884c\u5f00\u53d1\uff08\u5f53\u7136\u73b0\u5728\u5f88\u591a IDE \u5982 VS Code \u63d0\u4f9b\u4e86 ssh \u63d2\u4ef6\u53ef\u4ee5\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\uff09\u3002 \u5f02\u5e38\u4e30\u5bcc\u7684\u63d2\u4ef6\u751f\u6001\uff0c\u8ba9\u4f60\u62e5\u6709\u4e16\u754c\u4e0a\u6700\u82b1\u91cc\u80e1\u54e8\u7684\u547d\u4ee4\u884c\u7f16\u8f91\u5668\u3002","title":"\u4e3a\u4ec0\u4e48\u5b66\u4e60 Vim"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Vim/#vim_2","text":"\u4e0d\u5e78\u7684\u662f Vim \u7684\u5b66\u4e60\u66f2\u7ebf\u786e\u5b9e\u76f8\u5f53\u9661\u5ced\uff0c\u6211\u82b1\u4e86\u597d\u51e0\u4e2a\u661f\u671f\u624d\u6162\u6162\u9002\u5e94\u4e86\u7528 Vim \u8fdb\u884c\u5f00\u53d1\u7684\u8fc7\u7a0b\u3002\u6700\u5f00\u59cb\u4f60\u4f1a\u89c9\u5f97\u975e\u5e38\u4e0d\u9002\u5e94\uff0c\u4f46\u4e00\u65e6\u71ac\u8fc7\u4e86\u521d\u59cb\u9636\u6bb5\uff0c\u76f8\u4fe1\u6211\uff0c\u4f60\u4f1a\u7231\u4e0a Vim\u3002 Vim \u7684\u5b66\u4e60\u8d44\u6599\u6d69\u5982\u70df\u6d77\uff0c\u4f46\u638c\u63e1\u5b83\u6700\u597d\u7684\u65b9\u5f0f\u8fd8\u662f\u5c06\u5b83\u7528\u5728\u65e5\u5e38\u7684\u5f00\u53d1\u8fc7\u7a0b\u4e2d\uff0c\u800c\u4e0d\u662f\u4e00\u4e0a\u6765\u5c31\u53bb\u5b66\u5404\u79cd\u82b1\u91cc\u80e1\u54e8\u7684\u9ad8\u7ea7 Vim \u6280\u5de7\u3002\u4e2a\u4eba\u63a8\u8350\u7684\u5b66\u4e60\u8def\u7ebf\u5982\u4e0b\uff1a \u5148\u9605\u8bfb \u8fd9\u7bc7 tutorial \uff0c\u638c\u63e1\u57fa\u672c\u7684 Vim \u6982\u5ff5\u548c\u4f7f\u7528\u65b9\u5f0f\u3002 \u7528 Vim \u81ea\u5e26\u7684 vimtutor \u8fdb\u884c\u7ec3\u4e60\uff0c\u5b89\u88c5\u5b8c Vim \u4e4b\u540e\u76f4\u63a5\u5728\u547d\u4ee4\u884c\u91cc\u8f93\u5165 vimtutor \u5373\u53ef\u8fdb\u5165\u7ec3\u4e60\u7a0b\u5e8f\u3002 \u6700\u540e\u5c31\u662f\u5f3a\u8feb\u81ea\u5df1\u4f7f\u7528 Vim \u8fdb\u884c\u5f00\u53d1\uff0cIDE \u91cc\u53ef\u4ee5\u5b89\u88c5 Vim \u63d2\u4ef6\u3002 \u7b49\u4f60\u5b8c\u5168\u9002\u5e94 Vim \u4e4b\u540e\u65b0\u7684\u4e16\u754c\u4fbf\u5411\u4f60\u655e\u5f00\u4e86\u5927\u95e8\uff0c\u4f60\u53ef\u4ee5\u6309\u9700\u914d\u7f6e\u81ea\u5df1\u7684 Vim\uff08\u4fee\u6539 .vimrc \u6587\u4ef6\uff09\uff0c\u7f51\u4e0a\u6709\u6570\u4e0d\u80dc\u6570\u7684\u8d44\u6e90\u53ef\u4ee5\u501f\u9274\u3002","title":"\u5982\u4f55\u5b66\u4e60 Vim"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Vim/#_1","text":"Neil, Drew. Practical Vim: Edit Text at the Speed of Thought. N.p., Pragmatic Bookshelf, 2015. Neil, Drew. Modern Vim: Craft Your Development Environment with Vim 8 and Neovim. United States, Pragmatic Bookshelf.","title":"\u63a8\u8350\u53c2\u8003\u8d44\u6599"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/thesis/","text":"\u6bd5\u4e1a\u8bba\u6587 \u4e3a\u4ec0\u4e48\u5199\u8fd9\u4efd\u6559\u7a0b 2022\u5e74\uff0c\u6211\u672c\u79d1\u6bd5\u4e1a\u4e86\u3002\u5728\u5f00\u59cb\u52a8\u624b\u5199\u6bd5\u4e1a\u8bba\u6587\u7684\u65f6\u5019\uff0c\u6211\u5c34\u5c2c\u5730\u53d1\u73b0\uff0c\u6211\u5bf9 Word \u7684\u638c\u63e1\u7a0b\u5ea6\u4ec5\u9650\u4e8e\u8c03\u8282\u5b57\u4f53\u3001\u4fdd\u5b58\u5bfc\u51fa\u8fd9\u4e9b\u50bb\u74dc\u529f\u80fd\u3002\u66fe\u60f3\u8f6c\u6218 Latex\uff0c\u4f46\u8bba\u6587\u7684\u6bb5\u843d\u683c\u5f0f\u8981\u6c42\u8c03\u6574\u8d77\u6765\u8fd8\u662f\u7528 Word 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\u6559\u5b66\u89c6\u9891\u5f00\u59cb\u5934\u60ac\u6881\u9525\u523a\u80a1\uff0c\u56e0\u4e3a\u751f\u4ea7\u4e00\u4efd\u5e94\u4ed8\u6bd5\u4e1a\u7684\u5b66\u672f\u5783\u573e\u53ea\u8981\u5b66\u534a\u5c0f\u65f6\u80fd\u4e0a\u624b\u5c31\u591f\u4e86\u3002\u6211\u5f53\u65f6\u770b\u7684 \u4e00\u4e2a B \u7ad9\u7684\u6559\u5b66\u89c6\u9891 \uff0c\u77ed\u5c0f\u7cbe\u608d\u975e\u5e38\u5b9e\u7528\uff0c\u5168\u957f\u534a\u5c0f\u65f6\u6781\u901f\u5165\u95e8\u3002 \u751f\u4ea7\u5b66\u672f\u5783\u573e\uff1a\u6700\u5bb9\u6613\u7684\u4e00\u6b65\uff0c\u5927\u5bb6\u516b\u4ed9\u8fc7\u6d77\uff0c\u5404\u663e\u795e\u901a\u5427\uff0c\u795d\u5927\u5bb6\u6bd5\u4e1a\u987a\u5229\uff5e\uff5e","title":"\u6bd5\u4e1a\u8bba\u6587"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/thesis/#_1","text":"","title":"\u6bd5\u4e1a\u8bba\u6587"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/thesis/#_2","text":"2022\u5e74\uff0c\u6211\u672c\u79d1\u6bd5\u4e1a\u4e86\u3002\u5728\u5f00\u59cb\u52a8\u624b\u5199\u6bd5\u4e1a\u8bba\u6587\u7684\u65f6\u5019\uff0c\u6211\u5c34\u5c2c\u5730\u53d1\u73b0\uff0c\u6211\u5bf9 Word \u7684\u638c\u63e1\u7a0b\u5ea6\u4ec5\u9650\u4e8e\u8c03\u8282\u5b57\u4f53\u3001\u4fdd\u5b58\u5bfc\u51fa\u8fd9\u4e9b\u50bb\u74dc\u529f\u80fd\u3002\u66fe\u60f3\u8f6c\u6218 Latex\uff0c\u4f46\u8bba\u6587\u7684\u6bb5\u843d\u683c\u5f0f\u8981\u6c42\u8c03\u6574\u8d77\u6765\u8fd8\u662f\u7528 Word \u66f4\u4e3a\u65b9\u4fbf\uff0c\u7ecf\u8fc7\u4e00\u756a\u75db\u82e6\u7f20\u6597\u4e4b\u540e\uff0c\u603b\u7b97\u662f\u6709\u60ca\u65e0\u9669\u5730\u5b8c\u6210\u4e86\u8bba\u6587\u7684\u5199\u4f5c\u548c\u7b54\u8fa9\u3002\u4e3a\u4e86\u4e0d\u8ba9\u540e\u6765\u8005\u91cd\u8e48\u8986\u8f99\uff0c\u9042\u628a\u76f8\u5173\u8d44\u6e90\u6574\u7406\u6210\u4e00\u4efd\u5f00\u7bb1\u5373\u7528\u7684\u6587\u6863\uff0c\u4f9b\u5927\u5bb6\u53c2\u8003\u3002","title":"\u4e3a\u4ec0\u4e48\u5199\u8fd9\u4efd\u6559\u7a0b"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/thesis/#word","text":"\u6b63\u5982\u5c06\u5927\u8c61\u88c5\u8fdb\u51b0\u7bb1\u9700\u8981\u4e09\u6b65\uff0c\u7528 Word \u5199\u6bd5\u4e1a\u8bba\u6587\u4e5f\u53ea\u9700\u8981\u7b80\u5355\u4e09\u6b65\uff1a \u786e\u5b9a\u8bba\u6587\u7684\u683c\u5f0f\u8981\u6c42\uff1a\u901a\u5e38\u5b66\u9662\u90fd\u4f1a\u4e0b\u53d1\u6bd5\u4e1a\u8bba\u6587\u7684\u683c\u5f0f\u8981\u6c42\uff08\u5404\u7ea7\u6807\u9898\u7684\u5b57\u4f53\u5b57\u53f7\u3001\u56fe\u4f8b\u548c\u5f15\u7528\u7684\u683c\u5f0f\u7b49\u7b49\uff09\uff0c\u5982\u679c\u66f4\u4e3a\u8d34\u5fc3\u7684\u8bdd\u751a\u81f3\u4f1a\u76f4\u63a5\u7ed9\u51fa\u8bba\u6587\u6a21\u7248\uff08\u5982\u662f\u6b64\u60c5\u51b5\u8bf7\u76f4\u63a5\u8df3\u8f6c\u5230\u4e0b\u4e00\u6b65\uff09\u3002\u5f88\u4e0d\u5e78\u7684\u662f\uff0c\u6211\u7684\u5b66\u9662\u5e76\u6ca1\u6709\u4e0b\u53d1\u6807\u51c6\u7684\u8bba\u6587\u683c\u5f0f\u8981\u6c42\uff0c\u8fd8\u63d0\u4f9b\u4e86\u4e00\u4efd\u683c\u5f0f\u6df7\u4e71\u51e0\u4e4e\u6beb\u65e0\u7528\u5904\u7684\u8bba\u6587\u6a21\u7248\u8188\u5e94\u6211\uff0c\u88ab\u903c\u65e0\u5948\u4e4b\u4e0b\u6211\u627e\u5230\u4e86\u5317\u4eac\u5927\u5b66\u7814\u7a76\u751f\u7684 \u8bba\u6587\u683c\u5f0f\u8981\u6c42 \uff0c\u5e76\u6309\u7167\u5176\u8981\u6c42\u5236\u4f5c\u4e86 \u4e00\u4efd\u6a21\u7248 \uff0c\u5927\u5bb6\u9700\u8981\u7684\u8bdd\u81ea\u53d6\uff0c\u672c\u4eba\u4e0d\u627f\u62c5\u65e0\u6cd5\u6bd5\u4e1a\u7b49\u4efb\u4f55\u8d23\u4efb\u3002 \u5b66\u4e60 Word \u6392\u7248\uff1a\u5230\u8fbe\u8fd9\u4e00\u6b65\u7684\u7ae5\u978b\u5206\u4e3a\u4e24\u7c7b\uff0c\u4e00\u662f\u5df2\u7ecf\u62e5\u6709\u4e86\u5b66\u9662\u63d0\u4f9b\u7684\u6807\u51c6\u6a21\u7248\uff0c\u4e8c\u662f\u53ea\u6709\u4e00\u4efd\u865a\u65e0\u7f25\u7f08\u7684\u683c\u5f0f\u8981\u6c42\u3002\u90a3\u73b0\u5728\u5f53\u52a1\u4e4b\u6025\u5c31\u662f\u5b66\u4e60\u57fa\u7840\u7684 Word \u6392\u7248\u6280\u672f\uff0c\u5bf9\u4e8e\u524d\u8005\u53ef\u4ee5\u5b66\u4f1a\u4f7f\u7528\u6a21\u7248\uff0c\u5bf9\u4e8e\u540e\u8005\u5219\u53ef\u4ee5\u5b66\u4f1a\u5236\u4f5c\u6a21\u7248\u3002\u6b64\u65f6\u5207\u8bb0\u4e0d\u8981\u96c4\u5fc3\u52c3\u52c3\u5730\u9009\u62e9\u4e00\u4e2a\u5341\u51e0\u4e2a\u5c0f\u65f6\u7684 Word \u6559\u5b66\u89c6\u9891\u5f00\u59cb\u5934\u60ac\u6881\u9525\u523a\u80a1\uff0c\u56e0\u4e3a\u751f\u4ea7\u4e00\u4efd\u5e94\u4ed8\u6bd5\u4e1a\u7684\u5b66\u672f\u5783\u573e\u53ea\u8981\u5b66\u534a\u5c0f\u65f6\u80fd\u4e0a\u624b\u5c31\u591f\u4e86\u3002\u6211\u5f53\u65f6\u770b\u7684 \u4e00\u4e2a B \u7ad9\u7684\u6559\u5b66\u89c6\u9891 \uff0c\u77ed\u5c0f\u7cbe\u608d\u975e\u5e38\u5b9e\u7528\uff0c\u5168\u957f\u534a\u5c0f\u65f6\u6781\u901f\u5165\u95e8\u3002 \u751f\u4ea7\u5b66\u672f\u5783\u573e\uff1a\u6700\u5bb9\u6613\u7684\u4e00\u6b65\uff0c\u5927\u5bb6\u516b\u4ed9\u8fc7\u6d77\uff0c\u5404\u663e\u795e\u901a\u5427\uff0c\u795d\u5927\u5bb6\u6bd5\u4e1a\u987a\u5229\uff5e\uff5e","title":"\u5982\u4f55\u7528 Word \u5199\u6bd5\u4e1a\u8bba\u6587"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/","text":"\u5b9e\u7528\u5de5\u5177\u7bb1 \u4e0b\u8f7d\u5de5\u5177 Libgen : PDF\u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9\u3002 z-epub : ePub\u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9\u3002 bitdownloader : \u6cb9\u7ba1\u89c6\u9891\u4e0b\u8f7d\u5668\u3002 zlibrary : \u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9(\u53ef\u80fd\u9700\u8981\u7ffb\u5899)\u3002 \u8bbe\u8ba1\u5de5\u5177 excalidraw : \u4e00\u6b3e\u624b\u7ed8\u98ce\u683c\u7684\u7ed8\u56fe\u5de5\u5177\uff0c\u975e\u5e38\u9002\u5408\u7ed8\u5236\u8bfe\u7a0b\u62a5\u544a\u6216\u8005PPT\u5185\u7684\u793a\u610f\u56fe\u3002 origamiway : \u624b\u628a\u624b\u6559\u4f60\u600e\u4e48\u6298\u7eb8\u3002 thingiverse : \u56ca\u62ec\u5404\u7c7b 2D/3D \u8bbe\u8ba1\u8d44\u6e90\uff0c\u5176 STL \u6587\u4ef6\u4e0b\u8f7d\u53ef\u76f4\u63a5 3D \u6253\u5370\u3002 iconfont : \u56fd\u5185\u6700\u5927\u7684\u56fe\u6807\u548c\u63d2\u753b\u8d44\u6e90\u5e93\uff0c\u53ef\u7528\u4e8e\u5f00\u53d1\u6216\u7ed8\u5236\u7cfb\u7edf\u67b6\u6784\u56fe\u3002 turbosquid : \u53ef\u4ee5\u8d2d\u4e70\u5404\u5f0f\u5404\u6837\u7684\u6a21\u578b\u3002 \u7f16\u7a0b\u76f8\u5173 sqlfiddle : \u4e00\u4e2a\u7b80\u6613\u7684\u5728\u7ebf SQL Playground\u3002 godbolt : \u975e\u5e38\u65b9\u4fbf\u7684\u7f16\u8bd1\u5668\u63a2\u7d22\u5de5\u5177\u3002\u4f60\u53ef\u4ee5\u5199\u4e00\u6bb5 C/C++ \u4ee3\u7801\uff0c\u9009\u62e9\u4e00\u6b3e\u7f16\u8bd1\u5668\uff0c\u7136\u540e\u4fbf\u53ef\u4ee5\u89c2\u5bdf\u751f\u6210\u7684\u5177\u4f53\u6c47\u7f16\u4ee3\u7801\u3002 explainshell : \u4f60\u662f\u5426\u66fe\u4e3a\u4e00\u6bb5 shell \u4ee3\u7801\u7684\u5177\u4f53\u542b\u4e49\u611f\u5230\u56f0\u6270\uff1fmanpage \u770b\u534a\u5929\u8fd8\u662f\u4e0d\u660e\u6240\u4ee5\uff1f\u8bd5\u8bd5\u8fd9\u4e2a\u7f51\u7ad9\uff01 regex101 : \u6b63\u5219\u8868\u8fbe\u5f0f\u8c03\u8bd5\u7f51\u7ad9\uff0c\u652f\u6301\u5404\u79cd\u7f16\u7a0b\u8bed\u8a00\u7684\u5339\u914d\u6807\u51c6\u3002 typingtom : \u9488\u5bf9\u7a0b\u5e8f\u5458\u7684\u6253\u5b57\u7ec3\u4e60/\u6d4b\u901f\u7f51\u7ad9\u3002 \u5b66\u4e60\u7f51\u7ad9 HFS : \u5404\u7c7b\u8f6f\u4ef6\u6559\u7a0b\u3002 os-wiki : \u64cd\u4f5c\u7cfb\u7edf\u6280\u672f\u8d44\u6e90\u767e\u79d1\u5168\u4e66\u3002 Shadertoy : \u7f16\u5199\u5404\u5f0f\u5404\u6837\u7684 shader\u3002 \u6742\u9879 tophub : \u65b0\u95fb\u70ed\u699c\u5408\u96c6\uff08\u7efc\u5408\u4e86\u77e5\u4e4e\u3001\u5fae\u535a\u3001\u767e\u5ea6\u3001\u5fae\u4fe1\u7b49\uff09\u3002 speedtest : \u5728\u7ebf\u7f51\u7edc\u6d4b\u901f\u7f51\u7ad9\u3002 public-apis : \u516c\u5171 API \u5408\u96c6\u5217\u8868\u3002","title":"\u5b9e\u7528\u5de5\u5177\u7bb1"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_1","text":"","title":"\u5b9e\u7528\u5de5\u5177\u7bb1"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_2","text":"Libgen : PDF\u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9\u3002 z-epub : ePub\u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9\u3002 bitdownloader : \u6cb9\u7ba1\u89c6\u9891\u4e0b\u8f7d\u5668\u3002 zlibrary : \u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9(\u53ef\u80fd\u9700\u8981\u7ffb\u5899)\u3002","title":"\u4e0b\u8f7d\u5de5\u5177"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_3","text":"excalidraw : \u4e00\u6b3e\u624b\u7ed8\u98ce\u683c\u7684\u7ed8\u56fe\u5de5\u5177\uff0c\u975e\u5e38\u9002\u5408\u7ed8\u5236\u8bfe\u7a0b\u62a5\u544a\u6216\u8005PPT\u5185\u7684\u793a\u610f\u56fe\u3002 origamiway : \u624b\u628a\u624b\u6559\u4f60\u600e\u4e48\u6298\u7eb8\u3002 thingiverse : \u56ca\u62ec\u5404\u7c7b 2D/3D \u8bbe\u8ba1\u8d44\u6e90\uff0c\u5176 STL \u6587\u4ef6\u4e0b\u8f7d\u53ef\u76f4\u63a5 3D \u6253\u5370\u3002 iconfont : \u56fd\u5185\u6700\u5927\u7684\u56fe\u6807\u548c\u63d2\u753b\u8d44\u6e90\u5e93\uff0c\u53ef\u7528\u4e8e\u5f00\u53d1\u6216\u7ed8\u5236\u7cfb\u7edf\u67b6\u6784\u56fe\u3002 turbosquid : \u53ef\u4ee5\u8d2d\u4e70\u5404\u5f0f\u5404\u6837\u7684\u6a21\u578b\u3002","title":"\u8bbe\u8ba1\u5de5\u5177"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_4","text":"sqlfiddle : \u4e00\u4e2a\u7b80\u6613\u7684\u5728\u7ebf SQL Playground\u3002 godbolt : \u975e\u5e38\u65b9\u4fbf\u7684\u7f16\u8bd1\u5668\u63a2\u7d22\u5de5\u5177\u3002\u4f60\u53ef\u4ee5\u5199\u4e00\u6bb5 C/C++ \u4ee3\u7801\uff0c\u9009\u62e9\u4e00\u6b3e\u7f16\u8bd1\u5668\uff0c\u7136\u540e\u4fbf\u53ef\u4ee5\u89c2\u5bdf\u751f\u6210\u7684\u5177\u4f53\u6c47\u7f16\u4ee3\u7801\u3002 explainshell : \u4f60\u662f\u5426\u66fe\u4e3a\u4e00\u6bb5 shell \u4ee3\u7801\u7684\u5177\u4f53\u542b\u4e49\u611f\u5230\u56f0\u6270\uff1fmanpage \u770b\u534a\u5929\u8fd8\u662f\u4e0d\u660e\u6240\u4ee5\uff1f\u8bd5\u8bd5\u8fd9\u4e2a\u7f51\u7ad9\uff01 regex101 : \u6b63\u5219\u8868\u8fbe\u5f0f\u8c03\u8bd5\u7f51\u7ad9\uff0c\u652f\u6301\u5404\u79cd\u7f16\u7a0b\u8bed\u8a00\u7684\u5339\u914d\u6807\u51c6\u3002 typingtom : \u9488\u5bf9\u7a0b\u5e8f\u5458\u7684\u6253\u5b57\u7ec3\u4e60/\u6d4b\u901f\u7f51\u7ad9\u3002","title":"\u7f16\u7a0b\u76f8\u5173"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_5","text":"HFS : \u5404\u7c7b\u8f6f\u4ef6\u6559\u7a0b\u3002 os-wiki : \u64cd\u4f5c\u7cfb\u7edf\u6280\u672f\u8d44\u6e90\u767e\u79d1\u5168\u4e66\u3002 Shadertoy : \u7f16\u5199\u5404\u5f0f\u5404\u6837\u7684 shader\u3002","title":"\u5b66\u4e60\u7f51\u7ad9"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_6","text":"tophub : \u65b0\u95fb\u70ed\u699c\u5408\u96c6\uff08\u7efc\u5408\u4e86\u77e5\u4e4e\u3001\u5fae\u535a\u3001\u767e\u5ea6\u3001\u5fae\u4fe1\u7b49\uff09\u3002 speedtest : \u5728\u7ebf\u7f51\u7edc\u6d4b\u901f\u7f51\u7ad9\u3002 public-apis : \u516c\u5171 API \u5408\u96c6\u5217\u8868\u3002","title":"\u6742\u9879"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/%E7%BF%BB%E5%A2%99/","text":"\u7ffb\u5899 \u6b64\u94fe\u63a5 \u51fa\u73b0\u5728\u8fd9\u91cc\u7eaf\u5c5e\u4e8c\u8fdb\u5236 bit \u7684\u968f\u610f\u7ec4\u5408\uff0c\u4e0e\u672c\u4eba\u6beb\u65e0\u5173\u7cfb\u3002","title":"\u7ffb\u5899"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/%E7%BF%BB%E5%A2%99/#_1","text":"\u6b64\u94fe\u63a5 \u51fa\u73b0\u5728\u8fd9\u91cc\u7eaf\u5c5e\u4e8c\u8fdb\u5236 bit \u7684\u968f\u610f\u7ec4\u5408\uff0c\u4e0e\u672c\u4eba\u6beb\u65e0\u5173\u7cfb\u3002","title":"\u7ffb\u5899"},{"location":"%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/CS162/","text":"CS162: Operating System \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS61A, CS61B, CS61C \u7f16\u7a0b\u8bed\u8a00\uff1aC, x86\u6c47\u7f16 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a200 \u5c0f\u65f6+\uff0c\u4e0a\u4e0d\u5c01\u9876 \u8fd9\u95e8\u8bfe\u8ba9\u6211\u8bb0\u5fc6\u72b9\u65b0\u7684\u6709\u4e24\u4e2a\u90e8\u5206\uff1a \u9996\u5148\u662f\u6559\u6750\uff0c\u8fd9\u672c\u4e66\u7528\u7684\u6559\u6750 Operating Systems: Principles and Practice (2nd Edition) \u4e00\u5171\u56db\u5377\uff0c\u5199\u5f97\u975e\u5e38\u6df1\u5165\u6d45\u51fa\uff0c\u5f88\u597d\u5730\u5f25\u8865\u4e86 MIT6.S081 \u5728\u7406\u8bba\u77e5\u8bc6\u4e0a\u7684\u4e9b\u8bb8\u7a7a\u767d\uff0c\u975e\u5e38\u5efa\u8bae\u5927\u5bb6\u9605\u8bfb\u3002\u76f8\u5173\u8d44\u6e90\u4f1a\u5206\u4eab\u5728\u672c\u4e66\u7684\u7ecf\u5178\u4e66\u7c4d\u63a8\u8350\u6a21\u5757\u3002 \u5176\u6b21\u662f\u8fd9\u95e8\u8bfe\u7684 Project \u2014\u2014 Pintos\u3002Pintos \u662f\u7531 Ben Pfaff \u7b49\u4eba\u5728 x86 \u5e73\u53f0\u4e0a\u7f16\u5199\u7684\u6559\u5b66\u7528\u64cd\u4f5c\u7cfb\u7edf\uff0cBen Pfaff \u751a\u81f3\u4e13\u95e8\u53d1\u4e86\u7bc7 paper \u6765\u9610\u8ff0 Pintos \u7684\u8bbe\u8ba1\u601d\u60f3\u3002 \u548c MIT \u7684 xv6 \u5c0f\u800c\u7cbe\u7684 lab \u8bbe\u8ba1\u7406\u5ff5\u4e0d\u540c\uff0cPintos \u66f4\u6ce8\u91cd\u7cfb\u7edf\u7684 Design and Implementation\u3002Pintos \u672c\u8eab\u4ec5\u4e00\u4e07\u884c\u5de6\u53f3\uff0c\u53ea\u63d0\u4f9b\u4e86\u64cd\u4f5c\u7cfb\u7edf\u6700\u57fa\u672c\u7684\u529f\u80fd\u3002\u800c 4 \u4e2aProject\uff0c\u5c31\u662f\u8ba9\u4f60\u5728\u8fd9\u4e2a\u6781\u4e3a\u7cbe\u7b80\u7684\u64cd\u4f5c\u7cfb\u7edf\u4e4b\u4e0a\uff0c\u5206\u522b\u4e3a\u5176\u589e\u52a0\u7ebf\u7a0b\u8c03\u5ea6\u673a\u5236 (Project1)\uff0c\u7cfb\u7edf\u8c03\u7528 (Project2)\uff0c\u865a\u62df\u5185\u5b58 (Project3) \u4ee5\u53ca\u6587\u4ef6\u7cfb\u7edf (Project4)\u3002\u6240\u6709\u7684 Project \u90fd\u7ed9\u5b66\u751f\u7559\u6709\u5f88\u5927\u7684\u8bbe\u8ba1\u7a7a\u95f4\uff0c\u603b\u4ee3\u7801\u91cf\u5728 2000 \u884c\u5de6\u53f3\u3002\u6839\u636e Stanford \u5b66\u751f \u81ea\u5df1\u7684\u53cd\u9988 \uff0c\u5728 3-4 \u4eba\u7ec4\u961f\u7684\u60c5\u51b5\u4e0b\uff0c\u540e\u4e24\u4e2a Project \u7684\u4eba\u5747\u8017\u65f6\u4e5f\u5728 40 \u4e2a\u5c0f\u65f6\u4ee5\u4e0a\u3002 \u867d\u7136\u96be\u5ea6\u5f88\u5927\uff0c\u4f46 Stanford, Berkeley, JHU \u7b49\u591a\u6240\u7f8e\u56fd\u9876\u5c16\u540d\u6821\u7684\u64cd\u7edf\u8bfe\u7a0b\u5747\u91c7\u7528\u4e86 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\u914d\u7f6e\u4e86\u8de8\u5e73\u53f0\u7684\u5b9e\u9a8c\u73af\u5883\uff0c\u60f3\u81ea\u5b66\u7684\u540c\u5b66\u53ef\u4ee5\u6309\u6587\u6863\u81ea\u884c\u5b66\u4e60\u3002\u5728\u6bd5\u4e1a\u524d\u7684\u6700\u540e\u4e00\u4e2a\u5b66\u671f\uff0c\u5e0c\u671b\u80fd\u7528\u8fd9\u6837\u7684\u5c1d\u8bd5\uff0c\u8ba9\u66f4\u591a\u4eba\u7231\u4e0a\u7cfb\u7edf\u9886\u57df\uff0c\u4e3a\u56fd\u5185\u7684\u7cfb\u7edf\u7814\u7a76\u6dfb\u7816\u52a0\u74e6\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://cs162.org/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.youtube.com/watch?v=YfHY0pvpRkk \uff0c\u6bcf\u8282\u8bfe\u7684\u94fe\u63a5\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a Operating Systems: Principles and Practice (2nd Edition) \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://cs162.org/ \uff0c6 \u4e2a Homework, 3 \u4e2a Project\uff0c\u5177\u4f53\u8981\u6c42\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8d44\u6e90\u6c47\u603b 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Implementation"},{"location":"%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/NJUOS/#nju-os-operating-system-design-and-implementation","text":"","title":"NJU OS: Operating System Design and Implementation"},{"location":"%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/NJUOS/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1a\u5357\u4eac\u5927\u5b66 \u5148\u4fee\u8981\u6c42\uff1a\u4f53\u7cfb\u7ed3\u6784 + \u624e\u5b9e\u7684 C \u8bed\u8a00\u529f\u5e95 \u7f16\u7a0b\u8bed\u8a00\uff1aC \u8bed\u8a00 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 \u4e4b\u524d\u4e00\u76f4\u542c\u8bf4\u5357\u5927\u7684\u848b\u708e\u5ca9\u8001\u5e08\u5f00\u8bbe\u7684\u64cd\u4f5c\u7cfb\u7edf\u8bfe\u7a0b\u8bb2\u5f97\u5f88\u597d\uff0c\u4e45\u95fb\u4e0d\u5982\u4e00\u89c1\uff0c\u8fd9\u5b66\u671f\u6709\u5e78\u5728 B 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http://jyywiki.cn/OS/2022/","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/NJUOS/#_3","text":"\u6309\u848b\u8001\u5e08\u7684\u8981\u6c42\uff0c\u6211\u7684\u4f5c\u4e1a\u5b9e\u73b0\u6ca1\u6709\u5f00\u6e90\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITLA/","text":"MIT18.06: Linear Algebra \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u82f1\u6587 \u7f16\u7a0b\u8bed\u8a00\uff1a\u65e0 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a\u56e0\u4eba\u800c\u5f02 \u6570\u5b66\u5927\u725b Gilbert Strang \u8001\u5148\u751f\u5e74\u903e\u53e4\u7a00\u4ecd\u575a\u6301\u6388\u8bfe\uff0c\u5176\u7ecf\u5178\u6559\u6750 Introduction to Linear Algebra \u5df2\u88ab\u6e05\u534e\u91c7\u7528\u4e3a\u5b98\u65b9\u6559\u6750\u3002\u6211\u5f53\u65f6\u770b\u5b8c\u76d7\u7248 PDF \u4e4b\u540e\u6df1\u611f\u6127\u759a\uff0c\u542b\u6cea\u82b1\u4e86\u4e24\u767e\u591a\u4e70\u4e86\u4e00\u672c\u82f1\u6587\u6b63\u7248\u6536\u85cf\u3002\u4e0b\u9762\u9644\u4e0a\u6b64\u4e66\u5c01\u9762\uff0c\u5982\u679c\u4f60\u80fd\u5b8c\u5168\u7406\u89e3\u5c01\u9762\u56fe\u7684\u6570\u5b66\u542b\u4e49\uff0c\u90a3\u4f60\u5bf9\u7ebf\u6027\u4ee3\u6570\u7684\u7406\u89e3\u4e00\u5b9a\u4f1a\u8fbe\u5230\u65b0\u7684\u9ad8\u5ea6\u3002 \u914d\u5408\u6cb9\u7ba1\u6570\u5b66\u7f51\u7ea2 3Blue1Brown \u7684 \u7ebf\u6027\u4ee3\u6570\u7684\u672c\u8d28 \u7cfb\u5217\u89c6\u9891\u98df\u7528\u66f4\u4f73\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/syllabus/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1aIntroduction to Linear Algebra. Gilbert Strang \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9","title":"MIT18.06: Linear Algebra"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITLA/#mit1806-linear-algebra","text":"","title":"MIT18.06: Linear Algebra"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITLA/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u82f1\u6587 \u7f16\u7a0b\u8bed\u8a00\uff1a\u65e0 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a\u56e0\u4eba\u800c\u5f02 \u6570\u5b66\u5927\u725b Gilbert Strang \u8001\u5148\u751f\u5e74\u903e\u53e4\u7a00\u4ecd\u575a\u6301\u6388\u8bfe\uff0c\u5176\u7ecf\u5178\u6559\u6750 Introduction to Linear Algebra \u5df2\u88ab\u6e05\u534e\u91c7\u7528\u4e3a\u5b98\u65b9\u6559\u6750\u3002\u6211\u5f53\u65f6\u770b\u5b8c\u76d7\u7248 PDF \u4e4b\u540e\u6df1\u611f\u6127\u759a\uff0c\u542b\u6cea\u82b1\u4e86\u4e24\u767e\u591a\u4e70\u4e86\u4e00\u672c\u82f1\u6587\u6b63\u7248\u6536\u85cf\u3002\u4e0b\u9762\u9644\u4e0a\u6b64\u4e66\u5c01\u9762\uff0c\u5982\u679c\u4f60\u80fd\u5b8c\u5168\u7406\u89e3\u5c01\u9762\u56fe\u7684\u6570\u5b66\u542b\u4e49\uff0c\u90a3\u4f60\u5bf9\u7ebf\u6027\u4ee3\u6570\u7684\u7406\u89e3\u4e00\u5b9a\u4f1a\u8fbe\u5230\u65b0\u7684\u9ad8\u5ea6\u3002 \u914d\u5408\u6cb9\u7ba1\u6570\u5b66\u7f51\u7ea2 3Blue1Brown \u7684 \u7ebf\u6027\u4ee3\u6570\u7684\u672c\u8d28 \u7cfb\u5217\u89c6\u9891\u98df\u7528\u66f4\u4f73\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITLA/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a https://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/syllabus/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1aIntroduction to Linear Algebra. Gilbert Strang \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITmaths/","text":"MIT Calculus Course \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u82f1\u8bed \u7f16\u7a0b\u8bed\u8a00\uff1a\u65e0 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a\u56e0\u4eba\u800c\u5f02 MIT \u7684\u5fae\u79ef\u5206\u8bfe\u7531 MIT18.01: Single Variable Calculus \u548c MIT18.02: Multivariable Calculus \u4e24\u95e8\u8bfe\u7ec4\u6210\u3002\u5bf9\u81ea\u5df1\u6570\u5b66\u57fa\u7840\u6bd4\u8f83\u81ea\u4fe1\u7684\u540c\u5b66\u53ef\u4ee5\u53ea\u770b\u8bfe\u7a0b notes\uff0c\u5199\u5f97\u975e\u5e38\u6d45\u663e\u751f\u52a8\u5e76\u4e14\u6293\u4f4f\u672c\u8d28\uff0c\u8ba9\u4f60\u4e0d\u518d\u75b2\u4e8e\u505a\u9898\u800c\u662f\u80fd\u591f\u771f\u6b63\u7aa5\u89c1\u5fae\u79ef\u5206\u7684\u672c\u8d28\u9b45\u529b\u3002 \u914d\u5408\u6cb9\u7ba1\u6570\u5b66\u7f51\u7ea2 3Blue1Brown \u7684 \u5fae\u79ef\u5206\u7684\u672c\u8d28 \u7cfb\u5217\u89c6\u9891\u98df\u7528\u66f4\u4f73\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a 18.01 , 18.02 \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a\u53c2\u89c1\u8bfe\u7a0b notes \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u4e66\u9762\u4f5c\u4e1a\u53ca\u7b54\u6848\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9","title":"MIT18.01/18.02: Calculus"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITmaths/#mit-calculus-course","text":"","title":"MIT Calculus Course"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITmaths/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u82f1\u8bed \u7f16\u7a0b\u8bed\u8a00\uff1a\u65e0 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a\u56e0\u4eba\u800c\u5f02 MIT \u7684\u5fae\u79ef\u5206\u8bfe\u7531 MIT18.01: Single Variable Calculus \u548c MIT18.02: Multivariable Calculus \u4e24\u95e8\u8bfe\u7ec4\u6210\u3002\u5bf9\u81ea\u5df1\u6570\u5b66\u57fa\u7840\u6bd4\u8f83\u81ea\u4fe1\u7684\u540c\u5b66\u53ef\u4ee5\u53ea\u770b\u8bfe\u7a0b notes\uff0c\u5199\u5f97\u975e\u5e38\u6d45\u663e\u751f\u52a8\u5e76\u4e14\u6293\u4f4f\u672c\u8d28\uff0c\u8ba9\u4f60\u4e0d\u518d\u75b2\u4e8e\u505a\u9898\u800c\u662f\u80fd\u591f\u771f\u6b63\u7aa5\u89c1\u5fae\u79ef\u5206\u7684\u672c\u8d28\u9b45\u529b\u3002 \u914d\u5408\u6cb9\u7ba1\u6570\u5b66\u7f51\u7ea2 3Blue1Brown \u7684 \u5fae\u79ef\u5206\u7684\u672c\u8d28 \u7cfb\u5217\u89c6\u9891\u98df\u7528\u66f4\u4f73\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITmaths/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a 18.01 , 18.02 \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a\u53c2\u89c1\u8bfe\u7a0b notes \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u4e66\u9762\u4f5c\u4e1a\u53ca\u7b54\u6848\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/information/","text":"MIT6.050J: Information theory and Entropy \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u65e0 \u7f16\u7a0b\u8bed\u8a00\uff1a\u65e0 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 MIT \u9762\u5411\u5927\u4e00\u65b0\u751f\u7684\u4fe1\u606f\u8bba\u5165\u95e8\u8bfe\u7a0b\uff0cPenfield \u6559\u6388\u4e13\u95e8\u4e3a\u8fd9\u95e8\u8bfe\u5199\u4e86\u4e00\u672c \u6559\u6750 \u4f5c\u4e3a\u8bfe\u7a0b notes\uff0c\u5185\u5bb9\u6df1\u5165\u6d45\u51fa\uff0c\u751f\u52a8\u6709\u8da3\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/index.htm \u8bfe\u7a0b\u6559\u6750\uff1a https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/syllabus/MIT6_050JS08_textbook.pdf \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u8be6\u89c1\u8bfe\u7a0b\u7f51\u7ad9\uff0c\u5305\u542b\u4e66\u9762\u4f5c\u4e1a\u4e0e Matlab \u7f16\u7a0b\u4f5c\u4e1a\u3002","title":"MIT6.050J: Information theory and Entropy"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/information/#mit6050j-information-theory-and-entropy","text":"","title":"MIT6.050J: Information theory and Entropy"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/information/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u65e0 \u7f16\u7a0b\u8bed\u8a00\uff1a\u65e0 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 MIT \u9762\u5411\u5927\u4e00\u65b0\u751f\u7684\u4fe1\u606f\u8bba\u5165\u95e8\u8bfe\u7a0b\uff0cPenfield 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\u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1aCalculus, Linear Algebra \u7f16\u7a0b\u8bed\u8a00\uff1aPython preferred \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a50-70 \u5c0f\u65f6 MIT \u7684\u79bb\u6563\u6570\u5b66\u4ee5\u53ca\u6982\u7387\u7efc\u5408\u8bfe\u7a0b\uff0c\u5bfc\u5e08\u662f\u5927\u540d\u9f0e\u9f0e\u7684 Tom Leighton ( Akamai \u7684\u8054\u5408\u521b\u59cb\u4eba\u4e4b\u4e00)\u3002\u5b66\u5b8c\u4e4b\u540e\u5bf9\u4e8e\u540e\u7eed\u7684\u7b97\u6cd5\u5b66\u4e60\u5927\u6709\u88e8\u76ca\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1L741147VX \u8bfe\u7a0b\u4f5c\u4e1a\uff1a 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Join\uff0c\u7edf\u8ba1\u4fe1\u606f\u4ee5\u53ca\u4ee3\u4ef7\u4f30\u8ba1\uff0c\u5b50\u67e5\u8be2\u5b9e\u73b0\uff0cAgg\uff0cGroup By \u7684\u5b9e\u73b0\u7b49\u3002\u9664\u6b64\u4e4b\u5916\uff0c\u8fd8\u6709 B+\u6811\uff0cWAL \u76f8\u5173\u5b9e\u9a8c\u3002\u672c\u95e8\u8bfe\u7a0b\u9002\u5408\u5728\u5b66\u5b8c CMU15-445 \u8bfe\u7a0b\u4e4b\u540e\uff0c\u5bf9\u67e5\u8be2\u4f18\u5316\u76f8\u5173\u5185\u5bb9\u6709\u5174\u8da3\u7684\u540c\u5b66\u3002 \u4e0b\u9762\u4ecb\u7ecd\u4e00\u4e0b\u8fd9\u95e8\u8bfe\u7684\u524d 3 \u4e2a Assignment \u4e5f\u5c31\u662f\u5b9e\u9a8c Lab \u6240\u8981\u5b9e\u73b0\u7684\u529f\u80fd\uff1a Assignment1 \u4e3a NanoDB \u63d0\u4f9b delete\uff0cupdate \u8bed\u53e5\u7684\u652f\u6301\u3002 \u4e3a Buffer Pool Manager \u6dfb\u52a0\u5408\u9002\u7684 pin/unpin \u4ee3\u7801\u3002 \u63d0\u5347 insert \u8bed\u53e5\u7684\u6027\u80fd\uff0c \u540c\u65f6\u4e0d\u4f7f\u6570\u636e\u5e93\u6587\u4ef6\u5927\u5c0f\u8fc7\u5206\u81a8\u80c0\u3002 Assignment2 \u5b9e\u73b0\u4e00\u4e2a\u7b80\u5355\u7684\u8ba1\u5212\u751f\u6210\u5668\uff0c\u5c06\u5404\u79cd\u5df2\u7ecf Parser \u8fc7\u7684 SQL \u8bed\u53e5\u8f6c\u5316\u4e3a\u53ef\u6267\u884c\u7684\u6267\u884c\u8ba1\u5212\u3002 \u4f7f\u7528 nested-loop join \u7b97\u6cd5\uff0c\u5b9e\u73b0\u652f\u6301 inner- and outer-join \u7684 Join \u8ba1\u5212\u8282\u70b9\u3002 \u6dfb\u52a0\u4e00\u4e9b\u5355\u5143\u6d4b\u8bd5\uff0c \u4fdd\u8bc1 inner- and outer-join \u529f\u80fd\u5b9e\u73b0\u6b63\u786e\u3002 Assignment3 \u5b8c\u6210\u6536\u96c6\u8868\u7684\u7edf\u8ba1\u4fe1\u606f\u3002 \u5b8c\u6210\u5404\u79cd\u8ba1\u5212\u8282\u70b9\u7684\u8ba1\u5212\u6210\u672c\u8ba1\u7b97\u3002 \u8ba1\u7b97\u53ef\u51fa\u73b0\u5728\u6267\u884c\u8ba1\u5212\u4e2d\u7684\u5404\u79cd\u8c13\u8bcd\u7684\u9009\u62e9\u6027\u3002 \u6839\u636e\u8c13\u8bcd\u66f4\u65b0\u8ba1\u5212\u8282\u70b9\u8f93\u51fa\u7684\u5143\u7ec4\u7edf\u8ba1\u4fe1\u606f\u3002 \u5269\u4f59 Assignment \u548c Challenges \u53ef\u4ee5\u67e5\u770b\u8bfe\u7a0b\u4ecb\u7ecd\uff0c\u63a8\u8350\u4f7f\u7528 IDEA \u6253\u5f00\u5de5\u7a0b\uff0cMaven \u6784\u5efa\uff0c\u6ce8\u610f\u65e5\u5fd7\u76f8\u5173\u914d\u7f6e\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://courses.cms.caltech.edu/cs122/ \u8bfe\u7a0b\u4ee3\u7801\uff1a https://gitlab.caltech.edu/cs122-19wi \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a7 Assignments + 2 Challenges","title":"Caltech CS122: Database System Implementation"},{"location":"%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/CS122/#caltech-cs-122-database-system-implementation","text":"","title":"Caltech CS 122: Database System Implementation"},{"location":"%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/CS122/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aCaltech \u5148\u4fee\u8981\u6c42\uff1a\u65e0 \u7f16\u7a0b\u8bed\u8a00\uff1aJava \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f 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\u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 \u4f2f\u514b\u5229\u7684\u6570\u636e\u79d1\u5b66\u5165\u95e8\u8bfe\u7a0b\uff0c\u5185\u5bb9\u76f8\u5bf9\u57fa\u7840\uff0c\u8986\u76d6\u4e86\u6570\u636e\u6e05\u6d17\u3001\u7279\u5f81\u63d0\u53d6\u3001\u6570\u636e\u53ef\u89c6\u5316\u4ee5\u53ca\u673a\u5668\u5b66\u4e60\u548c\u63a8\u7406\u7684\u57fa\u7840\u5185\u5bb9\uff0c\u4e5f\u4f1a\u8bb2\u6388 Pandas, Numpy, Matplotlib \u7b49\u6570\u636e\u79d1\u5b66\u5e38\u7528\u5de5\u5177\u3002\u5176\u4e30\u5bcc\u6709\u8da3\u7684\u7f16\u7a0b\u4f5c\u4e1a\u4e5f\u662f\u8fd9\u95e8\u8bfe\u7684\u4e00\u5927\u4eae\u70b9\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6/Data100/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a https://ds100.org/fa21/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a https://www.textbook.ds100.org/intro.html 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\u4ee5\u53ca\u7f51\u7edc\u6d41\u7b97\u6cd5\u90fd\u662f\u5728\u8fd9\u95e8\u8bfe\u4e0a\u8ba9\u6211\u8305\u585e\u987f\u5f00\u7684\uff0c\u65f6\u9694\u4e24\u5e74\u6211\u751a\u81f3\u8fd8\u80fd\u5199\u51fa\u8fd9\u4e24\u4e2a\u7b97\u6cd5\u7684\u63a8\u5bfc\u4e0e\u8bc1\u660e\u3002 \u4f60\u662f\u5426\u89c9\u5f97\u7b97\u6cd5\u5b66\u4e86\u5c31\u5fd8\u5462\uff1f\u6211\u89c9\u5f97\u8ba9\u4f60\u5b8c\u5168\u638c\u63e1\u4e00\u4e2a\u7b97\u6cd5\u7684\u6838\u5fc3\u5728\u4e8e\u7406\u89e3\u4e09\u70b9\uff1a \u4e3a\u4ec0\u4e48\u8fd9\u4e48\u505a\uff1f\uff08\u6b63\u786e\u6027\u63a8\u5bfc\uff0c\u6291\u6216\u662f\u6574\u4e2a\u7b97\u6cd5\u7684\u6838\u5fc3\u672c\u8d28\uff09 \u5982\u4f55\u5b9e\u73b0\u5b83\uff1f\uff08\u5149\u5b66\u4e0d\u7528\u5047\u628a\u5f0f\uff09 \u7528\u5b83\u89e3\u51b3\u5b9e\u9645\u95ee\u9898\uff08\u5b66\u4ee5\u81f4\u7528\u624d\u662f\u771f\u672c\u4e8b\uff09 \u8fd9\u95e8\u8bfe\u7684\u6784\u6210\u5c31\u975e\u5e38\u597d\u5730\u5951\u5408\u4e86\u4e0a\u8ff0\u4e09\u4e2a\u6b65\u9aa4\u3002\u89c2\u770b\u8bfe\u7a0b\u89c6\u9891\u5e76\u4e14\u9605\u8bfb\u6559\u6388\u7684 \u5f00\u6e90\u8bfe\u672c \u6709\u52a9\u4e8e\u4f60\u7406\u89e3\u7b97\u6cd5\u7684\u672c\u8d28\uff0c\u8ba9\u4f60\u4e5f\u53ef\u4ee5\u7528\u975e\u5e38 \u751f\u52a8\u6d45\u663e\u7684\u8bdd\u8bed\u5411\u522b\u4eba\u8bb2\u8ff0\u4e3a\u4ec0\u4e48\u8fd9\u4e2a\u7b97\u6cd5\u5f97\u957f\u8fd9\u4e2a\u6837\u5b50\u3002 \u5728\u7406\u89e3\u7b97\u6cd5\u4e4b\u540e\uff0c\u4f60\u53ef\u4ee5\u9605\u8bfb\u6559\u6388\u5bf9\u4e8e\u8bfe\u7a0b\u4e2d\u8bb2\u6388\u7684\u6240\u6709\u6570\u636e\u7ed3\u6784\u4e0e\u7b97\u6cd5\u7684 \u4ee3\u7801\u5b9e\u73b0 \u3002 \u6ce8\u610f\uff0c\u8fd9\u4e9b\u5b9e\u73b0\u53ef\u4e0d\u662f demo \u6027\u8d28\u7684\uff0c\u800c\u662f\u5de5\u4e1a\u7ea7\u7684\u9ad8\u6548\u5b9e\u73b0\uff0c\u4ece\u6ce8\u91ca\u5230\u53d8\u91cf\u547d\u540d\u90fd\u975e\u5e38\u4e25\u8c28\uff0c\u6a21\u5757\u5316\u4e5f\u505a\u5f97\u76f8\u5f53\u597d\uff0c\u662f\u8d28\u91cf\u5f88\u9ad8\u7684\u4ee3\u7801\u3002\u6211\u4ece\u8fd9\u4e9b\u4ee3\u7801\u4e2d\u6536\u83b7\u826f\u591a\u3002 \u6700\u540e\uff0c\u5c31\u662f\u8fd9\u95e8\u8bfe\u6700\u6fc0\u52a8\u4eba\u5fc3\u7684\u90e8\u5206\u4e86\uff0c10 \u4e2a\u9ad8\u8d28\u91cf\u7684 Project\uff0c\u5e76\u4e14\u5168\u90fd\u6709\u5b9e\u9645\u95ee\u9898\u7684\u80cc\u666f\u63cf\u8ff0\uff0c\u4e30\u5bcc\u7684\u6d4b\u8bd5\u6837\u4f8b\uff0c\u81ea\u52a8\u7684\u8bc4\u5206\u7cfb\u7edf\uff08\u4ee3\u7801\u98ce\u683c\u4e5f\u662f\u8bc4\u5206\u7684\u4e00\u73af\uff09\u3002\u8ba9\u4f60\u5728\u5b9e\u9645\u751f\u6d3b\u4e2d \u9886\u7565\u7b97\u6cd5\u7684\u9b45\u529b\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a Algorithm I , Algorithm II \u8bfe\u7a0b\u89c6\u9891\uff1a\u8be6\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a https://algs4.cs.princeton.edu/home/ \u8bfe\u7a0b\u4f5c\u4e1a\uff1a10\u4e2aProject\uff0c\u5177\u4f53\u8981\u6c42\u8be6\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/Princeton-Algorithm - GitHub \u4e2d\u3002","title":"Coursera: Algorithms I & II"},{"location":"%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/Algo/#coursera-algorithms-i-ii","text":"","title":"Coursera: Algorithms I & II"},{"location":"%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/Algo/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aPrinceton \u5148\u4fee\u8981\u6c42\uff1aCS61A \u7f16\u7a0b\u8bed\u8a00\uff1aJava \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a60 \u5c0f\u65f6 \u8fd9\u662f Coursera \u4e0a\u8bc4\u5206\u6700\u9ad8\u7684\u7b97\u6cd5\u8bfe\u7a0b\u3002Robert Sedgewick \u6559\u6388\u6709\u4e00\u79cd\u9b54\u529b\uff0c\u53ef\u4ee5\u5c06\u65e0\u8bba\u591a\u4e48\u590d\u6742\u7684\u7b97\u6cd5\u8bb2\u5f97\u6781\u4e3a\u751f\u52a8\u6d45\u663e\u3002\u5b9e\u4e0d\u76f8\u7792\uff0c\u56f0\u6270\u6211\u591a\u5e74\u7684 KMP \u4ee5\u53ca\u7f51\u7edc\u6d41\u7b97\u6cd5\u90fd\u662f\u5728\u8fd9\u95e8\u8bfe\u4e0a\u8ba9\u6211\u8305\u585e\u987f\u5f00\u7684\uff0c\u65f6\u9694\u4e24\u5e74\u6211\u751a\u81f3\u8fd8\u80fd\u5199\u51fa\u8fd9\u4e24\u4e2a\u7b97\u6cd5\u7684\u63a8\u5bfc\u4e0e\u8bc1\u660e\u3002 \u4f60\u662f\u5426\u89c9\u5f97\u7b97\u6cd5\u5b66\u4e86\u5c31\u5fd8\u5462\uff1f\u6211\u89c9\u5f97\u8ba9\u4f60\u5b8c\u5168\u638c\u63e1\u4e00\u4e2a\u7b97\u6cd5\u7684\u6838\u5fc3\u5728\u4e8e\u7406\u89e3\u4e09\u70b9\uff1a \u4e3a\u4ec0\u4e48\u8fd9\u4e48\u505a\uff1f\uff08\u6b63\u786e\u6027\u63a8\u5bfc\uff0c\u6291\u6216\u662f\u6574\u4e2a\u7b97\u6cd5\u7684\u6838\u5fc3\u672c\u8d28\uff09 \u5982\u4f55\u5b9e\u73b0\u5b83\uff1f\uff08\u5149\u5b66\u4e0d\u7528\u5047\u628a\u5f0f\uff09 \u7528\u5b83\u89e3\u51b3\u5b9e\u9645\u95ee\u9898\uff08\u5b66\u4ee5\u81f4\u7528\u624d\u662f\u771f\u672c\u4e8b\uff09 \u8fd9\u95e8\u8bfe\u7684\u6784\u6210\u5c31\u975e\u5e38\u597d\u5730\u5951\u5408\u4e86\u4e0a\u8ff0\u4e09\u4e2a\u6b65\u9aa4\u3002\u89c2\u770b\u8bfe\u7a0b\u89c6\u9891\u5e76\u4e14\u9605\u8bfb\u6559\u6388\u7684 \u5f00\u6e90\u8bfe\u672c \u6709\u52a9\u4e8e\u4f60\u7406\u89e3\u7b97\u6cd5\u7684\u672c\u8d28\uff0c\u8ba9\u4f60\u4e5f\u53ef\u4ee5\u7528\u975e\u5e38 \u751f\u52a8\u6d45\u663e\u7684\u8bdd\u8bed\u5411\u522b\u4eba\u8bb2\u8ff0\u4e3a\u4ec0\u4e48\u8fd9\u4e2a\u7b97\u6cd5\u5f97\u957f\u8fd9\u4e2a\u6837\u5b50\u3002 \u5728\u7406\u89e3\u7b97\u6cd5\u4e4b\u540e\uff0c\u4f60\u53ef\u4ee5\u9605\u8bfb\u6559\u6388\u5bf9\u4e8e\u8bfe\u7a0b\u4e2d\u8bb2\u6388\u7684\u6240\u6709\u6570\u636e\u7ed3\u6784\u4e0e\u7b97\u6cd5\u7684 \u4ee3\u7801\u5b9e\u73b0 \u3002 \u6ce8\u610f\uff0c\u8fd9\u4e9b\u5b9e\u73b0\u53ef\u4e0d\u662f demo \u6027\u8d28\u7684\uff0c\u800c\u662f\u5de5\u4e1a\u7ea7\u7684\u9ad8\u6548\u5b9e\u73b0\uff0c\u4ece\u6ce8\u91ca\u5230\u53d8\u91cf\u547d\u540d\u90fd\u975e\u5e38\u4e25\u8c28\uff0c\u6a21\u5757\u5316\u4e5f\u505a\u5f97\u76f8\u5f53\u597d\uff0c\u662f\u8d28\u91cf\u5f88\u9ad8\u7684\u4ee3\u7801\u3002\u6211\u4ece\u8fd9\u4e9b\u4ee3\u7801\u4e2d\u6536\u83b7\u826f\u591a\u3002 \u6700\u540e\uff0c\u5c31\u662f\u8fd9\u95e8\u8bfe\u6700\u6fc0\u52a8\u4eba\u5fc3\u7684\u90e8\u5206\u4e86\uff0c10 \u4e2a\u9ad8\u8d28\u91cf\u7684 Project\uff0c\u5e76\u4e14\u5168\u90fd\u6709\u5b9e\u9645\u95ee\u9898\u7684\u80cc\u666f\u63cf\u8ff0\uff0c\u4e30\u5bcc\u7684\u6d4b\u8bd5\u6837\u4f8b\uff0c\u81ea\u52a8\u7684\u8bc4\u5206\u7cfb\u7edf\uff08\u4ee3\u7801\u98ce\u683c\u4e5f\u662f\u8bc4\u5206\u7684\u4e00\u73af\uff09\u3002\u8ba9\u4f60\u5728\u5b9e\u9645\u751f\u6d3b\u4e2d \u9886\u7565\u7b97\u6cd5\u7684\u9b45\u529b\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/Algo/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a Algorithm I , Algorithm II \u8bfe\u7a0b\u89c6\u9891\uff1a\u8be6\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a https://algs4.cs.princeton.edu/home/ \u8bfe\u7a0b\u4f5c\u4e1a\uff1a10\u4e2aProject\uff0c\u5177\u4f53\u8981\u6c42\u8be6\u89c1\u8bfe\u7a0b\u7f51\u7ad9","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/Algo/#_3","text":"@PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/Princeton-Algorithm - GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS170/","text":"CS170: Efficient Algorithms and Intractable Problems \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS61B, CS70 \u7f16\u7a0b\u8bed\u8a00\uff1aLaTeX \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a60 \u5c0f\u65f6 \u4f2f\u514b\u5229\u7684\u7b97\u6cd5\u8bbe\u8ba1\u8bfe\uff0c\u66f4\u6ce8\u91cd\u7b97\u6cd5\u7684\u7406\u8bba\u57fa\u7840\u4e0e\u590d\u6742\u5ea6\u5206\u6790\u3002\u8bfe\u7a0b\u5185\u5bb9\u6db5\u76d6\u4e86\u5206\u6cbb\u3001\u56fe\u7b97\u6cd5\u3001\u6700\u77ed\u8def\u3001\u751f\u6210\u6811\u3001\u8d2a\u5fc3\u3001\u52a8\u89c4\u3001\u5e76\u67e5\u96c6\u3001\u7ebf\u6027\u89c4\u5212\u3001\u7f51\u7edc\u6d41\u3001NP \u95ee\u9898\u3001\u968f\u673a\u7b97\u6cd5\u3001\u54c8\u5e0c\u7b97\u6cd5\u7b49\u7b49\u3002 \u8fd9\u95e8\u8bfe\u7684\u6559\u6750\u5199\u7684\u5f88\u597d\uff0c\u8bc1\u660e\u6d45\u663e\u6613\u61c2\uff0c\u975e\u5e38\u9002\u5408\u4f5c\u4e3a\u5de5\u5177\u4e66\u67e5\u9605\u3002\u53e6\u5916\uff0c\u8fd9\u95e8\u8bfe\u53ea\u6709\u4e66\u9762\u4f5c\u4e1a\uff0c\u5e76\u4e14\u63a8\u8350\u7528 LaTeX \u7f16\u5199\uff0c\u5927\u5bb6\u53ef\u4ee5\u501f\u6b64\u673a\u4f1a\u953b\u70bc\u81ea\u5df1\u7684 LaTeX \u6280\u5de7\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://cs170.org/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1BU4y1b7RK \u8bfe\u7a0b\u6559\u6750\uff1a\u8be6\u89c1\u8bfe\u7a0b\u7f51\u7ad9 notes \u8bfe\u7a0b\u4f5c\u4e1a\uff1a13 \u6b21\u4e66\u9762\u4f5c\u4e1a\uff0c\u7528 LaTeX \u7f16\u5199 \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/UCB-CS170 - GitHub \u4e2d\u3002","title":"UCB CS170: Efficient Algorithms 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\u76f8\u5173\u7684\u7f16\u7a0b\u5f00\u53d1\u7684\u8bdd\uff0c\u8fd9\u95e8\u8bfe\u6709\u4e30\u5bcc\u4e14\u89c4\u8303\u7684\u4ee3\u7801\u793a\u4f8b\u4ee5\u4f9b\u53c2\u8003\u3002 \u6240\u6709\u7684\u8bfe\u7a0b\u8d44\u6e90\u5168\u90e8\u5f00\u6e90\u5e76\u4e14\u6709\u4e2d\u6587\u548c\u82f1\u6587\u4e24\u4e2a\u7248\u672c\uff0cB\u7ad9\u548c\u6cb9\u7ba1\u5206\u522b\u6709\u4e2d\u6587\u548c\u82f1\u6587\u7684\u8bfe\u7a0b\u5f55\u5f71\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://mlc.ai/summer22-zh/ \u8bfe\u7a0b\u89c6\u9891\uff1a Bilibili \u8bfe\u7a0b\u7b14\u8bb0\uff1a https://mlc.ai/zh/index.html \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://github.com/mlc-ai/notebooks/blob/main/assignment","title":"Machine Learning Compilation"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%B3%BB%E7%BB%9F/MLC/#machine-learning-compilation","text":"","title":"Machine Learning Compilation"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%B3%BB%E7%BB%9F/MLC/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aBilibili \u5927\u5b66 \u5148\u4fee\u8981\u6c42\uff1a\u673a\u5668\u5b66\u4e60/\u6df1\u5ea6\u5b66\u4e60\u57fa\u7840 \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a30\u5c0f\u65f6 \u8fd9\u95e8\u8bfe\u662f\u673a\u5668\u5b66\u4e60\u7f16\u8bd1\u9886\u57df\u7684\u9876\u5c16\u5b66\u8005\u9648\u5929\u5947\u57282022\u5e74\u6691\u671f\u5f00\u8bbe\u7684\u4e00\u95e8\u5728\u7ebf\u8bfe\u7a0b\u3002\u5176\u5b9e\u673a\u5668\u5b66\u4e60\u7f16\u8bd1\u65e0\u8bba\u5728\u5de5\u4e1a\u754c\u8fd8\u662f\u5b66\u672f\u754c\u4ecd\u7136\u662f\u4e00\u4e2a\u975e\u5e38\u524d\u6cbf\u4e14\u5feb\u901f\u66f4\u8fed\u7684\u9886\u57df\uff0c\u56fd\u5185\u5916\u6b64\u524d\u8fd8\u6ca1\u6709\u4e3a\u8fd9\u4e2a\u65b9\u5411\u4e13\u95e8\u5f00\u8bbe\u7684\u76f8\u5173\u8bfe\u7a0b\u3002\u56e0\u6b64\u5982\u679c\u5bf9\u673a\u5668\u5b66\u4e60\u7f16\u8bd1\u611f\u5174\u8da3\u60f3\u6709\u4e2a\u5168\u8c8c\u6027\u7684\u611f\u77e5\u7684\u8bdd\uff0c\u53ef\u4ee5\u5b66\u4e60\u4e00\u4e0b\u8fd9\u95e8\u8bfe\u3002 \u672c\u8bfe\u7a0b\u4e3b\u8981\u4ee5 Apache TVM \u8fd9\u4e00\u4e3b\u6d41\u7684\u673a\u5668\u5b66\u4e60\u7f16\u8bd1\u6846\u67b6\u4e3a\u4f8b\uff08\u9648\u5929\u5947\u662f\u8fd9\u4e2a\u6846\u67b6\u7684\u521b\u59cb\u4eba\u4e4b\u4e00\uff09\uff0c\u805a\u7126\u4e8e\u5982\u4f55\u5c06\u5f00\u53d1\u6a21\u5f0f\u4e0b\uff08\u5982 Tensorflow, Pytorch, Jax\uff09\u7684\u5404\u7c7b\u673a\u5668\u5b66\u4e60\u6a21\u578b\uff0c\u901a\u8fc7\u4e00\u5957\u666e\u9002\u7684\u62bd\u8c61\u548c\u4f18\u5316\u7b97\u6cd5\uff0c\u53d8\u6362\u4e3a\u62e5\u6709\u66f4\u9ad8\u6027\u80fd\u5e76\u4e14\u9002\u914d\u5404\u7c7b\u5e95\u5c42\u786c\u4ef6\u7684\u90e8\u7f72\u6a21\u5f0f\u3002\u8bfe\u7a0b\u8bb2\u6388\u7684\u77e5\u8bc6\u70b9\u90fd\u662f\u76f8\u5bf9 High-Level \u7684\u5b8f\u89c2\u6982\u5ff5\uff0c\u540c\u65f6\u6bcf\u8282\u8bfe\u90fd\u4f1a\u6709\u4e00\u4e2a\u914d\u5957\u7684 Jupyter Notebook \u6765\u901a\u8fc7\u5177\u4f53\u7684\u4ee3\u7801\u8bb2\u89e3\u77e5\u8bc6\u70b9\uff0c\u56e0\u6b64\u5982\u679c\u4ece\u4e8b TVM \u76f8\u5173\u7684\u7f16\u7a0b\u5f00\u53d1\u7684\u8bdd\uff0c\u8fd9\u95e8\u8bfe\u6709\u4e30\u5bcc\u4e14\u89c4\u8303\u7684\u4ee3\u7801\u793a\u4f8b\u4ee5\u4f9b\u53c2\u8003\u3002 \u6240\u6709\u7684\u8bfe\u7a0b\u8d44\u6e90\u5168\u90e8\u5f00\u6e90\u5e76\u4e14\u6709\u4e2d\u6587\u548c\u82f1\u6587\u4e24\u4e2a\u7248\u672c\uff0cB\u7ad9\u548c\u6cb9\u7ba1\u5206\u522b\u6709\u4e2d\u6587\u548c\u82f1\u6587\u7684\u8bfe\u7a0b\u5f55\u5f71\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%B3%BB%E7%BB%9F/MLC/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a https://mlc.ai/summer22-zh/ \u8bfe\u7a0b\u89c6\u9891\uff1a Bilibili \u8bfe\u7a0b\u7b14\u8bb0\uff1a https://mlc.ai/zh/index.html \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://github.com/mlc-ai/notebooks/blob/main/assignment","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CMU10-708/","text":"CMU 10-708: Probabilistic Graphical Models \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aCMU \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Reinforcement Learning \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://sailinglab.github.io/pgm-spring-2019/ \u8fd9\u4e2a\u7f51\u7ad9\u5305\u542b\u4e86\u6240\u6709\u7684\u8d44\u6e90\uff1aslides, nots, video, homework, project \u8fd9\u95e8\u8bfe\u7a0b\u662f CMU \u7684\u56fe\u6a21\u578b\u57fa\u7840 + \u8fdb\u9636\u8bfe\uff0c\u6388\u8bfe\u8001\u5e08\u4e3a Eric P. Xing\uff0c\u6db5\u76d6\u4e86\u56fe\u6a21\u578b\u57fa\u7840\uff0c\u4e0e\u795e\u7ecf\u7f51\u7edc\u7684\u7ed3\u5408\uff0c\u5728\u5f3a\u5316\u5b66\u4e60\u4e2d\u7684\u5e94\u7528\uff0c\u4ee5\u53ca\u975e\u53c2\u6570\u65b9\u6cd5\u3002\u76f8\u5f53\u786c\u6838","title":"CMU 10-708: Probabilistic Graphical Models"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CMU10-708/#cmu-10-708-probabilistic-graphical-models","text":"","title":"CMU 10-708: Probabilistic Graphical Models"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CMU10-708/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aCMU \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Reinforcement Learning \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://sailinglab.github.io/pgm-spring-2019/ \u8fd9\u4e2a\u7f51\u7ad9\u5305\u542b\u4e86\u6240\u6709\u7684\u8d44\u6e90\uff1aslides, nots, video, homework, project \u8fd9\u95e8\u8bfe\u7a0b\u662f CMU \u7684\u56fe\u6a21\u578b\u57fa\u7840 + \u8fdb\u9636\u8bfe\uff0c\u6388\u8bfe\u8001\u5e08\u4e3a Eric P. Xing\uff0c\u6db5\u76d6\u4e86\u56fe\u6a21\u578b\u57fa\u7840\uff0c\u4e0e\u795e\u7ecf\u7f51\u7edc\u7684\u7ed3\u5408\uff0c\u5728\u5f3a\u5316\u5b66\u4e60\u4e2d\u7684\u5e94\u7528\uff0c\u4ee5\u53ca\u975e\u53c2\u6570\u65b9\u6cd5\u3002\u76f8\u5f53\u786c\u6838","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CS229M/","text":"STATS214 / CS229M: Machine Learning Theory \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Statistics \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/stats214/ \u7ecf\u5178\u5b66\u4e60\u7406\u8bba + \u6700\u65b0\u6df1\u5ea6\u5b66\u4e60\u7406\u8bba\uff0c\u975e\u5e38\u786c\u6838\u3002\u6388\u8bfe\u8001\u5e08\u4e4b\u524d\u662f Percy Liang\uff0c\u73b0\u5728\u662f Tengyu Ma","title":"Stanford STATS214 / CS229M: Machine Learning Theory"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CS229M/#stats214-cs229m-machine-learning-theory","text":"","title":"STATS214 / CS229M: Machine Learning Theory"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CS229M/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Statistics \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/stats214/ \u7ecf\u5178\u5b66\u4e60\u7406\u8bba + \u6700\u65b0\u6df1\u5ea6\u5b66\u4e60\u7406\u8bba\uff0c\u975e\u5e38\u786c\u6838\u3002\u6388\u8bfe\u8001\u5e08\u4e4b\u524d\u662f Percy Liang\uff0c\u73b0\u5728\u662f Tengyu Ma","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STA4273/","text":"STA 4273 Winter 2021: Minimizing Expectations \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aU Toronto \u5148\u4fee\u8981\u6c42\uff1aBayesian Inference, Reinforcement Learning \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://www.cs.toronto.edu/~cmaddis/courses/sta4273_w21/ \u8fd9\u662f\u4e00\u95e8\u8f83\u4e3a\u8fdb\u9636\u7684 Ph.D. \u7814\u7a76\u8bfe\u7a0b\uff0c\u6838\u5fc3\u5185\u5bb9\u662f inference \u548c control \u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u6388\u8bfe\u8001\u5e08\u4e3a Chris Maddison (AlphaGo founding member, NeurIPS 14 best paper)","title":"U Toronto STA 4273 Winter 2021: Minimizing Expectations"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STA4273/#sta-4273-winter-2021-minimizing-expectations","text":"","title":"STA 4273 Winter 2021: Minimizing Expectations"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STA4273/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aU Toronto \u5148\u4fee\u8981\u6c42\uff1aBayesian Inference, Reinforcement Learning \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://www.cs.toronto.edu/~cmaddis/courses/sta4273_w21/ \u8fd9\u662f\u4e00\u95e8\u8f83\u4e3a\u8fdb\u9636\u7684 Ph.D. \u7814\u7a76\u8bfe\u7a0b\uff0c\u6838\u5fc3\u5185\u5bb9\u662f inference \u548c control \u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u6388\u8bfe\u8001\u5e08\u4e3a Chris Maddison (AlphaGo founding member, NeurIPS 14 best paper)","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STAT8201/","text":"Columbia STAT 8201: Deep Generative Models \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aColumbia University \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Graphical Models \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://stat.columbia.edu/~cunningham/teaching/GR8201/ \u8fd9\u95e8\u8bfe\u662f\u4e00\u95e8 PhD \u8ba8\u8bba\u73ed\uff0c\u6bcf\u5468\u7684\u5185\u5bb9\u662f\u5c55\u793a + \u8ba8\u8bba\u8bba\u6587\uff0c\u6388\u8bfe\u8001\u5e08\u662f John Cunningham\u3002Deep Generative Models \uff08\u6df1\u5ea6\u751f\u6210\u6a21\u578b\uff09 \u662f\u56fe\u6a21\u578b\u4e0e\u795e\u7ecf\u7f51\u7edc\u7684\u7ed3\u5408\uff0c\u4e5f\u662f\u73b0\u4ee3\u673a\u5668\u5b66\u4e60\u6700\u91cd\u8981\u7684\u65b9\u5411\u4e4b\u4e00","title":"Columbia STAT 8201: Deep Generative Models"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STAT8201/#columbia-stat-8201-deep-generative-models","text":"","title":"Columbia STAT 8201: Deep Generative Models"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STAT8201/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aColumbia University \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Graphical Models \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://stat.columbia.edu/~cunningham/teaching/GR8201/ \u8fd9\u95e8\u8bfe\u662f\u4e00\u95e8 PhD \u8ba8\u8bba\u73ed\uff0c\u6bcf\u5468\u7684\u5185\u5bb9\u662f\u5c55\u793a + \u8ba8\u8bba\u8bba\u6587\uff0c\u6388\u8bfe\u8001\u5e08\u662f John Cunningham\u3002Deep Generative Models \uff08\u6df1\u5ea6\u751f\u6210\u6a21\u578b\uff09 \u662f\u56fe\u6a21\u578b\u4e0e\u795e\u7ecf\u7f51\u7edc\u7684\u7ed3\u5408\uff0c\u4e5f\u662f\u73b0\u4ee3\u673a\u5668\u5b66\u4e60\u6700\u91cd\u8981\u7684\u65b9\u5411\u4e4b\u4e00","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/","text":"\u673a\u5668\u5b66\u4e60\u8fdb\u9636 \u6b64\u8def\u7ebf\u56fe\u9002\u7528\u4e8e\u5df2\u7ecf\u5b66\u8fc7\u4e86\u57fa\u7840\u673a\u5668\u5b66\u4e60 (ML, NLP, CV, RL) \u7684\u540c\u5b66 (\u9ad8\u5e74\u7ea7\u672c\u79d1\u751f\u6216\u4f4e\u5e74\u7ea7\u7814\u7a76\u751f)\uff0c\u5df2\u7ecf\u53d1\u8868\u8fc7\u81f3\u5c11\u4e00\u7bc7\u9876\u4f1a\u8bba\u6587 (NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, CVPR, ICCV) \u60f3\u8981\u8d70\u673a\u5668\u5b66\u4e60\u79d1\u7814\u8def\u7ebf\u7684\u9009\u624b\u3002 \u6b64\u8def\u7ebf\u7684\u76ee\u6807\u662f\u4e3a\u8bfb\u61c2\u4e0e\u53d1\u8868\u673a\u5668\u5b66\u4e60\u9876\u4f1a\u8bba\u6587\u6253\u4e0b\u7406\u8bba\u57fa\u7840\uff0c\u7279\u522b\u662f Probabilistic Methods \u8fd9\u4e2a track \u4e0b\u7684\u6587\u7ae0 \u673a\u5668\u5b66\u4e60\u8fdb\u9636\u53ef\u80fd\u5b58\u5728\u591a\u79cd\u4e0d\u540c\u7684\u5b66\u4e60\u8def\u7ebf\uff0c\u6b64\u8def\u7ebf\u53ea\u80fd\u4ee3\u8868\u4f5c\u8005 Yao Fu \u6240\u7406\u89e3\u7684\u6700\u4f73\u8def\u5f84\uff0c\u4fa7\u91cd\u4e8e\u8d1d\u53f6\u65af\u5b66\u6d3e\u4e0b\u7684\u6982\u7387\u5efa\u6a21\u65b9\u6cd5\uff0c\u4e5f\u4f1a\u6d89\u53ca\u5230\u5404\u9879\u76f8\u5173\u5b66\u79d1\u7684\u4ea4\u53c9\u77e5\u8bc6\u3002 \u5fc5\u8bfb\u6559\u6750 PRML: Pattern Recognition and Machine Learning. Christopher Bishop \u7ecf\u5178\u8d1d\u53f6\u65af\u5b66\u6d3e\u6559\u6750 AoS: All of Statistics. Larry Wasserman \u7ecf\u5178\u9891\u7387\u5b66\u6d3e\u6559\u6750 \u6240\u4ee5\u8fd9\u4e24\u672c\u4e66\u521a\u597d\u76f8\u8f85\u76f8\u6210 \u5b57\u5178 MLAPP: Machine Learning: A Probabilistic Perspective. Kevin Murphy Convex Optimization. Stephen Boyd and Lieven Vandenberghe \u8fdb\u9636\u4e66\u7c4d W&J: Graphical Models, Exponential Families, and Variational Inference. Martin Wainwright and Michael Jordan Theory of Point Estimation. E. L. Lehmann and George Casella \u5982\u4f55\u9605\u8bfb Guidelines \u5fc5\u8bfb\u6559\u6750\u5c31\u662f\u4e00\u5b9a\u8981\u8bfb\u7684\u6559\u6750 \u5b57\u5178\u7684\u610f\u601d\u662f\uff0c\u4e00\u822c\u60c5\u51b5\u4e0b\u4e0d\u7ba1\u5b83\uff0c\u4f46\u5f53\u9047\u5230\u4e86\u4e0d\u61c2\u7684\u6982\u5ff5\u7684\u65f6\u5019\uff0c\u5c31\u53bb\u5b57\u5178\u91cc\u9762\u67e5\uff08\u800c\u4e0d\u662f\u7ef4\u57fa\u767e\u79d1\uff09 \u8fdb\u9636\u4e66\u7c4d\u5148\u4e0d\u8bfb\uff0c\u5148\u8bfb\u5b8c\u5fc5\u8bfb\u4e66\u7c4d\u3002\u5fc5\u8bfb\u4e66\u7c4d\u4e00\u822c\u90fd\u662f\u8981\u524d\u524d\u540e\u540e\u53cd\u590d\u770b\u8fc7 N \u904d\u624d\u7b97\u8bfb\u5b8c \u8bfb\u7684\u8fc7\u7a0b\u4e2d\uff0c\u6700\u91cd\u8981\u7684\u8bfb\u6cd5\u5c31\u662f\u5bf9\u6bd4\u9605\u8bfb (contrastive-comparative reading)\uff1a\u540c\u65f6\u6253\u5f00\u4e24\u672c\u4e66\u8bb2\u540c\u4e00\u4e3b\u9898\u7684\u7ae0\u8282\uff0c\u7136\u540e\u5bf9\u6bd4\u76f8\u540c\u70b9\u548c\u4e0d\u540c\u70b9\u548c\u8054\u7cfb \u8bfb\u7684\u8fc7\u7a0b\u4e2d\uff0c\u5c3d\u91cf\u53bb\u56de\u60f3\u4e4b\u524d\u8bfb\u8fc7\u7684\u8bba\u6587\uff0c\u6bd4\u8f83\u8bba\u6587\u548c\u6559\u6750\u7684\u76f8\u540c\u70b9\u4e0e\u4e0d\u540c\u70b9 \u57fa\u7840\u8def\u5f84 \u5148\u8bfb AoS \u7b2c\u516d\u7ae0: Models, Statistical Inference and Learning\uff0c\u8fd9\u4e00\u90e8\u5206\u662f\u6700\u57fa\u7840\u7684\u79d1\u666e \u7136\u540e\u8bfb PRML \u7b2c 10, 11 \u7ae0 \u7b2c 10 \u7ae0\u7684\u5185\u5bb9\u662f Variational Inference, \u7b2c 11 \u7ae0\u7684\u5185\u5bb9\u662f MCMC, \u8fd9\u4e24\u79cd\u65b9\u6cd5\u662f\u8d1d\u53f6\u65af\u63a8\u65ad\u7684\u4e24\u6761\u6700\u4e3b\u8981\u8def\u7ebf \u5982\u679c\u5728\u8bfb PRML \u7684\u8fc7\u7a0b\u4e2d\u53d1\u73b0\u6709\u4efb\u4f55\u4e0d\u61c2\u7684\u540d\u8bcd\uff0c\u5c31\u53bb\u7ffb\u524d\u9762\u7684\u7ae0\u8282\u3002\u5f88\u5927\u6982\u7387\u80fd\u591f\u5728\u7b2c 3\uff0c4 \u7ae0\u627e\u5230\u76f8\u5bf9\u5e94\u7684\u5b9a\u4e49\uff1b\u5982\u679c\u627e\u4e0d\u5230\u6216\u8005\u4e0d\u591f\u8be6\u7ec6\uff0c\u5c31\u53bb\u67e5 MLAPP AoS \u7b2c 8 \u7ae0 (Parametric Inference) \u548c\u7b2c 11 \u7ae0 (Bayesian Inference) \u4e5f\u53ef\u4ee5\u4f5c\u4e3a\u53c2\u8003\u3002\u6700\u597d\u7684\u65b9\u6cd5\u662f\u591a\u672c\u4e66\u5bf9\u6bd4\u9605\u8bfb\uff0c\u6d41\u7a0b\u5982\u4e0b \u5047\u8bbe\u6211\u5728\u8bfb PRML \u7b2c 10 \u7ae0\u7684\u65f6\u5019\u53d1\u73b0\u4e86\u4e00\u4e2a\u4e0d\u61c2\u7684\u8bcd\uff1aposterior inference \u4e8e\u662f\u6211\u5f80\u524d\u7ffb\uff0c\u7ffb\u5230\u4e86\u7b2c 3 \u7ae0 (Linear Model for Regression)\uff0c\u770b\u5230\u4e86\u6700\u7b80\u5355\u7684 posterior \u7136\u540e\u6211\u63a5\u7740\u7ffb AoS\uff0c\u7ffb\u5230\u4e86\u7b2c 11 \u7ae0\uff0c\u4e5f\u6709\u5bf9 posterior \u7684\u63cf\u8ff0 \u7136\u540e\u6211\u5bf9\u6bd4 PRML \u7b2c 10 \u7ae0\uff0c\u7b2c 3 \u7ae0\uff0cAoS \u7b2c 11 \u7ae0\uff0c\u4e09\u5904\u4e0d\u540c\u5730\u65b9\u5bf9 posterior \u7684\u89e3\u8bfb\uff0c\u6bd4\u8f83\u5176\u76f8\u540c\u70b9\u548c\u4e0d\u540c\u70b9\u548c\u8054\u7cfb \u8bfb\u5b8c PRML \u7b2c 10 \u548c 11 \u7ae0\u4e4b\u540e\uff0c\u63a5\u7740\u8bfb AoS \u7b2c 24 \u7ae0 (Simulation Methods)\uff0c\u7136\u540e\u628a\u5b83\u548c PRML \u7b2c 11 \u7ae0\u5bf9\u6bd4\u9605\u8bfb -- \u8fd9\u4fe9\u90fd\u662f\u8bb2 MCMC \u5982\u679c\u5230\u6b64\u5904\u53d1\u73b0\u8fd8\u6709\u57fa\u7840\u6982\u5ff5\u8bfb\u4e0d\u61c2\uff0c\u5c31\u56de\u5230 PRML \u7b2c 3 \u7ae0\uff0c\u628a\u5b83\u548c AoS \u7b2c 11 \u7ae0\u5bf9\u6bd4\u9605\u8bfb Again\uff0c\u5bf9\u6bd4\u9605\u8bfb\u975e\u5e38\u91cd\u8981\uff0c\u4e00\u5b9a\u8981\u628a\u4e0d\u540c\u672c\u4e66\u7684\u7c7b\u4f3c\u5185\u5bb9\u540c\u65f6\u6446\u5728\u9762\u524d\u76f8\u4e92\u5bf9\u6bd4\uff0c\u8fd9\u6837\u53ef\u4ee5\u663e\u8457\u589e\u5f3a\u8bb0\u5fc6 \u7136\u540e\u8bfb PRML \u7b2c 13 \u7ae0\uff08\u8df3\u8fc7\u7b2c 12 \u7ae0\uff09\uff0c\u8fd9\u4e00\u7ae0\u53ef\u4ee5\u548c MLAPP \u7684\u7b2c 17, 18 \u7ae0\u5bf9\u6bd4\u9605\u8bfb MLAPP \u7b2c 17 \u7ae0\u662f PRML \u7b2c 13.2 \u7ae0\u7684\u8be6\u7ec6\u7248\uff0c\u4e3b\u8981\u8bb2 HMM MLAPP \u7b2c 18 \u7ae0\u662f PRML \u7b2c 13.3 \u7ae0\u7684\u8be6\u7ec6\u7248\uff0c\u4e3b\u8981\u8bb2 LDS \u8bfb\u5b8c PRML \u7b2c 13 \u7ae0\u4e4b\u540e\uff0c\u518d\u53bb\u8bfb PRML \u7b2c 8 \u7ae0 (Graphical Models) -- \u6b64\u65f6\u8fd9\u90e8\u5206\u5e94\u8be5\u4f1a\u8bfb\u5f97\u5f88\u8f7b\u677e \u4ee5\u4e0a\u7684\u5185\u5bb9\u53ef\u4ee5\u8fdb\u4e00\u6b65\u5bf9\u7167 CMU 10-708 PGM \u8bfe\u7a0b\u6750\u6599 \u5230\u76ee\u524d\u4e3a\u6b62\uff0c\u5e94\u8be5\u80fd\u591f\u638c\u63e1 \u6982\u7387\u6a21\u578b\u7684\u57fa\u7840\u5b9a\u4e49 \u7cbe\u51c6\u63a8\u65ad - Sum-Product \u8fd1\u4f3c\u63a8\u65ad - MCMC \u8fd1\u4f3c\u63a8\u65ad - VI \u7136\u540e\u5c31\u53ef\u4ee5\u53bb\u505a\u66f4\u8fdb\u9636\u7684\u5185\u5bb9","title":"\u8fdb\u9636\u8def\u7ebf\u56fe"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_1","text":"\u6b64\u8def\u7ebf\u56fe\u9002\u7528\u4e8e\u5df2\u7ecf\u5b66\u8fc7\u4e86\u57fa\u7840\u673a\u5668\u5b66\u4e60 (ML, NLP, CV, RL) \u7684\u540c\u5b66 (\u9ad8\u5e74\u7ea7\u672c\u79d1\u751f\u6216\u4f4e\u5e74\u7ea7\u7814\u7a76\u751f)\uff0c\u5df2\u7ecf\u53d1\u8868\u8fc7\u81f3\u5c11\u4e00\u7bc7\u9876\u4f1a\u8bba\u6587 (NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, CVPR, ICCV) \u60f3\u8981\u8d70\u673a\u5668\u5b66\u4e60\u79d1\u7814\u8def\u7ebf\u7684\u9009\u624b\u3002 \u6b64\u8def\u7ebf\u7684\u76ee\u6807\u662f\u4e3a\u8bfb\u61c2\u4e0e\u53d1\u8868\u673a\u5668\u5b66\u4e60\u9876\u4f1a\u8bba\u6587\u6253\u4e0b\u7406\u8bba\u57fa\u7840\uff0c\u7279\u522b\u662f Probabilistic Methods \u8fd9\u4e2a track \u4e0b\u7684\u6587\u7ae0 \u673a\u5668\u5b66\u4e60\u8fdb\u9636\u53ef\u80fd\u5b58\u5728\u591a\u79cd\u4e0d\u540c\u7684\u5b66\u4e60\u8def\u7ebf\uff0c\u6b64\u8def\u7ebf\u53ea\u80fd\u4ee3\u8868\u4f5c\u8005 Yao Fu \u6240\u7406\u89e3\u7684\u6700\u4f73\u8def\u5f84\uff0c\u4fa7\u91cd\u4e8e\u8d1d\u53f6\u65af\u5b66\u6d3e\u4e0b\u7684\u6982\u7387\u5efa\u6a21\u65b9\u6cd5\uff0c\u4e5f\u4f1a\u6d89\u53ca\u5230\u5404\u9879\u76f8\u5173\u5b66\u79d1\u7684\u4ea4\u53c9\u77e5\u8bc6\u3002","title":"\u673a\u5668\u5b66\u4e60\u8fdb\u9636"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_2","text":"PRML: Pattern Recognition and Machine Learning. Christopher Bishop \u7ecf\u5178\u8d1d\u53f6\u65af\u5b66\u6d3e\u6559\u6750 AoS: All of Statistics. Larry Wasserman \u7ecf\u5178\u9891\u7387\u5b66\u6d3e\u6559\u6750 \u6240\u4ee5\u8fd9\u4e24\u672c\u4e66\u521a\u597d\u76f8\u8f85\u76f8\u6210","title":"\u5fc5\u8bfb\u6559\u6750"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_3","text":"MLAPP: Machine Learning: A Probabilistic Perspective. Kevin Murphy Convex Optimization. Stephen Boyd and Lieven Vandenberghe","title":"\u5b57\u5178"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_4","text":"W&J: Graphical Models, Exponential Families, and Variational Inference. Martin Wainwright and Michael Jordan Theory of Point Estimation. E. L. Lehmann and George Casella","title":"\u8fdb\u9636\u4e66\u7c4d"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_5","text":"","title":"\u5982\u4f55\u9605\u8bfb"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#guidelines","text":"\u5fc5\u8bfb\u6559\u6750\u5c31\u662f\u4e00\u5b9a\u8981\u8bfb\u7684\u6559\u6750 \u5b57\u5178\u7684\u610f\u601d\u662f\uff0c\u4e00\u822c\u60c5\u51b5\u4e0b\u4e0d\u7ba1\u5b83\uff0c\u4f46\u5f53\u9047\u5230\u4e86\u4e0d\u61c2\u7684\u6982\u5ff5\u7684\u65f6\u5019\uff0c\u5c31\u53bb\u5b57\u5178\u91cc\u9762\u67e5\uff08\u800c\u4e0d\u662f\u7ef4\u57fa\u767e\u79d1\uff09 \u8fdb\u9636\u4e66\u7c4d\u5148\u4e0d\u8bfb\uff0c\u5148\u8bfb\u5b8c\u5fc5\u8bfb\u4e66\u7c4d\u3002\u5fc5\u8bfb\u4e66\u7c4d\u4e00\u822c\u90fd\u662f\u8981\u524d\u524d\u540e\u540e\u53cd\u590d\u770b\u8fc7 N \u904d\u624d\u7b97\u8bfb\u5b8c \u8bfb\u7684\u8fc7\u7a0b\u4e2d\uff0c\u6700\u91cd\u8981\u7684\u8bfb\u6cd5\u5c31\u662f\u5bf9\u6bd4\u9605\u8bfb (contrastive-comparative reading)\uff1a\u540c\u65f6\u6253\u5f00\u4e24\u672c\u4e66\u8bb2\u540c\u4e00\u4e3b\u9898\u7684\u7ae0\u8282\uff0c\u7136\u540e\u5bf9\u6bd4\u76f8\u540c\u70b9\u548c\u4e0d\u540c\u70b9\u548c\u8054\u7cfb \u8bfb\u7684\u8fc7\u7a0b\u4e2d\uff0c\u5c3d\u91cf\u53bb\u56de\u60f3\u4e4b\u524d\u8bfb\u8fc7\u7684\u8bba\u6587\uff0c\u6bd4\u8f83\u8bba\u6587\u548c\u6559\u6750\u7684\u76f8\u540c\u70b9\u4e0e\u4e0d\u540c\u70b9","title":"Guidelines"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_6","text":"\u5148\u8bfb AoS \u7b2c\u516d\u7ae0: Models, Statistical Inference and Learning\uff0c\u8fd9\u4e00\u90e8\u5206\u662f\u6700\u57fa\u7840\u7684\u79d1\u666e \u7136\u540e\u8bfb PRML \u7b2c 10, 11 \u7ae0 \u7b2c 10 \u7ae0\u7684\u5185\u5bb9\u662f Variational Inference, \u7b2c 11 \u7ae0\u7684\u5185\u5bb9\u662f MCMC, \u8fd9\u4e24\u79cd\u65b9\u6cd5\u662f\u8d1d\u53f6\u65af\u63a8\u65ad\u7684\u4e24\u6761\u6700\u4e3b\u8981\u8def\u7ebf \u5982\u679c\u5728\u8bfb PRML \u7684\u8fc7\u7a0b\u4e2d\u53d1\u73b0\u6709\u4efb\u4f55\u4e0d\u61c2\u7684\u540d\u8bcd\uff0c\u5c31\u53bb\u7ffb\u524d\u9762\u7684\u7ae0\u8282\u3002\u5f88\u5927\u6982\u7387\u80fd\u591f\u5728\u7b2c 3\uff0c4 \u7ae0\u627e\u5230\u76f8\u5bf9\u5e94\u7684\u5b9a\u4e49\uff1b\u5982\u679c\u627e\u4e0d\u5230\u6216\u8005\u4e0d\u591f\u8be6\u7ec6\uff0c\u5c31\u53bb\u67e5 MLAPP AoS \u7b2c 8 \u7ae0 (Parametric Inference) \u548c\u7b2c 11 \u7ae0 (Bayesian Inference) \u4e5f\u53ef\u4ee5\u4f5c\u4e3a\u53c2\u8003\u3002\u6700\u597d\u7684\u65b9\u6cd5\u662f\u591a\u672c\u4e66\u5bf9\u6bd4\u9605\u8bfb\uff0c\u6d41\u7a0b\u5982\u4e0b \u5047\u8bbe\u6211\u5728\u8bfb PRML \u7b2c 10 \u7ae0\u7684\u65f6\u5019\u53d1\u73b0\u4e86\u4e00\u4e2a\u4e0d\u61c2\u7684\u8bcd\uff1aposterior inference \u4e8e\u662f\u6211\u5f80\u524d\u7ffb\uff0c\u7ffb\u5230\u4e86\u7b2c 3 \u7ae0 (Linear Model for Regression)\uff0c\u770b\u5230\u4e86\u6700\u7b80\u5355\u7684 posterior \u7136\u540e\u6211\u63a5\u7740\u7ffb AoS\uff0c\u7ffb\u5230\u4e86\u7b2c 11 \u7ae0\uff0c\u4e5f\u6709\u5bf9 posterior \u7684\u63cf\u8ff0 \u7136\u540e\u6211\u5bf9\u6bd4 PRML \u7b2c 10 \u7ae0\uff0c\u7b2c 3 \u7ae0\uff0cAoS \u7b2c 11 \u7ae0\uff0c\u4e09\u5904\u4e0d\u540c\u5730\u65b9\u5bf9 posterior \u7684\u89e3\u8bfb\uff0c\u6bd4\u8f83\u5176\u76f8\u540c\u70b9\u548c\u4e0d\u540c\u70b9\u548c\u8054\u7cfb \u8bfb\u5b8c PRML \u7b2c 10 \u548c 11 \u7ae0\u4e4b\u540e\uff0c\u63a5\u7740\u8bfb AoS \u7b2c 24 \u7ae0 (Simulation Methods)\uff0c\u7136\u540e\u628a\u5b83\u548c PRML \u7b2c 11 \u7ae0\u5bf9\u6bd4\u9605\u8bfb -- \u8fd9\u4fe9\u90fd\u662f\u8bb2 MCMC \u5982\u679c\u5230\u6b64\u5904\u53d1\u73b0\u8fd8\u6709\u57fa\u7840\u6982\u5ff5\u8bfb\u4e0d\u61c2\uff0c\u5c31\u56de\u5230 PRML \u7b2c 3 \u7ae0\uff0c\u628a\u5b83\u548c AoS \u7b2c 11 \u7ae0\u5bf9\u6bd4\u9605\u8bfb Again\uff0c\u5bf9\u6bd4\u9605\u8bfb\u975e\u5e38\u91cd\u8981\uff0c\u4e00\u5b9a\u8981\u628a\u4e0d\u540c\u672c\u4e66\u7684\u7c7b\u4f3c\u5185\u5bb9\u540c\u65f6\u6446\u5728\u9762\u524d\u76f8\u4e92\u5bf9\u6bd4\uff0c\u8fd9\u6837\u53ef\u4ee5\u663e\u8457\u589e\u5f3a\u8bb0\u5fc6 \u7136\u540e\u8bfb PRML \u7b2c 13 \u7ae0\uff08\u8df3\u8fc7\u7b2c 12 \u7ae0\uff09\uff0c\u8fd9\u4e00\u7ae0\u53ef\u4ee5\u548c MLAPP \u7684\u7b2c 17, 18 \u7ae0\u5bf9\u6bd4\u9605\u8bfb MLAPP \u7b2c 17 \u7ae0\u662f PRML \u7b2c 13.2 \u7ae0\u7684\u8be6\u7ec6\u7248\uff0c\u4e3b\u8981\u8bb2 HMM MLAPP \u7b2c 18 \u7ae0\u662f PRML \u7b2c 13.3 \u7ae0\u7684\u8be6\u7ec6\u7248\uff0c\u4e3b\u8981\u8bb2 LDS \u8bfb\u5b8c PRML \u7b2c 13 \u7ae0\u4e4b\u540e\uff0c\u518d\u53bb\u8bfb PRML \u7b2c 8 \u7ae0 (Graphical Models) -- \u6b64\u65f6\u8fd9\u90e8\u5206\u5e94\u8be5\u4f1a\u8bfb\u5f97\u5f88\u8f7b\u677e \u4ee5\u4e0a\u7684\u5185\u5bb9\u53ef\u4ee5\u8fdb\u4e00\u6b65\u5bf9\u7167 CMU 10-708 PGM \u8bfe\u7a0b\u6750\u6599 \u5230\u76ee\u524d\u4e3a\u6b62\uff0c\u5e94\u8be5\u80fd\u591f\u638c\u63e1 \u6982\u7387\u6a21\u578b\u7684\u57fa\u7840\u5b9a\u4e49 \u7cbe\u51c6\u63a8\u65ad - Sum-Product \u8fd1\u4f3c\u63a8\u65ad - MCMC \u8fd1\u4f3c\u63a8\u65ad - VI \u7136\u540e\u5c31\u53ef\u4ee5\u53bb\u505a\u66f4\u8fdb\u9636\u7684\u5185\u5bb9","title":"\u57fa\u7840\u8def\u5f84"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224n/","text":"CS224n: Natural Language Processing \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6df1\u5ea6\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 Stanford \u7684 NLP \u5165\u95e8\u8bfe\u7a0b\uff0c\u7531\u81ea\u7136\u8bed\u8a00\u5904\u7406\u9886\u57df\u7684\u5de8\u4f6c Chris Manning \u9886\u8854\u6559\u6388\uff08word2vec \u7b97\u6cd5\u7684\u5f00\u521b\u8005\uff09\u3002\u5185\u5bb9\u8986\u76d6\u4e86\u8bcd\u5411\u91cf\u3001RNN\u3001LSTM\u3001Seq2Seq \u6a21\u578b\u3001\u673a\u5668\u7ffb\u8bd1\u3001\u6ce8\u610f\u529b\u673a\u5236\u3001Transformer \u7b49\u7b49 NLP \u9886\u57df\u7684\u6838\u5fc3\u77e5\u8bc6\u70b9\u3002 5 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a\u96be\u5ea6\u5faa\u5e8f\u6e10\u8fdb\uff0c\u5206\u522b\u662f\u8bcd\u5411\u91cf\u3001word2vec \u7b97\u6cd5\u3001Dependency parsing\u3001\u673a\u5668\u7ffb\u8bd1\u4ee5\u53ca Transformer \u7684 fine-tune\u3002 \u6700\u7ec8\u7684\u5927\u4f5c\u4e1a\u662f\u5728 Stanford \u8457\u540d\u7684 SQuAD \u6570\u636e\u96c6\u4e0a\u8bad\u7ec3 QA \u6a21\u578b\uff0c\u6709\u5b66\u751f\u7684\u5927\u4f5c\u4e1a\u751a\u81f3\u76f4\u63a5\u53d1\u8868\u4e86\u9876\u4f1a\u8bba\u6587\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs224n/index.html \u8bfe\u7a0b\u89c6\u9891\uff1aB \u7ad9\u641c\u7d22 CS224n \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a http://web.stanford.edu/class/cs224n/index.html \uff0c5 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a + 1 \u4e2a Final Project \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS224n - GitHub \u4e2d\u3002","title":"Stanford CS224n: Natural Language Processing"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224n/#cs224n-natural-language-processing","text":"","title":"CS224n: Natural Language Processing"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224n/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6df1\u5ea6\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 Stanford \u7684 NLP \u5165\u95e8\u8bfe\u7a0b\uff0c\u7531\u81ea\u7136\u8bed\u8a00\u5904\u7406\u9886\u57df\u7684\u5de8\u4f6c Chris Manning \u9886\u8854\u6559\u6388\uff08word2vec \u7b97\u6cd5\u7684\u5f00\u521b\u8005\uff09\u3002\u5185\u5bb9\u8986\u76d6\u4e86\u8bcd\u5411\u91cf\u3001RNN\u3001LSTM\u3001Seq2Seq \u6a21\u578b\u3001\u673a\u5668\u7ffb\u8bd1\u3001\u6ce8\u610f\u529b\u673a\u5236\u3001Transformer \u7b49\u7b49 NLP \u9886\u57df\u7684\u6838\u5fc3\u77e5\u8bc6\u70b9\u3002 5 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a\u96be\u5ea6\u5faa\u5e8f\u6e10\u8fdb\uff0c\u5206\u522b\u662f\u8bcd\u5411\u91cf\u3001word2vec \u7b97\u6cd5\u3001Dependency parsing\u3001\u673a\u5668\u7ffb\u8bd1\u4ee5\u53ca Transformer \u7684 fine-tune\u3002 \u6700\u7ec8\u7684\u5927\u4f5c\u4e1a\u662f\u5728 Stanford \u8457\u540d\u7684 SQuAD \u6570\u636e\u96c6\u4e0a\u8bad\u7ec3 QA \u6a21\u578b\uff0c\u6709\u5b66\u751f\u7684\u5927\u4f5c\u4e1a\u751a\u81f3\u76f4\u63a5\u53d1\u8868\u4e86\u9876\u4f1a\u8bba\u6587\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224n/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs224n/index.html \u8bfe\u7a0b\u89c6\u9891\uff1aB \u7ad9\u641c\u7d22 CS224n \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a http://web.stanford.edu/class/cs224n/index.html \uff0c5 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a + 1 \u4e2a Final Project","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224n/#_3","text":"@PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS224n - GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224w/","text":"CS224w: Machine Learning with Graphs 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\u8bfe\u7a0b\u4f5c\u4e1a\uff1a http://web.stanford.edu/class/cs224w/ \uff0c6 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a\uff0c3 \u4e2a LaTeX \u4e66\u9762\u4f5c\u4e1a","title":"Stanford CS224w: Machine Learning with Graphs"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224w/#cs224w-machine-learning-with-graphs","text":"","title":"CS224w: Machine Learning with Graphs"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224w/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6df1\u5ea6\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython, LaTeX \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 Stanford \u7684\u56fe\u795e\u7ecf\u7f51\u7edc\u5165\u95e8\u8bfe\uff0c\u8fd9\u95e8\u8bfe\u6211\u6ca1\u6709\u4e0a\u8fc7\uff0c\u4f46\u4f17\u591a\u505a GNN \u7684\u670b\u53cb\u90fd\u5411\u6211\u529b\u8350\u8fc7\u8fd9\u95e8\u8bfe\uff0c\u60f3\u5fc5 Stanford \u7684\u8bfe\u8d28\u91cf\u8fd8\u662f\u4e00\u5982\u65e2\u5f80\u5730\u6709\u4fdd\u8bc1\u7684\u3002\u53e6\u5916\u5c31\u662f\u8fd9\u95e8\u8bfe\u7684\u6388\u8bfe\u8001\u5e08\u975e\u5e38\u5e74\u8f7b\u5e05\u6c14:)","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224w/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs224w/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.youtube.com/watch?v=JAB_plj2rbA \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a http://web.stanford.edu/class/cs224w/ \uff0c6 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a\uff0c3 \u4e2a LaTeX \u4e66\u9762\u4f5c\u4e1a","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS230/","text":"Coursera: Deep Learning \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u673a\u5668\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython 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https://www.coursera.org/specializations/deep-learning \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.coursera.org/specializations/deep-learning \uff0cB\u7ad9\u6709\u642c\u8fd0 \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://www.coursera.org/specializations/deep-learning","title":"Coursera: Deep Learning"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS230/#coursera-deep-learning","text":"","title":"Coursera: Deep Learning"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS230/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u673a\u5668\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 \u5434\u6069\u8fbe\u5728 Coursera \u5f00\u8bbe\u7684\u53e6\u4e00\u95e8\u7f51\u7ea2\u8bfe\u7a0b\uff0c\u5b66\u4e60\u8005\u65e0\u6570\uff0c\u582a\u79f0\u5723\u7ecf\u7ea7\u7684\u6df1\u5ea6\u5b66\u4e60\u5165\u95e8\u8bfe\u3002\u6df1\u5165\u6d45\u51fa\u7684\u8bb2\u89e3\uff0c\u773c\u82b1\u7f2d\u4e71\u7684 Project\u3002\u4ece\u6700\u57fa\u7840\u7684\u795e\u7ecf\u7f51\u7edc\uff0c\u5230 CNN, RNN\uff0c\u518d\u5230\u6700\u8fd1\u5927\u70ed\u7684 Transformer\u3002\u5b66\u5b8c\u8fd9\u95e8\u8bfe\uff0c\u4f60\u5c06\u521d\u6b65\u638c\u63e1\u6df1\u5ea6\u5b66\u4e60\u9886\u57df\u5fc5\u5907\u7684\u77e5\u8bc6\u548c\u6280\u80fd\uff0c\u5e76\u4e14\u53ef\u4ee5\u5728 Kaggle \u4e2d\u53c2\u52a0\u81ea\u5df1\u611f\u5174\u8da3\u7684\u6bd4\u8d5b\uff0c\u5728\u5b9e\u8df5\u4e2d\u953b\u70bc\u81ea\u5df1\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS230/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a https://www.coursera.org/specializations/deep-learning \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.coursera.org/specializations/deep-learning \uff0cB\u7ad9\u6709\u642c\u8fd0 \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://www.coursera.org/specializations/deep-learning","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS231/","text":"CS231n: CNN for Visual Recognition \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u673a\u5668\u5b66\u4e60\u57fa\u7840 \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 Stanford \u7684 CV \u5165\u95e8\u8bfe\uff0c\u7531\u8ba1\u7b97\u673a\u9886\u57df\u7684\u5de8\u4f6c\u674e\u98de\u98de\u9662\u58eb\u9886\u8854\u6559\u6388\uff08CV \u9886\u57df\u5212\u65f6\u4ee3\u7684\u8457\u540d\u6570\u636e\u96c6 ImageNet \u7684\u7814\u7a76\u56e2\u961f\uff09\uff0c\u4f46\u5176\u5185\u5bb9\u76f8\u5bf9\u57fa\u7840\u4e14\u53cb\u597d\uff0c\u5982\u679c\u4e0a\u8fc7 CS230 \u7684\u8bdd\u53ef\u4ee5\u76f4\u63a5\u4e0a\u624b Project \u4f5c\u4e3a\u7ec3\u4e60\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://cs231n.stanford.edu/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1nJ411z7fe \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a http://cs231n.stanford.edu/schedule.html \uff0c3\u4e2a\u7f16\u7a0b\u4f5c\u4e1a","title":"Stanford CS231n: CNN for Visual Recognition"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS231/#cs231n-cnn-for-visual-recognition","text":"","title":"CS231n: CNN for Visual Recognition"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS231/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u673a\u5668\u5b66\u4e60\u57fa\u7840 \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 Stanford \u7684 CV \u5165\u95e8\u8bfe\uff0c\u7531\u8ba1\u7b97\u673a\u9886\u57df\u7684\u5de8\u4f6c\u674e\u98de\u98de\u9662\u58eb\u9886\u8854\u6559\u6388\uff08CV \u9886\u57df\u5212\u65f6\u4ee3\u7684\u8457\u540d\u6570\u636e\u96c6 ImageNet \u7684\u7814\u7a76\u56e2\u961f\uff09\uff0c\u4f46\u5176\u5185\u5bb9\u76f8\u5bf9\u57fa\u7840\u4e14\u53cb\u597d\uff0c\u5982\u679c\u4e0a\u8fc7 CS230 \u7684\u8bdd\u53ef\u4ee5\u76f4\u63a5\u4e0a\u624b Project \u4f5c\u4e3a\u7ec3\u4e60\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS231/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://cs231n.stanford.edu/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1nJ411z7fe \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a http://cs231n.stanford.edu/schedule.html 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\u90fd\u6709\u8fdc\u7a0b\u5728\u7ebf\u7248\uff0c\u975e\u5e38\u9002\u5408\u5927\u5bb6\u5728\u5bb6\u81ea\u5b66\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/EE16/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a EE16A , EE16B \u8bfe\u7a0b\u89c6\u9891\uff1aB \u7ad9\u641c\u7d22 \u8bfe\u7a0b\u6559\u6750\uff1a\u53c2\u89c1\u8bfe\u7a0b notes \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u53c2\u89c1\u8bfe\u7a0b\u4e3b\u9875","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/EE16/#_3","text":"@PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/EE16A - GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/Signals_and_Systems_AVO/","text":"MIT 6.007 Signals and Systems \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1aCalculus, Linear 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Oppenheim \u597d\u7684\uff0c\u4e0a\u8fd9\u95e8\u8bfe\u7684\u7406\u7531\u5df2\u7ecf\u8db3\u591f\u4e86\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://ocw.mit.edu/resources/res-6-007-signals-and-systems-spring-2011/index.htm \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1CZ4y1j7hs \u8bfe\u7a0b\u6559\u6750\uff1aSignals and Systems, 2nd Edition \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://ocw.mit.edu/resources/res-6-007-signals-and-systems-spring-2011/assignments","title":"MIT 6.007 Signals and Systems"},{"location":"%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/Signals_and_Systems_AVO/#mit-6007-signals-and-systems","text":"","title":"MIT 6.007 Signals and Systems"},{"location":"%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/Signals_and_Systems_AVO/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1aCalculus, Linear Algebra \u7f16\u7a0b\u8bed\u8a00\uff1aMatlab Preferred \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a50-70 \u5c0f\u65f6 \u770b\u5230\u8bfe\u7a0b\u8001\u5e08\u7684\u540d\u5b57\uff1aProf. Alan V. Oppenheim \u597d\u7684\uff0c\u4e0a\u8fd9\u95e8\u8bfe\u7684\u7406\u7531\u5df2\u7ecf\u8db3\u591f\u4e86\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/Signals_and_Systems_AVO/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a https://ocw.mit.edu/resources/res-6-007-signals-and-systems-spring-2011/index.htm \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1CZ4y1j7hs \u8bfe\u7a0b\u6559\u6750\uff1aSignals and Systems, 2nd Edition \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://ocw.mit.edu/resources/res-6-007-signals-and-systems-spring-2011/assignments","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/signal/","text":"UCB EE120: Signal and Systems \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS61A, CS70\uff0c\u5fae\u79ef\u5206\uff0c\u7ebf\u6027\u4ee3\u6570 \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 \u8fd9\u95e8\u8bfe\u6700\u7cbe\u534e\u7684\u90e8\u5206\u5c31\u662f 6 \u4e2a\u8d85\u6709\u8da3\u7684\u7f16\u7a0b\u4f5c\u4e1a\u4e86\uff0c\u4f1a\u8ba9\u4f60\u7528 Python \u901a\u8fc7\u5b66\u4e60\u5230\u7684\u4fe1\u53f7\u4e0e\u7cfb\u7edf\u7684\u7406\u8bba\u77e5\u8bc6\uff0c\u89e3\u51b3\u5404\u7c7b\u5b9e\u9645\u95ee\u9898\u3002\u4f8b\u5982 lab3 \u4f1a\u8ba9\u4f60\u5b9e\u73b0 FFT \u7b97\u6cd5\uff0c\u5e76\u548c Numpy \u7684\u5b98\u65b9\u5b9e\u73b0\u8fdb\u884c\u6027\u80fd\u5bf9\u6bd4\uff1blab4 \u4f1a\u901a\u8fc7\u5206\u6790\u624b\u6307\u5934\u7684\u5f71\u50cf\u6570\u636e\u63a8\u65ad\u5fc3\u7387\uff1blab5 \u5c31\u66f4\u725b\u4e86\uff0c\u4f1a\u8ba9\u4f60\u7ed9\u54c8\u52c3\u671b\u8fdc\u955c\u62cd\u5230\u7684\u7167\u7247\u8fdb\u884c\u964d\u566a\u5904\u7406\uff0c\u6062\u590d\u7eda\u70c2\u6e05\u6670\u7684\u661f\u7a7a\uff1blab6 \u4f1a\u8ba9\u4f60\u6784\u9020\u4e00\u4e2a\u53cd\u9988\u7cfb\u7edf\uff0c\u5e73\u8861\u5c0f\u8f66\u4e0a\u7684\u7ec6\u6746\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://inst.eecs.berkeley.edu/~ee120/fa19/ \u8bfe\u7a0b\u6559\u6750\uff1a\u53c2\u89c1\u8bfe\u7a0b notes \u8bfe\u7a0b\u4f5c\u4e1a\uff1a5 \u4e2a\u4e66\u9762\u4f5c\u4e1a + 6 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/UCB-EE120 - 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GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"%E7%A8%8B%E5%BA%8F%E8%AF%AD%E8%A8%80%E8%AE%BE%E8%AE%A1/CS242/","text":"","title":"CS242"},{"location":"%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/CS161/","text":"UCB CS161: Computer Security \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS61A, CS61B, CS61C \u7f16\u7a0b\u8bed\u8a00\uff1aC, Go \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 \u4f2f\u514b\u5229\u7684\u8ba1\u7b97\u673a\u7cfb\u7edf\u5b89\u5168\u8bfe\u7a0b\uff0c\u8bfe\u7a0b\u5185\u5bb9\u5206\u4e3a5\u4e2a\u90e8\u5206\uff1a Security principles: how to design a secure system Memory safety: buffer overflow attack Cryptography: symmetric encryption, asymmetric encryption, MAC, digital signature ......... Web: SQL-injection, XSS, XSRF ....... Networking: attacks for each layer \u8fd9\u95e8\u8bfe\u8ba9\u6211\u5370\u8c61\u6700\u4e3a\u6df1\u523b\u7684\u90e8\u5206\u662f Project2\uff0c\u8ba9\u4f60\u7528 Go \u8bed\u8a00\u8bbe\u8ba1\u548c\u5b9e\u73b0\u4e00\u4e2a\u5b89\u5168\u7684\u6587\u4ef6\u5206\u4eab\u7cfb\u7edf\u3002\u6211\u82b1\u4e86\u6574\u6574\u4e09\u5929\u624d\u5b8c\u6210\u4e86\u8fd9\u4e2a\u975e\u5e38\u8650\u7684 Project\uff0c\u603b\u4ee3\u7801\u91cf\u8d85\u8fc7 3k \u884c\u3002\u5728\u8fd9\u6837\u5bc6\u96c6\u578b\u7684\u5f00\u53d1\u8fc7\u7a0b\u4e2d\uff0c\u80fd\u6781\u5927\u5730\u953b\u70bc\u4f60\u8bbe\u8ba1\u548c\u5b9e\u73b0\u4e00\u4e2a\u5b89\u5168\u7cfb\u7edf\u7684\u80fd\u529b\u3002 2020 \u5e74\u590f\u5b63\u5b66\u671f\u7684\u7248\u672c\u5f00\u6e90\u4e86\u8bfe\u7a0b\u5f55\u5f71\uff0c\u5927\u5bb6\u53ef\u4ee5\u5728\u4e0b\u9762\u7684\u8bfe\u7a0b\u7f51\u7ad9\u94fe\u63a5\u91cc\u627e\u5230\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://su20.cs161.org/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a https://textbook.cs161.org/ \u8bfe\u7a0b\u4f5c\u4e1a\uff1a7 \u4e2a\u5728\u7ebf HW + 3 \u4e2a Lab + 3 \u4e2a Project \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/UCB-CS161 - GitHub \u4e2d\u3002","title":"UCB CS161: Computer Security"},{"location":"%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/CS161/#ucb-cs161-computer-security","text":"","title":"UCB CS161: Computer Security"},{"location":"%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/CS161/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS61A, CS61B, CS61C \u7f16\u7a0b\u8bed\u8a00\uff1aC, Go \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 \u4f2f\u514b\u5229\u7684\u8ba1\u7b97\u673a\u7cfb\u7edf\u5b89\u5168\u8bfe\u7a0b\uff0c\u8bfe\u7a0b\u5185\u5bb9\u5206\u4e3a5\u4e2a\u90e8\u5206\uff1a Security principles: how to design a secure system Memory safety: buffer overflow attack Cryptography: symmetric encryption, asymmetric encryption, MAC, digital signature ......... Web: SQL-injection, XSS, XSRF ....... Networking: attacks for each layer \u8fd9\u95e8\u8bfe\u8ba9\u6211\u5370\u8c61\u6700\u4e3a\u6df1\u523b\u7684\u90e8\u5206\u662f Project2\uff0c\u8ba9\u4f60\u7528 Go \u8bed\u8a00\u8bbe\u8ba1\u548c\u5b9e\u73b0\u4e00\u4e2a\u5b89\u5168\u7684\u6587\u4ef6\u5206\u4eab\u7cfb\u7edf\u3002\u6211\u82b1\u4e86\u6574\u6574\u4e09\u5929\u624d\u5b8c\u6210\u4e86\u8fd9\u4e2a\u975e\u5e38\u8650\u7684 Project\uff0c\u603b\u4ee3\u7801\u91cf\u8d85\u8fc7 3k \u884c\u3002\u5728\u8fd9\u6837\u5bc6\u96c6\u578b\u7684\u5f00\u53d1\u8fc7\u7a0b\u4e2d\uff0c\u80fd\u6781\u5927\u5730\u953b\u70bc\u4f60\u8bbe\u8ba1\u548c\u5b9e\u73b0\u4e00\u4e2a\u5b89\u5168\u7cfb\u7edf\u7684\u80fd\u529b\u3002 2020 \u5e74\u590f\u5b63\u5b66\u671f\u7684\u7248\u672c\u5f00\u6e90\u4e86\u8bfe\u7a0b\u5f55\u5f71\uff0c\u5927\u5bb6\u53ef\u4ee5\u5728\u4e0b\u9762\u7684\u8bfe\u7a0b\u7f51\u7ad9\u94fe\u63a5\u91cc\u627e\u5230\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/CS161/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a https://su20.cs161.org/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a https://textbook.cs161.org/ \u8bfe\u7a0b\u4f5c\u4e1a\uff1a7 \u4e2a\u5728\u7ebf HW + 3 \u4e2a Lab + 3 \u4e2a Project","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/CS161/#_3","text":"@PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/UCB-CS161 - GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/MIT6.858/","text":"MIT 6.858: Computer System Security \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784\uff0c\u5bf9\u8ba1\u7b97\u673a\u7cfb\u7edf\u6709\u521d\u6b65\u4e86\u89e3 \u7f16\u7a0b\u8bed\u8a00\uff1aC, Python \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 MIT \u7684\u8ba1\u7b97\u673a\u7cfb\u7edf\u5b89\u5168\u8bfe\u7a0b\uff0c\u5b9e\u9a8c\u73af\u5883\u662f\u4e00\u4e2a Web Application Zoobar\u3002\u5b66\u751f\u5b66\u4e60\u653b\u9632\u6280\u672f\u5e76\u5e94\u7528\u4e8e\u8be5 Web Application\u3002 Lab 1: you will explore the zoobar web application, and use buffer overflow attacks to break its security properties. Lab 2: you will improve the zoobar web application by using privilege separation, so that if one component is compromised, the adversary doesn't get control over the whole web application. Lab 3: you will build a program analysis tool based on symbolic execution to find bugs in Python code such as the zoobar web application. Lab 4: you will improve the zoobar application against browser attacks. \u8fd9\u4e2a\u8bfe\u6211\u4e3b\u8981\u662f\u505a\u4e86 Lab 3\u3002Lab 3 \u662f\u901a\u8fc7\u6df7\u5408\u7b26\u53f7\u6267\u884c\u6765\u904d\u5386\u7a0b\u5e8f\u7684\u6240\u6709\u5206\u652f\uff0c\u7406\u89e3\u4e86\u7b26\u53f7\u6267\u884c\u7684\u601d\u60f3\u540e Lab \u5e76\u4e0d\u96be\u505a\u3002\u8fd9\u4e2a Lab \u76f4\u89c2\u5c55\u793a\u7b26\u53f7\u6267\u884c\u8fd9\u79cd\u6280\u672f\u7684\u4f7f\u7528\u65b9\u6cd5\u3002 \u8fd9\u4e2a\u8bfe\u7684 Final Project \u662f\u5b9e\u73b0 SecFS \uff0c\u4e00\u4e2a\u8fdc\u7aef\u6587\u4ef6\u7cfb\u7edf\uff0c\u9762\u5bf9\u5b8c\u5168\u4e0d\u53ef\u4fe1\u7684\u670d\u52a1\u5668\uff0c\u63d0\u4f9b\u673a\u5bc6\u6027\u548c\u5b8c\u6574\u6027\u3002\u53c2\u8003\u8bba\u6587\u4e3a SUNDR \u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://css.csail.mit.edu/6.858/2022/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a4 \u4e2a Lab + Final Project / Lab5","title":"MIT 6.858: Computer System Security"},{"location":"%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/MIT6.858/#mit-6858-computer-system-security","text":"","title":"MIT 6.858: Computer System Security"},{"location":"%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/MIT6.858/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784\uff0c\u5bf9\u8ba1\u7b97\u673a\u7cfb\u7edf\u6709\u521d\u6b65\u4e86\u89e3 \u7f16\u7a0b\u8bed\u8a00\uff1aC, Python \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 MIT \u7684\u8ba1\u7b97\u673a\u7cfb\u7edf\u5b89\u5168\u8bfe\u7a0b\uff0c\u5b9e\u9a8c\u73af\u5883\u662f\u4e00\u4e2a Web Application Zoobar\u3002\u5b66\u751f\u5b66\u4e60\u653b\u9632\u6280\u672f\u5e76\u5e94\u7528\u4e8e\u8be5 Web Application\u3002 Lab 1: you will explore the zoobar web application, and use buffer overflow attacks to break its security properties. Lab 2: you will improve the zoobar web application by using privilege separation, so that if one component is compromised, the adversary doesn't get control over the whole web application. Lab 3: you will build a program analysis tool based on symbolic execution to find bugs in Python code such as the zoobar web application. Lab 4: you will improve the zoobar application against browser attacks. \u8fd9\u4e2a\u8bfe\u6211\u4e3b\u8981\u662f\u505a\u4e86 Lab 3\u3002Lab 3 \u662f\u901a\u8fc7\u6df7\u5408\u7b26\u53f7\u6267\u884c\u6765\u904d\u5386\u7a0b\u5e8f\u7684\u6240\u6709\u5206\u652f\uff0c\u7406\u89e3\u4e86\u7b26\u53f7\u6267\u884c\u7684\u601d\u60f3\u540e Lab \u5e76\u4e0d\u96be\u505a\u3002\u8fd9\u4e2a Lab \u76f4\u89c2\u5c55\u793a\u7b26\u53f7\u6267\u884c\u8fd9\u79cd\u6280\u672f\u7684\u4f7f\u7528\u65b9\u6cd5\u3002 \u8fd9\u4e2a\u8bfe\u7684 Final Project \u662f\u5b9e\u73b0 SecFS \uff0c\u4e00\u4e2a\u8fdc\u7aef\u6587\u4ef6\u7cfb\u7edf\uff0c\u9762\u5bf9\u5b8c\u5168\u4e0d\u53ef\u4fe1\u7684\u670d\u52a1\u5668\uff0c\u63d0\u4f9b\u673a\u5bc6\u6027\u548c\u5b8c\u6574\u6027\u3002\u53c2\u8003\u8bba\u6587\u4e3a SUNDR \u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/MIT6.858/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://css.csail.mit.edu/6.858/2022/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a4 \u4e2a Lab + Final Project / Lab5","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106B_CS106X/","text":"Stanford CS106B/X: Programming Abstractions in C++ \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u8ba1\u7b97\u673a\u57fa\u7840 (CS50/CS106A/CS61A or equivalent) \u7f16\u7a0b\u8bed\u8a00\uff1aC++ \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a50-70 \u5c0f\u65f6 Stanford \u7684\u8fdb\u9636\u7f16\u7a0b\u8bfe\uff0cCS106X \u5728\u96be\u5ea6\u548c\u6df1\u5ea6\u4e0a\u4f1a\u6bd4 CS106B \u6709\u6240\u63d0\u9ad8\uff0c\u4f46\u4e3b\u4f53\u5185\u5bb9\u7c7b\u4f3c\u3002\u4e3b\u8981\u901a\u8fc7 C++ \u8bed\u8a00\u8ba9\u5b66\u751f\u5728\u5b9e\u9645\u7684\u7f16\u7a0b\u4f5c\u4e1a\u91cc\u57f9\u517b\u901a\u8fc7\u7f16\u7a0b\u62bd\u8c61\u89e3\u51b3\u5b9e\u9645\u95ee\u9898\u7684\u80fd\u529b\uff0c\u540c\u65f6\u4e5f\u4f1a\u6d89\u53ca\u4e00\u4e9b\u7b80\u5355\u7684\u6570\u636e\u7ed3\u6784\u548c\u7b97\u6cd5\u7684\u77e5\u8bc6\uff0c\u4f46\u603b\u4f53\u6765\u8bf4\u6ca1\u6709\u4e00\u95e8\u4e13\u95e8\u7684\u6570\u636e\u7ed3\u6784\u8bfe\u90a3\u4e48\u7cfb\u7edf\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a CS106B , CS106X \u8bfe\u7a0b\u6559\u6750\uff1a https://web.stanford.edu/class/cs106x/res/reader/CS106BX-Reader.pdf \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1G7411k7jG","title":"Stanford CS106B/X"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106B_CS106X/#stanford-cs106bx-programming-abstractions-in-c","text":"","title":"Stanford CS106B/X: Programming Abstractions in C++"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106B_CS106X/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u8ba1\u7b97\u673a\u57fa\u7840 (CS50/CS106A/CS61A or equivalent) \u7f16\u7a0b\u8bed\u8a00\uff1aC++ \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a50-70 \u5c0f\u65f6 Stanford \u7684\u8fdb\u9636\u7f16\u7a0b\u8bfe\uff0cCS106X \u5728\u96be\u5ea6\u548c\u6df1\u5ea6\u4e0a\u4f1a\u6bd4 CS106B \u6709\u6240\u63d0\u9ad8\uff0c\u4f46\u4e3b\u4f53\u5185\u5bb9\u7c7b\u4f3c\u3002\u4e3b\u8981\u901a\u8fc7 C++ \u8bed\u8a00\u8ba9\u5b66\u751f\u5728\u5b9e\u9645\u7684\u7f16\u7a0b\u4f5c\u4e1a\u91cc\u57f9\u517b\u901a\u8fc7\u7f16\u7a0b\u62bd\u8c61\u89e3\u51b3\u5b9e\u9645\u95ee\u9898\u7684\u80fd\u529b\uff0c\u540c\u65f6\u4e5f\u4f1a\u6d89\u53ca\u4e00\u4e9b\u7b80\u5355\u7684\u6570\u636e\u7ed3\u6784\u548c\u7b97\u6cd5\u7684\u77e5\u8bc6\uff0c\u4f46\u603b\u4f53\u6765\u8bf4\u6ca1\u6709\u4e00\u95e8\u4e13\u95e8\u7684\u6570\u636e\u7ed3\u6784\u8bfe\u90a3\u4e48\u7cfb\u7edf\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106B_CS106X/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a CS106B , CS106X \u8bfe\u7a0b\u6559\u6750\uff1a https://web.stanford.edu/class/cs106x/res/reader/CS106BX-Reader.pdf \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1G7411k7jG","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106L/","text":"CS106L: Standard C++ Programming \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6700\u597d\u638c\u63e1\u81f3\u5c11\u4e00\u95e8\u7f16\u7a0b\u8bed\u8a00 \u7f16\u7a0b\u8bed\u8a00\uff1aC++ \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a20 \u5c0f\u65f6 \u6211\u4ece\u5927\u4e00\u5f00\u59cb\u4e00\u76f4\u90fd\u662f\u5199\u7684 C++ \u4ee3\u7801\uff0c\u76f4\u5230\u5b66\u5b8c\u8fd9\u95e8\u8bfe\u6211\u624d\u610f\u8bc6\u5230\uff0c\u6211\u5199\u7684 C++ \u4ee3\u7801\u5927\u6982\u53ea\u662f C \u8bed\u8a00 + cin / cout \u800c\u5df2\u3002 \u8fd9\u95e8\u8bfe\u4f1a\u6df1\u5165\u5230\u5f88\u591a\u6807\u51c6 C++ \u7684\u7279\u6027\u548c\u8bed\u6cd5\uff0c\u8ba9\u4f60\u7f16\u5199\u51fa\u9ad8\u8d28\u91cf\u7684 C++ \u4ee3\u7801\u3002\u4f8b\u5982 auto binding, uniform initialization, lambda function, move semantics\uff0cRAII \u7b49\u6280\u5de7\u90fd\u5728\u6211\u6b64\u540e\u7684\u4ee3\u7801\u751f\u6daf\u4e2d\u88ab\u53cd\u590d\u7528\u5230\uff0c\u975e\u5e38\u5b9e\u7528\u3002 \u503c\u5f97\u4e00\u63d0\u7684\u662f\uff0c\u8fd9\u95e8\u8bfe\u7684\u4f5c\u4e1a\u91cc\u4f60\u4f1a\u5b9e\u73b0\u4e00\u4e2a HashMap\uff08\u7c7b\u4f3c\u4e8e STL \u4e2d\u7684 unordered_map ), \u8fd9\u4e2a\u4f5c\u4e1a\u51e0\u4e4e\u628a\u6574\u4e2a\u8bfe\u7a0b\u4e32\u8054\u4e86\u8d77\u6765\uff0c\u975e\u5e38\u8003\u9a8c\u4ee3\u7801\u80fd\u529b\u3002\u7279\u522b\u662f iterator \u7684\u5b9e\u73b0\uff0c\u505a\u5b8c\u8fd9\u4e2a\u4f5c\u4e1a\u6211\u5f00\u59cb\u7406\u89e3\u4e3a\u4ec0\u4e48 Linus \u5bf9 C/C++ \u55e4\u4e4b\u4ee5\u9f3b\u4e86\uff0c\u56e0\u4e3a\u771f\u7684\u5f88\u96be\u5199\u5bf9\u3002 \u603b\u7684\u6765\u8bb2\u8fd9\u95e8\u8bfe\u5e76\u4e0d\u96be\uff0c\u4f46\u662f\u4fe1\u606f\u91cf\u5f88\u5927\uff0c\u9700\u8981\u4f60\u5728\u4e4b\u540e\u7684\u5f00\u53d1\u5b9e\u8df5\u4e2d\u53cd\u590d\u5de9\u56fa\u3002Stanford \u4e4b\u6240\u4ee5\u5355\u5f00\u4e00\u95e8 C++ \u7684\u7f16\u7a0b\u8bfe\uff0c\u662f\u56e0\u4e3a\u5b83\u540e\u7eed\u7684\u5f88\u591a CS \u8bfe\u7a0b Project \u90fd\u662f\u57fa\u4e8e C++\u7684\u3002\u4f8b\u5982 CS144 \u8ba1\u7b97\u673a\u7f51\u7edc\u548c CS143 \u7f16\u8bd1\u5668\u3002\u8fd9\u4e24\u95e8\u8bfe\u5728\u672c\u4e66\u4e2d\u5747\u6709\u6536\u5f55\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs106l/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.youtube.com/channel/UCSqr6y-eaQT_qZJVUm_4QxQ/playlists \u8bfe\u7a0b\u6559\u6750\uff1a http://web.stanford.edu/class/cs106l/full_course_reader.pdf Assignment1\u4e0b\u8f7d\u7f51\u5740\uff1a https://github.com/snme/cs106L-assignment1 Assignment2\u4e0b\u8f7d\u7f51\u5740\uff1a https://github.com/snme/cs106L-assignment2 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u5177\u4f53\u5185\u5bb9\u89c1\u8bfe\u7a0b\u7f51\u7ad9\uff0c\u6211\u505a\u7684\u65f6\u5019\u4e00\u5171\u662f\u4e24\u4e2a\uff1a \u5b9e\u73b0\u4e00\u4e2a WikiRacer \u7684\u5c0f\u6e38\u620f \u5b9e\u73b0\u4e00\u4e2a\u7c7b\u4f3c STL \u5e93\u7684 HashMap \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS106L - GitHub \u4e2d\u3002","title":"Stanford CS106L: Standard C++ Programming"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106L/#cs106l-standard-c-programming","text":"","title":"CS106L: Standard C++ Programming"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106L/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6700\u597d\u638c\u63e1\u81f3\u5c11\u4e00\u95e8\u7f16\u7a0b\u8bed\u8a00 \u7f16\u7a0b\u8bed\u8a00\uff1aC++ \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a20 \u5c0f\u65f6 \u6211\u4ece\u5927\u4e00\u5f00\u59cb\u4e00\u76f4\u90fd\u662f\u5199\u7684 C++ \u4ee3\u7801\uff0c\u76f4\u5230\u5b66\u5b8c\u8fd9\u95e8\u8bfe\u6211\u624d\u610f\u8bc6\u5230\uff0c\u6211\u5199\u7684 C++ \u4ee3\u7801\u5927\u6982\u53ea\u662f C \u8bed\u8a00 + cin / cout \u800c\u5df2\u3002 \u8fd9\u95e8\u8bfe\u4f1a\u6df1\u5165\u5230\u5f88\u591a\u6807\u51c6 C++ \u7684\u7279\u6027\u548c\u8bed\u6cd5\uff0c\u8ba9\u4f60\u7f16\u5199\u51fa\u9ad8\u8d28\u91cf\u7684 C++ \u4ee3\u7801\u3002\u4f8b\u5982 auto binding, uniform initialization, lambda function, move semantics\uff0cRAII \u7b49\u6280\u5de7\u90fd\u5728\u6211\u6b64\u540e\u7684\u4ee3\u7801\u751f\u6daf\u4e2d\u88ab\u53cd\u590d\u7528\u5230\uff0c\u975e\u5e38\u5b9e\u7528\u3002 \u503c\u5f97\u4e00\u63d0\u7684\u662f\uff0c\u8fd9\u95e8\u8bfe\u7684\u4f5c\u4e1a\u91cc\u4f60\u4f1a\u5b9e\u73b0\u4e00\u4e2a HashMap\uff08\u7c7b\u4f3c\u4e8e STL \u4e2d\u7684 unordered_map ), \u8fd9\u4e2a\u4f5c\u4e1a\u51e0\u4e4e\u628a\u6574\u4e2a\u8bfe\u7a0b\u4e32\u8054\u4e86\u8d77\u6765\uff0c\u975e\u5e38\u8003\u9a8c\u4ee3\u7801\u80fd\u529b\u3002\u7279\u522b\u662f iterator \u7684\u5b9e\u73b0\uff0c\u505a\u5b8c\u8fd9\u4e2a\u4f5c\u4e1a\u6211\u5f00\u59cb\u7406\u89e3\u4e3a\u4ec0\u4e48 Linus \u5bf9 C/C++ \u55e4\u4e4b\u4ee5\u9f3b\u4e86\uff0c\u56e0\u4e3a\u771f\u7684\u5f88\u96be\u5199\u5bf9\u3002 \u603b\u7684\u6765\u8bb2\u8fd9\u95e8\u8bfe\u5e76\u4e0d\u96be\uff0c\u4f46\u662f\u4fe1\u606f\u91cf\u5f88\u5927\uff0c\u9700\u8981\u4f60\u5728\u4e4b\u540e\u7684\u5f00\u53d1\u5b9e\u8df5\u4e2d\u53cd\u590d\u5de9\u56fa\u3002Stanford \u4e4b\u6240\u4ee5\u5355\u5f00\u4e00\u95e8 C++ \u7684\u7f16\u7a0b\u8bfe\uff0c\u662f\u56e0\u4e3a\u5b83\u540e\u7eed\u7684\u5f88\u591a CS \u8bfe\u7a0b Project \u90fd\u662f\u57fa\u4e8e C++\u7684\u3002\u4f8b\u5982 CS144 \u8ba1\u7b97\u673a\u7f51\u7edc\u548c CS143 \u7f16\u8bd1\u5668\u3002\u8fd9\u4e24\u95e8\u8bfe\u5728\u672c\u4e66\u4e2d\u5747\u6709\u6536\u5f55\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106L/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs106l/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.youtube.com/channel/UCSqr6y-eaQT_qZJVUm_4QxQ/playlists \u8bfe\u7a0b\u6559\u6750\uff1a http://web.stanford.edu/class/cs106l/full_course_reader.pdf Assignment1\u4e0b\u8f7d\u7f51\u5740\uff1a https://github.com/snme/cs106L-assignment1 Assignment2\u4e0b\u8f7d\u7f51\u5740\uff1a https://github.com/snme/cs106L-assignment2 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u5177\u4f53\u5185\u5bb9\u89c1\u8bfe\u7a0b\u7f51\u7ad9\uff0c\u6211\u505a\u7684\u65f6\u5019\u4e00\u5171\u662f\u4e24\u4e2a\uff1a \u5b9e\u73b0\u4e00\u4e2a WikiRacer \u7684\u5c0f\u6e38\u620f \u5b9e\u73b0\u4e00\u4e2a\u7c7b\u4f3c STL \u5e93\u7684 HashMap","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106L/#_3","text":"@PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS106L - GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS110L/","text":"CS110L: Safety in Systems Programming \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6700\u597d\u6709\u4e00\u5b9a\u7684\u7f16\u7a0b\u80cc\u666f\u5e76\u5bf9\u8ba1\u7b97\u673a\u7cfb\u7edf\u6709\u521d\u6b65\u7684\u8ba4\u8bc6\u3002 \u7f16\u7a0b\u8bed\u8a00\uff1aRust \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a30 \u5c0f\u65f6 \u5728\u8fd9\u95e8\u8bfe\u4e2d\u4f60\u5c06\u4f1a\u5b66\u4e60 Rust \u8fd9\u95e8\u795e\u5947\u7684\u8bed\u8a00\u3002 \u5982\u679c\u4f60\u5b66\u8fc7 C \u5e76\u63a5\u89e6\u8fc7\u4e00\u4e9b\u7cfb\u7edf\u7f16\u7a0b\u7684\u8bdd\uff0c\u5e94\u8be5\u5bf9 C \u7684\u5185\u5b58\u6cc4\u6f0f\u4ee5\u53ca\u6307\u9488\u7684\u5371\u9669\u6709\u6240\u8033\u95fb\uff0c\u4f46 C \u7684\u5e95\u5c42\u7279\u6027\u4ee5\u53ca\u9ad8\u6548\u4ecd\u7136\u8ba9\u5b83\u5728\u7cfb\u7edf\u7ea7\u7f16\u7a0b\u4e2d\u65e0\u6cd5\u88ab\u4f8b\u5982 Java \u7b49\u81ea\u5e26\u5783\u573e\u6536\u96c6\u673a\u5236\u7684\u9ad8\u7ea7\u8bed\u8a00\u6240\u66ff\u4ee3\u3002\u800c Rust \u7684\u76ee\u6807\u5219\u662f\u5e0c\u671b\u5728 C \u7684\u9ad8\u6548\u57fa\u7840\u4e0a\uff0c\u5f25\u8865\u5176\u5b89\u5168\u4e0d\u8db3\u7684\u7f3a\u70b9\u3002\u56e0\u6b64 Rust \u5728\u8bbe\u8ba1\u4e4b\u521d\uff0c\u5c31\u6709\u5e26\u6709\u5f88\u591a\u7cfb\u7edf\u7f16\u7a0b\u7684\u89c2\u70b9\u3002\u5b66\u4e60 Rust\uff0c\u4e5f\u80fd\u8ba9\u4f60\u4e4b\u540e\u80fd\u7528 C \u8bed\u8a00\u7f16\u5199\u51fa\u66f4\u5b89\u5168\u66f4\u4f18\u96c5\u7684\u7cfb\u7edf\u7ea7\u4ee3\u7801\uff08\u4f8b\u5982\u64cd\u4f5c\u7cfb\u7edf\u7b49\uff09\u3002 \u8fd9\u95e8\u8bfe\u7684\u540e\u534a\u90e8\u5206\u5173\u6ce8\u5728\u5e76\u53d1\uff08concurrency\uff09\u8fd9\u4e00\u4e3b\u9898\u4e0a\uff0c\u4f60\u5c06\u4f1a\u7cfb\u7edf\u5730\u638c\u63e1\u591a\u8fdb\u7a0b\u3001\u591a\u7ebf\u7a0b\u3001\u57fa\u4e8e\u4e8b\u4ef6\u9a71\u52a8\u7684\u5e76\u53d1\u7b49\u82e5\u5e72\u5e76\u53d1\u6280\u672f\uff0c\u5e76\u5728\u7b2c\u4e8c\u4e2a Project \u4e2d\u6bd4\u8f83\u5b83\u4eec\u5404\u81ea\u7684\u4f18\u52a3\u3002Rust \u4e2d \u201cfutures\u201d \u7684\u6982\u5ff5\u975e\u5e38\u6709\u8da3\u548c\u4f18\u96c5\uff0c\u8fd9\u4e9b\u57fa\u7840\u77e5\u8bc6\u5bf9\u4f60\u540e\u7eed\u5bf9\u8ba1\u7b97\u673a\u7cfb\u7edf\u76f8\u5173\u8bfe\u7a0b\u7684\u5b66\u4e60\u5f88\u6709\u5e2e\u52a9\u3002\u53e6\u5916\uff0c\u6e05\u534e\u5927\u5b66\u7684\u64cd\u7edf\u5b9e\u9a8c rCore \u5c31\u662f\u57fa\u4e8e Rust \u7f16\u5199\u7684\uff0c\u5177\u4f53\u53c2\u89c1 \u6587\u6863 \u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://reberhardt.com/cs110l/spring-2020/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://youtu.be/j7AQrtLevUE \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u5171 6 \u4e2a Lab \u548c 2 \u4e2a Project\uff0c\u4f5c\u4e1a\u6587\u6863\u548c\u4ee3\u7801\u6846\u67b6\u8be6\u89c1\u8bfe\u7a0b\u7f51\u7ad9\u3002\u5176\u4e2d\u4e24\u4e2a Project \u975e\u5e38\u6709\u8da3\uff0c\u5206\u522b\u662f\uff1a \u7528 Rust \u5b9e\u73b0\u4e00\u4e2a\u7c7b\u4f3c\u4e8e GDB \u7684 debugger \u7528 Rust 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http://composingprograms.com/ \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u8bfe\u7a0b\u7f51\u7ad9\u4f1a\u6709\u6bcf\u4e2a\u4f5c\u4e1a\u5bf9\u5e94\u7684\u6587\u6863\u94fe\u63a5\u4ee5\u53ca\u4ee3\u7801\u6846\u67b6\u7684\u4e0b\u8f7d\u94fe\u63a5\u3002 \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS61A - GitHub \u4e2d\u3002","title":"UCB CS61A: Structure and Interpretation of Computer Programs"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS61A/#cs61a-structure-and-interpretation-of-computer-programs","text":"","title":"CS61A: Structure and Interpretation of Computer Programs"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS61A/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1a\u65e0 \u7f16\u7a0b\u8bed\u8a00\uff1aPython, Scheme, SQL \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a50 \u5c0f\u65f6 \u4f2f\u514b\u5229 CS61 \u7cfb\u5217\u7684\u7b2c\u4e00\u95e8\u8bfe\u7a0b\uff0c\u4e5f\u662f\u6211\u7684 Python \u5165\u95e8\u8bfe\u3002 CS61 \u7cfb\u5217\u662f\u4f2f\u514b\u5229 CS \u4e13\u4e1a\u7684\u5165\u95e8\u8bfe\uff0c\u5176\u4e2d\uff1a CS61A: \u5f3a\u8c03\u62bd\u8c61\uff0c\u8ba9\u5b66\u751f\u638c\u63e1\u7528\u7a0b\u5e8f\u6765\u89e3\u51b3\u5b9e\u9645\u95ee\u9898\uff0c\u800c\u4e0d\u5173\u6ce8\u5e95\u5c42\u7684\u786c\u4ef6\u7ec6\u8282\u3002 CS61B: \u6ce8\u91cd\u7b97\u6cd5\u4e0e\u6570\u636e\u7ed3\u6784\u4ee5\u53ca\u5927\u89c4\u6a21\u7a0b\u5e8f\u7684\u6784\u5efa\uff0c\u5b66\u751f\u4f1a\u7528 Java \u8bed\u8a00\u7ed3\u5408\u7b97\u6cd5\u4e0e\u6570\u636e\u7ed3\u6784\u7684\u77e5\u8bc6\u6765\u6784\u5efa\u5343\u884c\u4ee3\u7801\u7ea7\u522b\u7684\u5927\u578b\u9879\u76ee\uff08\u4e00\u4e2a\u7b80\u6613\u7684\u8c37\u6b4c\u5730\u56fe\uff0c\u4e00\u4e2a\u4e8c\u7ef4\u7248\u7684 Minecraft\uff09\u3002 CS61C: 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\u7684\u89e3\u91ca\u5668\u3002\u6b64\u5916\uff0c\u62bd\u8c61\u5c06\u662f\u8fd9\u95e8\u8bfe\u7684\u4e00\u5927\u4e3b\u9898\uff0c\u4f60\u5c06\u5b66\u4e60\u5230\u51fd\u6570\u5f0f\u7f16\u7a0b\u3001\u6570\u636e\u62bd\u8c61\u3001\u9762\u5411\u5bf9\u8c61\u7b49\u7b49\u77e5\u8bc6\u6765\u8ba9\u4f60\u7684\u4ee3\u7801\u66f4\u6613\u8bfb\uff0c\u66f4\u6a21\u5757\u5316\u3002\u5f53\u7136\uff0c\u5b66\u4e60\u7f16\u7a0b\u8bed\u8a00\u4e5f\u662f\u8fd9\u95e8\u8bfe\u7684\u4e00\u5927\u5185\u5bb9\uff0c\u4f60\u5c06\u4f1a\u638c\u63e1 Python\u3001Scheme \u548c SQL \u8fd9\u4e09\u79cd\u7f16\u7a0b\u8bed\u8a00\uff0c\u5728\u5b83\u4eec\u7684\u5b66\u4e60\u548c\u6bd4\u8f83\u4e2d\uff0c\u76f8\u4fe1\u4f60\u4f1a\u62e5\u6709\u5feb\u901f\u638c\u63e1\u4e00\u95e8\u65b0\u7684\u7f16\u7a0b\u8bed\u8a00\u7684\u80fd\u529b\u3002 \u6ce8\u610f\uff1a\u5982\u679c\u6b64\u524d\u5b8c\u5168\u6ca1\u6709\u7f16\u7a0b\u57fa\u7840\uff0c\u76f4\u63a5\u4e0a\u624b CS61A \u9700\u8981\u4e00\u5b9a\u7684\u5b66\u4e60\u80fd\u529b\u548c\u81ea\u5f8b\u8981\u6c42\u3002\u4e3a\u907f\u514d\u8bfe\u7a0b\u96be\u5ea6\u8fc7\u9ad8\u800c\u5bfc\u81f4\u7684\u4fe1\u5fc3\u632b\u6298\uff0c\u53ef\u4ee5\u9009\u62e9\u4e00\u4e2a\u66f4\u4e3a\u53cb\u597d\u7684\u5165\u95e8\u7f16\u7a0b\u8bfe\u7a0b\u3002\u4f8b\u5982\u4f2f\u514b\u5229\u7684 CS10 \u6216\u8005\u54c8\u4f5b\u5927\u5b66\u7684 CS50 \u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS61A/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a https://inst.eecs.berkeley.edu/~cs61a/su20/ \u8bfe\u7a0b\u89c6\u9891: \u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9\u94fe\u63a5 \u8bfe\u7a0b\u6559\u6750\uff1a http://composingprograms.com/ 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\u975e\u5e38\u597d\u7684\u8bfe\u7a0b\uff0c\u81ea\u6211\u611f\u89c9\u6536\u76ca\u975e\u5e38\u5927\uff1a \u4fa7\u91cd\u57fa\u7840\u548c\u57fa\u672c\u6982\u5ff5\uff1a\u5982 frame\u3001stack memory\u3001heap memory \u7b49\u8bb2\u5f97\u5f88\u900f\u3002 \u9488\u5bf9C\u6700\u96be\u638c\u63e1\u7684\u6307\u9488\uff0c\u6709\u597d\u7684\u7ec3\u4e60\u548c\u7f16\u7a0b\u6765\u52a0\u6df1\u548c\u5f3a\u5316\u7406\u89e3\u3002 \u975e\u5e38\u597d\u7684 GDB\uff0cValgrind \u4e0a\u624b\u8bad\u7ec3\uff0c\u4f5c\u4e1a\u4e5f\u4f1a\u6d89\u53ca\u4e00\u4e9b\u57fa\u672c\u7684 Git \u7ec3\u4e60\u3002 \u8001\u5e08\u5efa\u8bae\u4f5c\u4e1a\u7528 Emacs\uff0c\u6240\u4ee5\u5bf9 Emacs \u5c0f\u767d\u6765\u8bf4\uff0c\u662f\u4e2a\u4e0d\u9519\u7684\u5165\u95e8\u3002\u5982\u679c\u4f60\u4f1a\u7528 Vim \uff0c\u6211\u5efa\u8bae\u4f60\u7528 Evil \u63d2\u4ef6\u3002\u8fd9\u6837\u4f60\u4e0d\u4f1a\u4e22\u6389 Vim \u7684\u7f16\u8f91\u529f\u80fd\uff0c\u540c\u65f6\u53ef\u4ee5\u4f53\u4f1a Emacs \u7684\u5f3a\u5927\u3002\u5de5\u5177\u7bb1\u91cc\u540c\u65f6\u6709 Emacs \u548c Vim \u65f6\uff0c\u6548\u7387\u4f1a\u6709\u4e0d\u5c11\u63d0\u9ad8\u3002Emacs \u7684 org-mode\uff0c\u548c GDB \u7684\u987a\u6ed1\u6574\u5408\uff0c\u7b49\u7b49\u7b49\u7b49\uff0c\u90fd\u4f1a\u8ba9\u4f60\u5982\u864e\u6dfb\u7ffc\u3002 \u867d\u7136\u53ef\u80fd\u9700\u8981\u4ed8\u8d39\uff0c\u4f46\u6211\u89c9\u5f97\u503c\u3002 \u867d\u8bf4\u8bfe\u540d\u662f\u5165\u95e8\uff0c\u4f46\u517c\u5177\u5e7f\u5ea6\u548c\u6df1\u5ea6\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://www.coursera.org/specializations/c-programming \u8bfe\u7a0b\u89c6\u9891\uff1a\u540c\u4e0a \u8bfe\u7a0b\u6559\u6750\uff1a\u540c\u4e0a \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u540c\u4e0a \u8d44\u6e90\u6c47\u603b @haidongji \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7684\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 Duke Coursera Intro C 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\u7b49\u8bb2\u5f97\u5f88\u900f\u3002 \u9488\u5bf9C\u6700\u96be\u638c\u63e1\u7684\u6307\u9488\uff0c\u6709\u597d\u7684\u7ec3\u4e60\u548c\u7f16\u7a0b\u6765\u52a0\u6df1\u548c\u5f3a\u5316\u7406\u89e3\u3002 \u975e\u5e38\u597d\u7684 GDB\uff0cValgrind \u4e0a\u624b\u8bad\u7ec3\uff0c\u4f5c\u4e1a\u4e5f\u4f1a\u6d89\u53ca\u4e00\u4e9b\u57fa\u672c\u7684 Git \u7ec3\u4e60\u3002 \u8001\u5e08\u5efa\u8bae\u4f5c\u4e1a\u7528 Emacs\uff0c\u6240\u4ee5\u5bf9 Emacs \u5c0f\u767d\u6765\u8bf4\uff0c\u662f\u4e2a\u4e0d\u9519\u7684\u5165\u95e8\u3002\u5982\u679c\u4f60\u4f1a\u7528 Vim \uff0c\u6211\u5efa\u8bae\u4f60\u7528 Evil \u63d2\u4ef6\u3002\u8fd9\u6837\u4f60\u4e0d\u4f1a\u4e22\u6389 Vim \u7684\u7f16\u8f91\u529f\u80fd\uff0c\u540c\u65f6\u53ef\u4ee5\u4f53\u4f1a Emacs \u7684\u5f3a\u5927\u3002\u5de5\u5177\u7bb1\u91cc\u540c\u65f6\u6709 Emacs \u548c Vim \u65f6\uff0c\u6548\u7387\u4f1a\u6709\u4e0d\u5c11\u63d0\u9ad8\u3002Emacs \u7684 org-mode\uff0c\u548c GDB 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\u7f16\u7a0b\u3001\u547d\u4ee4\u884c\u914d\u7f6e\u3001Git\u3001Vim\u3001 tmux \u3001 ssh \u7b49\u7b49\u3002\u5982\u679c\u4f60\u662f\u4e00\u4e2a\u8ba1\u7b97\u673a\u5c0f\u767d\uff0c\u90a3\u4e48\u6211\u975e\u5e38\u5efa\u8bae\u4f60\u5b66\u4e60\u4e00\u4e0b\u8fd9\u95e8\u8bfe\uff0c\u56e0\u4e3a\u5b83\u57fa\u672c\u6d89\u53ca\u4e86\u672c\u4e66\u5fc5\u5b66\u5de5\u5177\u4e2d\u7684\u7edd\u5927\u90e8\u5206\u5185\u5bb9\u3002 \u9664\u4e86 MIT \u5b98\u65b9\u7684\u5b66\u4e60\u8d44\u6599\u5916\uff0c\u5317\u4eac\u5927\u5b66\u56fe\u7075\u73ed\u5f00\u8bbe\u7684\u524d\u6cbf\u8ba1\u7b97\u5b9e\u8df5\u4e2d\u4e5f\u5f00\u8bbe\u4e86\u76f8\u5173\u8bfe\u7a0b\uff0c\u8d44\u6599\u4f4d\u4e8e \u8fd9\u4e2a\u7f51\u7ad9 \u4e0b\uff0c\u4f9b\u5927\u5bb6\u53c2\u8003\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://missing.csail.mit.edu/2020/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.youtube.com/playlist?list=PLyzOVJj3bHQuloKGG59rS43e29ro7I57J \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u4e00\u4e9b\u968f\u5802\u5c0f\u7ec3\u4e60\uff0c\u5177\u4f53\u89c1\u8bfe\u7a0b\u7f51\u7ad9\u3002","title":"MIT-Missing-Semester"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/MIT-Missing-Semester/#mit-missing-semester","text":"","title":"MIT-Missing-Semester"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/MIT-Missing-Semester/#_1","text":"\u5148\u4fee\u8981\u6c42\uff1a\u65e0 \u7f16\u7a0b\u8bed\u8a00\uff1ashell \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a10 \u5c0f\u65f6 \u6b63\u5982\u8bfe\u7a0b\u540d\u5b57\u6240\u8a00\uff1a\u201c\u8ba1\u7b97\u673a\u6559\u5b66\u4e2d\u6d88\u5931\u7684\u4e00\u4e2a\u5b66\u671f\u201d\uff0c\u8fd9\u95e8\u8bfe\u5c06\u4f1a\u6559\u4f1a\u4f60\u8bb8\u591a\u5927\u5b66\u7684\u8bfe\u5802\u4e0a\u4e0d\u4f1a\u6d89\u53ca\u4f46\u5374\u5bf9\u6bcf\u4e2a CSer \u65e0\u6bd4\u91cd\u8981\u7684\u5de5\u5177\u6216\u8005\u77e5\u8bc6\u70b9\u3002\u4f8b\u5982 Shell 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https://www.youtube.com/playlist?list=PLyzOVJj3bHQuloKGG59rS43e29ro7I57J \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u4e00\u4e9b\u968f\u5802\u5c0f\u7ec3\u4e60\uff0c\u5177\u4f53\u89c1\u8bfe\u7a0b\u7f51\u7ad9\u3002","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E7%BC%96%E8%AF%91%E5%8E%9F%E7%90%86/6035/","text":"","title":"6035"},{"location":"%E7%BC%96%E8%AF%91%E5%8E%9F%E7%90%86/CS143/","text":"Stanford CS143: Compilers \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784 \u7f16\u7a0b\u8bed\u8a00\uff1aJava \u6216 C++ \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 \u65af\u5766\u798f\u7684\u7f16\u8bd1\u539f\u7406\u8bfe\u7a0b\uff0c\u8bbe\u8ba1\u8005\u5f00\u53d1\u4e86\u4e00\u4e2a Class-Object-Oriented-Language\uff0c\u7b80\u79f0 COOL \u8bed\u8a00\u3002\u8fd9\u95e8\u8bfe\u7684\u6838\u5fc3\u5c31\u662f\u901a\u8fc7\u7406\u8bba\u77e5\u8bc6\u7684\u5b66\u4e60\uff0c\u4e3a COOL \u8bed\u8a00\u5b9e\u73b0\u4e00\u4e2a\u7f16\u8bd1\u5668\uff0c\u5c06 COOL \u9ad8\u7ea7\u8bed\u8a00\u7f16\u8bd1\u4e3a MIPS \u6c47\u7f16\u5e76\u5728 Spim \u8fd9\u4e2a MIPS \u6a21\u62df\u5668\u4e0a\u6210\u529f\u6267\u884c\u3002 \u7406\u8bba\u90e8\u5206\u57fa\u672c\u6309\u7167\u9f99\u4e66\u7684\u987a\u5e8f\u8986\u76d6\u4e86\u8bcd\u6cd5\u5206\u6790\u3001\u8bed\u6cd5\u5206\u6790\u3001\u8bed\u4e49\u5206\u6790\u3001\u8fd0\u884c\u65f6\u73af\u5883\u3001\u5bc4\u5b58\u5668\u5206\u914d\u3001\u4ee3\u7801\u4f18\u5316\u4e0e\u751f\u6210\u7b49\u5185\u5bb9\uff0c\u5b9e\u8df5\u90e8\u5206\u5219\u76f8\u5e94\u5730\u5206\u4e3a\u8bcd\u6cd5\u5206\u6790\u3001\u8bed\u6cd5\u5206\u6790\u3001\u8bed\u4e49\u5206\u6790\u3001\u4ee3\u7801\u751f\u6210\u56db\u4e2a\u9636\u6bb5\uff0c\u96be\u5ea6\u5faa\u5e8f\u6e10\u8fdb\uff0c\u5e76\u5728\u4f18\u5316\u90e8\u5206\u7ed9\u5b66\u751f\u7559\u4e0b\u4e86\u5f88\u5927\u7684\u8bbe\u8ba1\u7a7a\u95f4\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs143/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV17K4y147Bz \u8bfe\u7a0b\u6559\u6750\uff1a\u9f99\u4e66 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a5 \u4e2a\u4e66\u9762\u4f5c\u4e1a + 5 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\u9664\u4e86\u6700\u65b0\u6700\u5168\u7684\u5185\u5bb9\u4e4b\u5916\uff0c\u672c\u8bfe\u7a0b\u4e0e\u5176\u5b83\u4efb\u4f55\u5b9e\u65f6\u6e32\u67d3\u7684\u6559\u7a0b\u8fd8\u6709\u4e00\u4e2a\u91cd\u8981\u7684\u533a\u522b\uff0c\u90a3\u5c31\u662f\u672c\u8bfe\u7a0b\u4e0d\u4f1a\u8bb2\u6388\u4efb\u4f55\u4e0e\u6e38\u620f\u5f15\u64ce\u7684\u4f7f\u7528\u76f8\u5173\u7684\u5185\u5bb9\uff0c\u5e76\u4e14\u4e0d\u4f1a\u7279\u522b\u5f3a\u8c03\u5177\u4f53\u7684\u7740\u8272\u5668\u5b9e\u73b0\u6280\u672f\uff0c\u800c\u4e3b\u8981\u8bb2\u6388\u5b9e\u65f6\u6e32\u67d3\u80cc\u540e\u7684\u79d1\u5b66\u4e0e\u77e5\u8bc6\u3002\u672c\u8bfe\u7a0b\u7684\u76ee\u6807\u662f\u5728\u4f60\u5b66\u4e60\u5b8c\u8fd9\u95e8\u8bfe\u7684\u65f6\u5019\uff0c\u4f60\u5c06\u6709\u6df1\u539a\u7684\u529f\u5e95\u53bb\u5f00\u53d1\u4e00\u4e2a\u5c5e\u4e8e\u4f60\u81ea\u5df1\u7684\u5b9e\u65f6\u6e32\u67d3\u5f15\u64ce\u3002 \u4f5c\u4e3a GAMES101 \u7684\u8fdb\u9636\u8bfe\u7a0b\uff0c\u96be\u5ea6\u6709\u4e00\u5b9a\u7684\u63d0\u5347\uff0c\u4f46\u4e0d\u4f1a\u5f88\u5927\uff0c\u76f8\u4fe1\u5b8c\u6210\u4e86 GAMES101 \u7684\u540c\u5b66\u90fd\u6709\u80fd\u529b\u5b8c\u6210\u8fd9\u95e8\u8bfe\u7a0b\u3002\u6bcf\u4e2a project \u4ee3\u7801\u91cf\u90fd\u4e0d\u4f1a\u5f88\u591a\uff0c\u4f46\u662f\u90fd\u9700\u8981\u4e00\u5b9a\u7684\u601d\u8003\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a GAMES202 \u8bfe\u7a0b\u89c6\u9891\uff1a bilibili \u8bfe\u7a0b\u6559\u6750\uff1aReal-Time Rendering, 4th edition. \u8bfe\u7a0b\u4f5c\u4e1a\uff1a 5\u4e2aproject","title":"GAMES202"},{"location":"%E8%AE%A1%E7%AE%97%E6%9C%BA%E5%9B%BE%E5%BD%A2%E5%AD%A6/GAMES202/#games202","text":"","title":"GAMES202"},{"location":"%E8%AE%A1%E7%AE%97%E6%9C%BA%E5%9B%BE%E5%BD%A2%E5%AD%A6/GAMES202/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aUCSB \u5148\u4fee\u8981\u6c42\uff1a\u7ebf\u6027\u4ee3\u6570\uff0c\u9ad8\u7b49\u6570\u5b66\uff0cC++\uff0cGAMES101 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Correctness (correct behavior right now) and defensiveness (correct behavior in the future) are required in any software we build. Easy to understand. The code has to communicate to future programmers who need to understand it and make changes in it (fixing bugs or adding new features). That future programmer might be you, months or years from now. You\u2019ll be surprised how much you forget if you don\u2019t write it down, and how much it helps your own future self to have a good design. Ready for change. Software always changes. Some designs make it easy to make changes; others require throwing away and rewriting a lot of code. \u4e3a\u6b64\uff0c\u8fd9\u95e8\u8bfe\u7684\u8bbe\u8ba1\u8005\u4eec\u7cbe\u5fc3\u7f16\u5199\u4e86\u4e00\u672c\u4e66\u6765\u9610\u91ca\u8bf8\u591a\u8f6f\u4ef6\u6784\u5efa\u7684\u6838\u5fc3\u539f\u5219\u4e0e\u524d\u4eba\u603b\u7ed3\u4e0b\u6765\u7684\u5b9d\u8d35\u7ecf\u9a8c\uff0c\u5185\u5bb9\u7ec6\u8282\u5230\u5982\u4f55\u7f16\u5199\u6ce8\u91ca\u548c\u51fd\u6570 Specification\uff0c\u5982\u4f55\u8bbe\u8ba1\u62bd\u8c61\u6570\u636e\u7ed3\u6784\u4ee5\u53ca\u8bf8\u591a\u5e76\u884c\u7f16\u7a0b\u7684\u5185\u5bb9\uff0c\u5e76\u4e14\u4f1a\u8ba9\u4f60\u5728\u7cbe\u5fc3\u8bbe\u8ba1\u7684 Java \u7f16\u7a0b\u9879\u76ee\u91cc\u4f53\u9a8c\u548c\u7ec3\u4e60\u8fd9\u4e9b\u7f16\u7a0b\u6a21\u5f0f\u3002 2016\u5e74\u6625\u5b63\u5b66\u671f\u8fd9\u95e8\u8bfe\u5f00\u6e90\u4e86\u5176\u6240\u6709\u7f16\u7a0b\u4f5c\u4e1a\u7684\u4ee3\u7801\u6846\u67b6\uff0c\u800c\u6700\u65b0\u7684\u8bfe\u7a0b\u6559\u6750\u53ef\u4ee5\u5728\u5176\u6700\u65b0\u7684\u6559\u5b66\u7f51\u7ad9\u4e0a\u627e\u5230\uff0c\u5177\u4f53\u94fe\u63a5\u53c2\u89c1\u4e0b\u65b9\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a 2021spring , 2016spring \u8bfe\u7a0b\u89c6\u9891\uff1a\u65e0 \u8bfe\u7a0b\u6559\u6750\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9\u7684\u8bfe\u7a0b notes \u8bfe\u7a0b\u4f5c\u4e1a\uff1a4 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a + 1 \u4e2a Project \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/MIT6.031-software-construction - GitHub \u4e2d\u3002 @pengzhangzhi \u5b8c\u6210\u4e86\u8fd9\u95e8\u8bfe\u7684\u4f5c\u4e1a\u5e76\u8bb0\u5f55\u4e86\u7b14\u8bb0, \u4ee3\u7801\u5f00\u6e90\u5728 pengzhangzhi/self-taught-CS/Software Construction - Github \u3002","title":"MIT 6.031: Software Construction"},{"location":"%E8%BD%AF%E4%BB%B6%E5%B7%A5%E7%A8%8B/6031/#mit-6031-software-construction","text":"","title":"MIT 6.031: Software Construction"},{"location":"%E8%BD%AF%E4%BB%B6%E5%B7%A5%E7%A8%8B/6031/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u638c\u63e1\u81f3\u5c11\u4e00\u95e8\u7f16\u7a0b\u8bed\u8a00 \u7f16\u7a0b\u8bed\u8a00\uff1aJava \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 \u8fd9\u95e8\u8bfe\u7684\u76ee\u6807\u5c31\u662f\u8ba9\u5b66\u751f\u5b66\u4f1a\u5982\u4f55\u5199\u51fa\u9ad8\u8d28\u91cf\u7684\u4ee3\u7801\uff0c\u6240\u8c13\u9ad8\u8d28\u91cf\uff0c\u5219\u662f\u6ee1\u8db3\u4e0b\u9762\u4e09\u4e2a\u76ee\u6807\uff08\u8bfe\u7a0b\u8bbe\u8ba1\u8005\u539f\u8bdd\u590d\u5236\uff0c\u4ee5\u9632\u81ea\u5df1\u7ffb\u8bd1\u66f2\u89e3\u672c\u610f\uff09\uff1a Safe from bugs. Correctness (correct behavior right now) and defensiveness (correct behavior in the future) are required in any software we build. Easy to understand. The code has to communicate to future programmers who need to understand it and make changes in it (fixing bugs or adding new features). That future programmer might be you, months or years from now. You\u2019ll be surprised how much you forget if you don\u2019t write it down, and how much it helps your own future self to have a good design. Ready for change. Software always changes. Some designs make it easy to make changes; others require throwing away and rewriting a lot of code. \u4e3a\u6b64\uff0c\u8fd9\u95e8\u8bfe\u7684\u8bbe\u8ba1\u8005\u4eec\u7cbe\u5fc3\u7f16\u5199\u4e86\u4e00\u672c\u4e66\u6765\u9610\u91ca\u8bf8\u591a\u8f6f\u4ef6\u6784\u5efa\u7684\u6838\u5fc3\u539f\u5219\u4e0e\u524d\u4eba\u603b\u7ed3\u4e0b\u6765\u7684\u5b9d\u8d35\u7ecf\u9a8c\uff0c\u5185\u5bb9\u7ec6\u8282\u5230\u5982\u4f55\u7f16\u5199\u6ce8\u91ca\u548c\u51fd\u6570 Specification\uff0c\u5982\u4f55\u8bbe\u8ba1\u62bd\u8c61\u6570\u636e\u7ed3\u6784\u4ee5\u53ca\u8bf8\u591a\u5e76\u884c\u7f16\u7a0b\u7684\u5185\u5bb9\uff0c\u5e76\u4e14\u4f1a\u8ba9\u4f60\u5728\u7cbe\u5fc3\u8bbe\u8ba1\u7684 Java \u7f16\u7a0b\u9879\u76ee\u91cc\u4f53\u9a8c\u548c\u7ec3\u4e60\u8fd9\u4e9b\u7f16\u7a0b\u6a21\u5f0f\u3002 2016\u5e74\u6625\u5b63\u5b66\u671f\u8fd9\u95e8\u8bfe\u5f00\u6e90\u4e86\u5176\u6240\u6709\u7f16\u7a0b\u4f5c\u4e1a\u7684\u4ee3\u7801\u6846\u67b6\uff0c\u800c\u6700\u65b0\u7684\u8bfe\u7a0b\u6559\u6750\u53ef\u4ee5\u5728\u5176\u6700\u65b0\u7684\u6559\u5b66\u7f51\u7ad9\u4e0a\u627e\u5230\uff0c\u5177\u4f53\u94fe\u63a5\u53c2\u89c1\u4e0b\u65b9\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E8%BD%AF%E4%BB%B6%E5%B7%A5%E7%A8%8B/6031/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a 2021spring , 2016spring \u8bfe\u7a0b\u89c6\u9891\uff1a\u65e0 \u8bfe\u7a0b\u6559\u6750\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9\u7684\u8bfe\u7a0b notes \u8bfe\u7a0b\u4f5c\u4e1a\uff1a4 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a + 1 \u4e2a Project","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E8%BD%AF%E4%BB%B6%E5%B7%A5%E7%A8%8B/6031/#_3","text":"@PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/MIT6.031-software-construction - GitHub \u4e2d\u3002 @pengzhangzhi \u5b8c\u6210\u4e86\u8fd9\u95e8\u8bfe\u7684\u4f5c\u4e1a\u5e76\u8bb0\u5f55\u4e86\u7b14\u8bb0, \u4ee3\u7801\u5f00\u6e90\u5728 pengzhangzhi/self-taught-CS/Software Construction - Github \u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"%E8%BD%AF%E4%BB%B6%E5%B7%A5%E7%A8%8B/CS169/","text":"UCB CS169: software engineering \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1a\u65e0 \u7f16\u7a0b\u8bed\u8a00\uff1aRuby/JavaScript \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 \u4f2f\u514b\u5229\u7684\u8f6f\u4ef6\u5de5\u7a0b\u8bfe\u7a0b\uff0c\u4e0d\u540c\u4e8e\u5f88\u591a\u4f20\u7edf\u7684\u8f6f\u4ef6\u5de5\u7a0b\u8bfe\u5f3a\u8c03\u5404\u79cd\u7c7b\u56fe\u3001\u6587\u6863\u8bbe\u8ba1 (plan and document \u6a21\u5f0f)\uff0c\u8fd9\u95e8\u8bfe\u4e13\u6ce8\u4e8e\u6700\u8fd1\u9010\u6e10\u6d41\u884c\u8d77\u6765\u7684\u654f\u6377\u5f00\u53d1 (Agile Development)\u6a21\u5f0f\uff0c\u5229\u7528\u4e91\u5e73\u53f0\u63d0\u4f9b\u8f6f\u4ef6\u5373\u670d\u52a1 (software as a service)\u3002\u4e3a\u6b64\uff0c\u8bfe\u7a0b\u8bbe\u8ba1\u8005\u7f16\u5199\u4e86 Software as a service \u8fd9\u672c\u6559\u6750\uff0c\u901a\u8fc7 Ruby/Rails \u6846\u67b6\u6765\u9610\u91ca SaaS \u8fd9\u4e2a\u6982\u5ff5\uff0c\u5e76\u4e14\u6709\u4e30\u5bcc\u7684\u914d\u5957\u7f16\u7a0b\u7ec3\u4e60\u3002 \u8fd9\u95e8\u8bfe\u5728 Edx \u8fd9\u4e2a\u7531 MIT \u548c Harvard \u5927\u5b66\u53d1\u8d77\u7684\u5728\u7ebf\u6559\u80b2\u5e73\u53f0\u5168\u8d44\u6599\u5f00\u6e90\uff0c\u5927\u5bb6\u53ef\u4ee5\u5728 Edx \u81ea\u884c\u641c\u7d22 Agile SaaS Development \u8fd9\u95e8\u8bfe\u7a0b\u8fdb\u884c\u5b66\u4e60\u3002\u8bfe\u7a0b\u5185\u5bb9\u57fa\u672c\u6309\u7167\u6559\u6750\u7684\u987a\u5e8f\u5e26\u4f60\u4e00\u6b65\u6b65\u4ee5\u654f\u6377\u5f00\u53d1\u7684\u65b9\u5f0f\u642d\u5efa\u4e00\u4e2a\u8f6f\u4ef6\u5e76\u514d\u8d39\u90e8\u7f72\u5728\u4e91\u5e73\u53f0\u4e0a\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://www.saasbook.info/courses \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1 Edx \u8bfe\u7a0b\u4e3b\u9875\u3002 \u8bfe\u7a0b\u6559\u6750\uff1a Software as a service \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u53c2\u89c1 Edx \u8bfe\u7a0b\u4e3b\u9875\u3002 \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS169-Software-Engineering - GitHub \u4e2d\u3002","title":"UCB CS169: software engineering"},{"location":"%E8%BD%AF%E4%BB%B6%E5%B7%A5%E7%A8%8B/CS169/#ucb-cs169-software-engineering","text":"","title":"UCB CS169: software engineering"},{"location":"%E8%BD%AF%E4%BB%B6%E5%B7%A5%E7%A8%8B/CS169/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1a\u65e0 \u7f16\u7a0b\u8bed\u8a00\uff1aRuby/JavaScript \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 \u4f2f\u514b\u5229\u7684\u8f6f\u4ef6\u5de5\u7a0b\u8bfe\u7a0b\uff0c\u4e0d\u540c\u4e8e\u5f88\u591a\u4f20\u7edf\u7684\u8f6f\u4ef6\u5de5\u7a0b\u8bfe\u5f3a\u8c03\u5404\u79cd\u7c7b\u56fe\u3001\u6587\u6863\u8bbe\u8ba1 (plan and document \u6a21\u5f0f)\uff0c\u8fd9\u95e8\u8bfe\u4e13\u6ce8\u4e8e\u6700\u8fd1\u9010\u6e10\u6d41\u884c\u8d77\u6765\u7684\u654f\u6377\u5f00\u53d1 (Agile Development)\u6a21\u5f0f\uff0c\u5229\u7528\u4e91\u5e73\u53f0\u63d0\u4f9b\u8f6f\u4ef6\u5373\u670d\u52a1 (software as a service)\u3002\u4e3a\u6b64\uff0c\u8bfe\u7a0b\u8bbe\u8ba1\u8005\u7f16\u5199\u4e86 Software as a service \u8fd9\u672c\u6559\u6750\uff0c\u901a\u8fc7 Ruby/Rails \u6846\u67b6\u6765\u9610\u91ca SaaS \u8fd9\u4e2a\u6982\u5ff5\uff0c\u5e76\u4e14\u6709\u4e30\u5bcc\u7684\u914d\u5957\u7f16\u7a0b\u7ec3\u4e60\u3002 \u8fd9\u95e8\u8bfe\u5728 Edx \u8fd9\u4e2a\u7531 MIT \u548c Harvard \u5927\u5b66\u53d1\u8d77\u7684\u5728\u7ebf\u6559\u80b2\u5e73\u53f0\u5168\u8d44\u6599\u5f00\u6e90\uff0c\u5927\u5bb6\u53ef\u4ee5\u5728 Edx \u81ea\u884c\u641c\u7d22 Agile SaaS Development \u8fd9\u95e8\u8bfe\u7a0b\u8fdb\u884c\u5b66\u4e60\u3002\u8bfe\u7a0b\u5185\u5bb9\u57fa\u672c\u6309\u7167\u6559\u6750\u7684\u987a\u5e8f\u5e26\u4f60\u4e00\u6b65\u6b65\u4ee5\u654f\u6377\u5f00\u53d1\u7684\u65b9\u5f0f\u642d\u5efa\u4e00\u4e2a\u8f6f\u4ef6\u5e76\u514d\u8d39\u90e8\u7f72\u5728\u4e91\u5e73\u53f0\u4e0a\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E8%BD%AF%E4%BB%B6%E5%B7%A5%E7%A8%8B/CS169/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://www.saasbook.info/courses \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1 Edx \u8bfe\u7a0b\u4e3b\u9875\u3002 \u8bfe\u7a0b\u6559\u6750\uff1a Software as a service \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u53c2\u89c1 Edx \u8bfe\u7a0b\u4e3b\u9875\u3002","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E8%BD%AF%E4%BB%B6%E5%B7%A5%E7%A8%8B/CS169/#_3","text":"@PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS169-Software-Engineering - GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/","text":"Foreword The English version is still under development, please check this issue if you want to contribute. This is a self-learning guide to computer science, and a memento of my three years of self-learning at university. It is also a gift to the young students at Peking University. It would be a great encouragement and comfort to me if this book could be of even the slightest help to you in your college life. The book is currently organized to include the following sections (if you have other good suggestions, or would like to join the ranks of contributors, please feel free to email zhongyinmin@pku.edu.cn or ask questions in the issue). Productivity Toolkit: IDE, VPN, StackOverflow, Git, Github, Vim, Latex, GNU Make and so on. Environment configuration: PC/Server development environment setup, DevOps tutorials and so on. Book recommendations: Those who have read the CSAPP must have realized the importance of good books. I will list links to books and resources in different areas of Computer Science that I find rewarding to read. List of high quality CS courses : I will summarize all the high quality foreign CS courses I have taken into different categories and give relevant self-learning advice. Most of them will have a separate repository containing relevant resources as well as my homework/project implementations. The place where dreams start \u2014\u2014 CS61A In my freshman year, I was a novice who knew nothing about computers. I installed a giant IDE Visual Studio and fight with OJ every day. With my high school maths background, I did pretty well in maths courses, but I felt struggled to learn courses in my major. When it came to programming, all I could do was open up that clunky IDE, create a new project that I didn't know exactly what it was for, and then cin , cout , for loops, and then CE, RE, WA loops. I was in a state where I was desperately trying to learn well but I didn't know how to learn. I listened carefully in class but I couldn't solve the homework problems. I spent almost all my spare time doing the homework after class, but the results were disappointing. I still retain the source code of the project for Introduction to Computing course \u2014\u2014 a single 1200-line C++ file with no header files, no class abstraction, no unit tests, no makefile, no version control. The only good thing is that it can run, the disadvantage is the complement of \"can run\". For a while I wondered if I wasn't cut out for computer science, as all my childhood imaginings of geekiness had been completely ruined by my first semester's experience. It all turned around during the winter break of my freshman year, when I had a hankering to learn Python. I overheard someone recommend CS61A, a freshman introductory course at UC Berkeley on Python. I'll never forget that day, when I opened the CS61A course website. It was like Columbus discovering a new continent, and I opened the door to a new world. I finished the course in 3 weeks and for the first time I felt that CS could be so fulfilling and interesting, and I was shocked that there existed such a great course in the world. To avoid any suspicion of pandering to foreign courses, I will tell you about my experience of studying CS61A from the perspective of a pure student. Course website developed by course staffs : The course website integrates all the course resources into one, with a well organised course schedule, links to all slides, recorded videos and homework, detailed and clear syllabus, list of exams and solutions from previous years. Aesthetics aside, this website is so convenient for students. Textbook written by course instructor : The course instructor has adapted the classic MIT textbook Structure and Interpretation of Computer Programs (SICP) into Python (the original textbook was based on Scheme). This is a great way to ensure that the classroom content is consistent with the textbook, while adding more details. The entire book is open source and can be read directly online. Various, comprehensive and interesting homework : There are 14 labs to reinforce the knowledge gained in class, 10 homework assignments to practice, and 4 projects each with thousands of lines of code, all with well-organized skeleton code and babysitting instructions. Unlike the old-school OJ and Word document assignments, each lab/homework/project has a detailed handout document, fully automated grading scripts, and CS61A staffs have even developed an automated assignment submission and grading system . Of course, one might say \"How much can you learn from a project where most of code are written by your teaching assistants?\" . For someone who is new to CS and even stumbling over installing Python, this well-developed skeleton code allows students to focus on reinforcing the core knowledge they've learned in class, but also gives them a sense of achievement that they already can make a little game despite of learning Python only for a month. It also gives them the opportunity to read and learn from other people's high quality code so that they can reuse it later. I think in the freshman year, this kind of skeleton code is absolutely beneficial. The only bad thing perhaps is for the instructors and teaching assistants, as developing such assignments can conceivably require a considerable time commitment. Weekly discussion sessions : The teaching assistants will explain the difficult knowledge in class and add some supplementary materials which may not be covered in class. Also, there will be exercises from exams of previous years. All the exercises are written in LaTeX with solutions. In CS61A, You don't need any prerequesites about CS at all. You just need to pay attention, spend time and work hard. The feeling that you do not know what to do, that you are not getting anything in return for all the time you put in, is gone. It suited me so well that I fell in love with self-learning. Imagine that if someone could chew up the hard knowledge and present it to you in a vivid and straightforward way, with so many fancy and varied projects to reinforce your theoretical knowledge, you'd think they were really trying their best to make you fully grasp the course, and it was even an insult to the course builders not to learn it well. If you think I'm exaggerating, start with CS61A , because it's where my dreams began. Why write this book? In the 2020 Fall semester, I worked as a teaching assistant for the class Introduction to Computer Systems at Peking University. At that time, I had been studying totally on my own for over a year. I enjoyed this style of learning immensely. To share this joy, I have made a CS Self-learning Materials List for students in my seminar. It was purely on a whim at the time, as I wouldn't dare to encourage my students to skip classes and study on their own. But after another year of maintenance, the list has become quite comprehensive, covering most of the courses in Computer Science, Artificial Intelligence and Soft Engineering, and I have built separate repositories for each course, summarising the self-learning materials that I used. In my last college year, when I opened up my curriculum book, I realized that it was already a subset of my self-learning list. By then, it was only two and a half years after I had started my self-learning journey. Then, a bold idea came to my mind: perhaps I could create a self-learning book, write down the difficulty I encountered and the interest I found during these years of self-learning, hoping to make it easy for students who may also enjoy self-learning to start their wonderful self-learning journey. If you can build up the whole CS foundation in less than three years, have relatively solid mathematical skills and coding ability, experience dozens of projects with thousands of lines of code, master at least C/C++/Java/JS/Python/Go/Rust and other mainstream programming languages, have a good understanding of algorithms, circuits, architectures, networks, operating systems, compilers, artificial intelligence, machine learning, computer vision, natural language processing, reinforcement learning, cryptography, information theory, game theory, numerical analysis, statistics, distributed systems, parallel computing, database systems, computer graphics, web development, cloud computing, supercomputing etc. I think you will be confident enough to choose the area you are interested in, and you will be quite competitive in both industry and academia. I firmly believe that if you have read to this line, you do not lack the ability and committment to learn CS well, you just need a good teacher to teach you a good course. And I will try my best to pick such courses for you, based on my three years of experience. Pros For me, the biggest advantage of self-learning is that I can adjust the pace of learning entirely according to my own progress. For difficult parts, I can watch the videos over and over again, Google it online and ask questions on StackOverflow until I have it all figured out. For those that I mastered relatively quickly, I could skip them at twice or even three times the speed. Another great thing about self-learning is that you can learn from different perspectives. I have taken core courses such as architectures, networking, operating systems, and compilers from different universities. Different instructors may have different views on the same knowledge, which will broaden your horizon. A third advantage of self-learning is that you do not need to go to the class, listening to the boring lectures. Cons Of course, as a big fan of self-learning, I have to admit that it has its disadvantages. The first is the difficulty of communication. I'm actually a very keen questioner, and I like to follow up all the points I don't understand. But when you're facing a screen and you hear a teacher talking about something you don't understand, you can't go to the other end of the network and ask him or her for clarification. I try to mitigate this by thinking independently and making good use of Google, but it would be great to have a few friends to study together. You can refer to README for more information on participating a community group. The second thing is that these courses are basically in English. From the videos to the slides to the assignments, all in English. You may struggle at first, but I think it's a challenge that if you overcome, it will be extremely rewarding. Because at the moment, as reluctant as I am, I have to admit that in computer science, a lot of high quality documentation, forums and websites are all in English. The third, and I think the most difficult one, is self-discipline. Because have no DDL can sometimes be a really scary thing, especially when you get deeper, many foreign courses are quite difficult. You have to be self-driven enough to force yourself to settle down, read dozens of pages of Project Handout, understand thousands of lines of skeleton code and endure hours of debugging time. With no credits, no grades, no teachers, no classmates, just one belief - that you are getting better. Who is this book for? As I said in the beginning, anyone who is interested in learning computer science on their own can refer to this book. If you already have some basic skills and are just interested in a particular area, you can selectively pick and choose what you are interested in to study. Of course, if you are a novice who knows nothing about computers like I did back then, and just begin your college journey, I hope this book will be your cheat sheet to get the knowledge and skills you need in the least amount of time. In a way, this book is more like a course search engine ordered according to my experience, helping you to learn high quality CS courses from the world's top universities without leaving home. Of course, as an undergraduate student who has not yet graduated, I feel that I am not in a position nor have the right to preach one way of learning. I just hope that this material will help those who are also self-motivated and persistent to gain a richer, more varied and satisfying college life. Special thanks I would like to express my sincere gratitude to all the professors who have made their courses public for free. These courses are the culmination of decades of their teaching careers, and they have chosen to selflessly make such a high quality CS education available to all. Without them, my university life would not have been as fulfilling and enjoyable. Many of the professors would even reply with hundreds of words in length after I had sent them a thank you email, which really touched me beyond words. They also inspired me all the time that if decide to do something, do it with all heart and soul. Want to join as a contributor? There is a limit to how much one person can do, and this book was written by me under a heavy research schedule, so there are inevitably imperfections. In addition, as I work in the area of systems, many of the courses focus on systems, and there is relatively little content related to advanced mathematics, computing theory, and advanced algorithms. If any of you would like to share your self-learning experience and resources in other areas, you can directly initiate a Pull Request in the project, or feel free to contact me by email ( zhongyinmin@pku.edu.cn ).","title":"Foreword"},{"location":"en/#foreword","text":"The English version is still under development, please check this issue if you want to contribute. This is a self-learning guide to computer science, and a memento of my three years of self-learning at university. It is also a gift to the young students at Peking University. It would be a great encouragement and comfort to me if this book could be of even the slightest help to you in your college life. The book is currently organized to include the following sections (if you have other good suggestions, or would like to join the ranks of contributors, please feel free to email zhongyinmin@pku.edu.cn or ask questions in the issue). Productivity Toolkit: IDE, VPN, StackOverflow, Git, Github, Vim, Latex, GNU Make and so on. Environment configuration: PC/Server development environment setup, DevOps tutorials and so on. Book recommendations: Those who have read the CSAPP must have realized the importance of good books. I will list links to books and resources in different areas of Computer Science that I find rewarding to read. List of high quality CS courses : I will summarize all the high quality foreign CS courses I have taken into different categories and give relevant self-learning advice. Most of them will have a separate repository containing relevant resources as well as my homework/project implementations.","title":"Foreword"},{"location":"en/#the-place-where-dreams-start-cs61a","text":"In my freshman year, I was a novice who knew nothing about computers. I installed a giant IDE Visual Studio and fight with OJ every day. With my high school maths background, I did pretty well in maths courses, but I felt struggled to learn courses in my major. When it came to programming, all I could do was open up that clunky IDE, create a new project that I didn't know exactly what it was for, and then cin , cout , for loops, and then CE, RE, WA loops. I was in a state where I was desperately trying to learn well but I didn't know how to learn. I listened carefully in class but I couldn't solve the homework problems. I spent almost all my spare time doing the homework after class, but the results were disappointing. I still retain the source code of the project for Introduction to Computing course \u2014\u2014 a single 1200-line C++ file with no header files, no class abstraction, no unit tests, no makefile, no version control. The only good thing is that it can run, the disadvantage is the complement of \"can run\". For a while I wondered if I wasn't cut out for computer science, as all my childhood imaginings of geekiness had been completely ruined by my first semester's experience. It all turned around during the winter break of my freshman year, when I had a hankering to learn Python. I overheard someone recommend CS61A, a freshman introductory course at UC Berkeley on Python. I'll never forget that day, when I opened the CS61A course website. It was like Columbus discovering a new continent, and I opened the door to a new world. I finished the course in 3 weeks and for the first time I felt that CS could be so fulfilling and interesting, and I was shocked that there existed such a great course in the world. To avoid any suspicion of pandering to foreign courses, I will tell you about my experience of studying CS61A from the perspective of a pure student. Course website developed by course staffs : The course website integrates all the course resources into one, with a well organised course schedule, links to all slides, recorded videos and homework, detailed and clear syllabus, list of exams and solutions from previous years. Aesthetics aside, this website is so convenient for students. Textbook written by course instructor : The course instructor has adapted the classic MIT textbook Structure and Interpretation of Computer Programs (SICP) into Python (the original textbook was based on Scheme). This is a great way to ensure that the classroom content is consistent with the textbook, while adding more details. The entire book is open source and can be read directly online. Various, comprehensive and interesting homework : There are 14 labs to reinforce the knowledge gained in class, 10 homework assignments to practice, and 4 projects each with thousands of lines of code, all with well-organized skeleton code and babysitting instructions. Unlike the old-school OJ and Word document assignments, each lab/homework/project has a detailed handout document, fully automated grading scripts, and CS61A staffs have even developed an automated assignment submission and grading system . Of course, one might say \"How much can you learn from a project where most of code are written by your teaching assistants?\" . For someone who is new to CS and even stumbling over installing Python, this well-developed skeleton code allows students to focus on reinforcing the core knowledge they've learned in class, but also gives them a sense of achievement that they already can make a little game despite of learning Python only for a month. It also gives them the opportunity to read and learn from other people's high quality code so that they can reuse it later. I think in the freshman year, this kind of skeleton code is absolutely beneficial. The only bad thing perhaps is for the instructors and teaching assistants, as developing such assignments can conceivably require a considerable time commitment. Weekly discussion sessions : The teaching assistants will explain the difficult knowledge in class and add some supplementary materials which may not be covered in class. Also, there will be exercises from exams of previous years. All the exercises are written in LaTeX with solutions. In CS61A, You don't need any prerequesites about CS at all. You just need to pay attention, spend time and work hard. The feeling that you do not know what to do, that you are not getting anything in return for all the time you put in, is gone. It suited me so well that I fell in love with self-learning. Imagine that if someone could chew up the hard knowledge and present it to you in a vivid and straightforward way, with so many fancy and varied projects to reinforce your theoretical knowledge, you'd think they were really trying their best to make you fully grasp the course, and it was even an insult to the course builders not to learn it well. If you think I'm exaggerating, start with CS61A , because it's where my dreams began.","title":"The place where dreams start \u2014\u2014 CS61A"},{"location":"en/#why-write-this-book","text":"In the 2020 Fall semester, I worked as a teaching assistant for the class Introduction to Computer Systems at Peking University. At that time, I had been studying totally on my own for over a year. I enjoyed this style of learning immensely. To share this joy, I have made a CS Self-learning Materials List for students in my seminar. It was purely on a whim at the time, as I wouldn't dare to encourage my students to skip classes and study on their own. But after another year of maintenance, the list has become quite comprehensive, covering most of the courses in Computer Science, Artificial Intelligence and Soft Engineering, and I have built separate repositories for each course, summarising the self-learning materials that I used. In my last college year, when I opened up my curriculum book, I realized that it was already a subset of my self-learning list. By then, it was only two and a half years after I had started my self-learning journey. Then, a bold idea came to my mind: perhaps I could create a self-learning book, write down the difficulty I encountered and the interest I found during these years of self-learning, hoping to make it easy for students who may also enjoy self-learning to start their wonderful self-learning journey. If you can build up the whole CS foundation in less than three years, have relatively solid mathematical skills and coding ability, experience dozens of projects with thousands of lines of code, master at least C/C++/Java/JS/Python/Go/Rust and other mainstream programming languages, have a good understanding of algorithms, circuits, architectures, networks, operating systems, compilers, artificial intelligence, machine learning, computer vision, natural language processing, reinforcement learning, cryptography, information theory, game theory, numerical analysis, statistics, distributed systems, parallel computing, database systems, computer graphics, web development, cloud computing, supercomputing etc. I think you will be confident enough to choose the area you are interested in, and you will be quite competitive in both industry and academia. I firmly believe that if you have read to this line, you do not lack the ability and committment to learn CS well, you just need a good teacher to teach you a good course. And I will try my best to pick such courses for you, based on my three years of experience.","title":"Why write this book?"},{"location":"en/#pros","text":"For me, the biggest advantage of self-learning is that I can adjust the pace of learning entirely according to my own progress. For difficult parts, I can watch the videos over and over again, Google it online and ask questions on StackOverflow until I have it all figured out. For those that I mastered relatively quickly, I could skip them at twice or even three times the speed. Another great thing about self-learning is that you can learn from different perspectives. I have taken core courses such as architectures, networking, operating systems, and compilers from different universities. Different instructors may have different views on the same knowledge, which will broaden your horizon. A third advantage of self-learning is that you do not need to go to the class, listening to the boring lectures.","title":"Pros"},{"location":"en/#cons","text":"Of course, as a big fan of self-learning, I have to admit that it has its disadvantages. The first is the difficulty of communication. I'm actually a very keen questioner, and I like to follow up all the points I don't understand. But when you're facing a screen and you hear a teacher talking about something you don't understand, you can't go to the other end of the network and ask him or her for clarification. I try to mitigate this by thinking independently and making good use of Google, but it would be great to have a few friends to study together. You can refer to README for more information on participating a community group. The second thing is that these courses are basically in English. From the videos to the slides to the assignments, all in English. You may struggle at first, but I think it's a challenge that if you overcome, it will be extremely rewarding. Because at the moment, as reluctant as I am, I have to admit that in computer science, a lot of high quality documentation, forums and websites are all in English. The third, and I think the most difficult one, is self-discipline. Because have no DDL can sometimes be a really scary thing, especially when you get deeper, many foreign courses are quite difficult. You have to be self-driven enough to force yourself to settle down, read dozens of pages of Project Handout, understand thousands of lines of skeleton code and endure hours of debugging time. With no credits, no grades, no teachers, no classmates, just one belief - that you are getting better.","title":"Cons"},{"location":"en/#who-is-this-book-for","text":"As I said in the beginning, anyone who is interested in learning computer science on their own can refer to this book. If you already have some basic skills and are just interested in a particular area, you can selectively pick and choose what you are interested in to study. Of course, if you are a novice who knows nothing about computers like I did back then, and just begin your college journey, I hope this book will be your cheat sheet to get the knowledge and skills you need in the least amount of time. In a way, this book is more like a course search engine ordered according to my experience, helping you to learn high quality CS courses from the world's top universities without leaving home. Of course, as an undergraduate student who has not yet graduated, I feel that I am not in a position nor have the right to preach one way of learning. I just hope that this material will help those who are also self-motivated and persistent to gain a richer, more varied and satisfying college life.","title":"Who is this book for?"},{"location":"en/#special-thanks","text":"I would like to express my sincere gratitude to all the professors who have made their courses public for free. These courses are the culmination of decades of their teaching careers, and they have chosen to selflessly make such a high quality CS education available to all. Without them, my university life would not have been as fulfilling and enjoyable. Many of the professors would even reply with hundreds of words in length after I had sent them a thank you email, which really touched me beyond words. They also inspired me all the time that if decide to do something, do it with all heart and soul.","title":"Special thanks"},{"location":"en/#want-to-join-as-a-contributor","text":"There is a limit to how much one person can do, and this book was written by me under a heavy research schedule, so there are inevitably imperfections. In addition, as I work in the area of systems, many of the courses focus on systems, and there is relatively little content related to advanced mathematics, computing theory, and advanced algorithms. If any of you would like to share your self-learning experience and resources in other areas, you can directly initiate a Pull Request in the project, or feel free to contact me by email ( zhongyinmin@pku.edu.cn ).","title":"Want to join as a contributor?"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/","text":"\u4e00\u4e2a\u4ec5\u4f9b\u53c2\u8003\u7684 CS \u5b66\u4e60\u89c4\u5212 \u8ba1\u7b97\u673a\u9886\u57df\u65b9\u5411\u5e9e\u6742\uff0c\u77e5\u8bc6\u6d69\u5982\u70df\u6d77\uff0c\u6bcf\u4e2a\u7ec6\u5206\u9886\u57df\u5982\u679c\u6df1\u7a76\u4e0b\u53bb\u90fd\u53ef\u4ee5\u8bf4\u5b66\u65e0\u6b62\u5883\u3002\u56e0\u6b64\uff0c\u4e00\u4e2a\u6e05\u6670\u660e\u786e\u7684\u5b66\u4e60\u89c4\u5212\u662f\u975e\u5e38\u91cd\u8981\u7684\u3002\u8fd9\u4e00\u8282\u7684\u5185\u5bb9\u662f\u5bf9\u540e\u7eed\u6574\u672c\u4e66\u7684\u5185\u5bb9\u7684\u4e00\u4e2a\u6982\u89c8\uff0c\u4f60\u53ef\u4ee5\u5c06\u5176\u770b\u4f5c\u662f\u8fd9\u672c\u4e66\u7684\u76ee\u5f55\uff0c\u6309\u9700\u9009\u62e9\u81ea\u5df1\u611f\u5174\u8da3\u7684\u5185\u5bb9\u8fdb\u884c\u5b66\u4e60\u3002 \u4e0d\u8fc7\uff0c\u5728\u5f00\u59cb\u5b66\u4e60\u4e4b\u524d\uff0c\u5148\u5411\u5c0f\u767d\u4eec\u5f3a\u70c8\u63a8\u8350\u4e00\u4e2a\u79d1\u666e\u5411\u7cfb\u5217\u89c6\u9891 Crash Course: Computer Science \uff0c\u5728\u77ed\u77ed 8 \u4e2a\u5c0f\u65f6\u91cc\u975e\u5e38\u751f\u52a8\u4e14\u5168\u9762\u5730\u79d1\u666e\u4e86\u5173\u4e8e\u8ba1\u7b97\u673a\u79d1\u5b66\u7684\u65b9\u65b9\u9762\u9762\uff1a\u8ba1\u7b97\u673a\u7684\u5386\u53f2\u3001\u8ba1\u7b97\u673a\u662f\u5982\u4f55\u8fd0\u4f5c\u7684\u3001\u7ec4\u6210\u8ba1\u7b97\u673a\u7684\u5404\u4e2a\u91cd\u8981\u6a21\u5757\u3001\u8ba1\u7b97\u673a\u79d1\u5b66\u4e2d\u7684\u91cd\u8981\u601d\u60f3\u7b49\u7b49\u7b49\u7b49\u3002\u6b63\u5982\u5b83\u7684\u53e3\u53f7\u6240\u8bf4\u7684 Computers are not magic! \uff0c\u5e0c\u671b\u770b\u5b8c\u8fd9\u4e2a\u89c6\u9891\u4e4b\u540e\uff0c\u5927\u5bb6\u80fd\u5bf9\u8ba1\u7b97\u673a\u79d1\u5b66\u6709\u4e2a\u5168\u8c8c\u6027\u5730\u611f\u77e5\uff0c\u4ece\u800c\u6000\u7740\u5174\u8da3\u53bb\u9762\u5bf9\u4e0b\u9762\u6d69\u5982\u70df\u6d77\u7684\u66f4\u4e3a\u7ec6\u81f4\u4e14\u6df1\u5165\u7684\u5b66\u4e60\u5185\u5bb9\u3002 \u5fc5\u5b66\u5de5\u5177 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\u6570\u5b66\u57fa\u7840 \u5fae\u79ef\u5206\u4e0e\u7ebf\u6027\u4ee3\u6570 \u4f5c\u4e3a\u5927\u4e00\u65b0\u751f\uff0c\u5b66\u597d\u5fae\u79ef\u5206\u7ebf\u4ee3\u662f\u548c\u5199\u4ee3\u7801\u81f3\u5c11\u540c\u7b49\u91cd\u8981\u7684\u4e8b\u60c5\uff0c\u76f8\u4fe1\u5df2\u7ecf\u6709\u65e0\u6570\u7684\u524d\u4eba\u7ecf\u9a8c\u63d0\u5230\u8fc7\u8fd9\u4e00\u70b9\uff0c\u4f46\u6211\u8fd8\u662f\u8981\u4e0d\u538c\u5176\u70e6\u5730\u518d\u5f3a\u8c03\u4e00\u904d\uff1a\u5b66\u597d\u5fae\u79ef\u5206\u7ebf\u4ee3\u771f\u7684\u5f88\u91cd\u8981\uff01\u4f60\u4e5f\u8bb8\u4f1a\u5410\u69fd\u8fd9\u4e9b\u4e1c\u897f\u5c82\u4e0d\u662f\u8003\u5b8c\u5c31\u5fd8\uff0c\u90a3\u6211\u89c9\u5f97\u4f60\u662f\u5e76\u6ca1\u6709\u628a\u63e1\u4f4f\u5b83\u4eec\u672c\u8d28\uff0c\u5bf9\u5b83\u4eec\u7684\u7406\u89e3\u8fd8\u6ca1\u6709\u8fbe\u5230\u523b\u9aa8\u94ed\u5fc3\u7684\u7a0b\u5ea6\u3002\u5982\u679c\u89c9\u5f97\u8001\u5e08\u8bfe\u4e0a\u8bb2\u7684\u5185\u5bb9\u6666\u6da9\u96be\u61c2\uff0c\u4e0d\u59a8\u53c2\u8003 MIT \u7684 Calculus Course \u548c 18.06: Linear Algebra \u7684\u8bfe\u7a0b notes\uff0c\u81f3\u5c11\u4e8e\u6211\u800c\u8a00\uff0c\u5b83\u5e2e\u52a9\u6211\u6df1\u523b\u7406\u89e3\u4e86\u5fae\u79ef\u5206\u548c\u7ebf\u6027\u4ee3\u6570\u7684\u8bb8\u591a\u672c\u8d28\u3002\u987a\u9053\u518d\u5b89\u5229\u4e00\u4e2a\u6cb9\u7ba1\u6570\u5b66\u7f51\u7ea2 3Blue1Brown \uff0c\u4ed6\u7684\u9891\u9053\u6709\u5f88\u591a\u7528\u751f\u52a8\u5f62\u8c61\u7684\u52a8\u753b\u9610\u91ca\u6570\u5b66\u672c\u8d28\u5185\u6838\u7684\u89c6\u9891\uff0c\u517c\u5177\u6df1\u5ea6\u548c\u5e7f\u5ea6\uff0c\u8d28\u91cf\u975e\u5e38\u9ad8\u3002 \u4fe1\u606f\u8bba\u5165\u95e8 \u4f5c\u4e3a\u8ba1\u7b97\u673a\u7cfb\u7684\u5b66\u751f\uff0c\u53ca\u65e9\u4e86\u89e3\u4e00\u4e9b\u4fe1\u606f\u8bba\u7684\u57fa\u7840\u77e5\u8bc6\uff0c\u6211\u89c9\u5f97\u662f\u5927\u6709\u88e8\u76ca\u7684\u3002\u4f46\u5927\u591a\u4fe1\u606f\u8bba\u8bfe\u7a0b\u90fd\u9762\u5411\u9ad8\u5e74\u7ea7\u672c\u79d1\u751f\u751a\u81f3\u7814\u7a76\u751f\uff0c\u5bf9\u65b0\u624b\u6781\u4e0d\u53cb\u597d\u3002\u800c MIT \u7684 6.050J: Information theory and Entropy \u8fd9\u95e8\u8bfe\u6b63\u662f\u4e3a\u5927\u4e00\u65b0\u751f\u91cf\u8eab\u5b9a\u5236\u7684\uff0c\u51e0\u4e4e\u6ca1\u6709\u5148\u4fee\u8981\u6c42\uff0c\u6db5\u76d6\u4e86\u7f16\u7801\u3001\u538b\u7f29\u3001\u901a\u4fe1\u3001\u4fe1\u606f\u71b5\u7b49\u7b49\u5185\u5bb9\uff0c\u975e\u5e38\u6709\u8da3\u3002 \u6570\u5b66\u8fdb\u9636 \u79bb\u6563\u6570\u5b66\u4e0e\u6982\u7387\u8bba \u96c6\u5408\u8bba\u3001\u56fe\u8bba\u3001\u6982\u7387\u8bba\u7b49\u7b49\u662f\u7b97\u6cd5\u63a8\u5bfc\u4e0e\u8bc1\u660e\u7684\u91cd\u8981\u5de5\u5177\uff0c\u4e5f\u662f\u540e\u7eed\u9ad8\u9636\u6570\u5b66\u8bfe\u7a0b\u7684\u57fa\u7840\u3002\u4f46\u6211\u89c9\u5f97\u8fd9\u7c7b\u8bfe\u7a0b\u7684\u8bb2\u6388\u5f88\u5bb9\u6613\u843d\u5165\u7406\u8bba\u5316\u4e0e\u5f62\u5f0f\u5316\u7684\u7aa0\u81fc\uff0c\u8ba9\u8bfe\u5802\u6210\u4e3a\u5b9a\u7406\u7ed3\u8bba\u7684\u5806\u780c\uff0c\u800c\u65e0\u6cd5\u4f7f\u5b66\u751f\u6df1\u523b\u628a\u63e1\u7406\u8bba\u7684\u672c\u8d28\uff0c\u8fdb\u800c\u9020\u6210\u5b66\u4e86\u5c31\u80cc\uff0c\u8003\u4e86\u5c31\u5fd8\u7684\u602a\u5708\u3002\u5982\u679c\u80fd\u5728\u7406\u8bba\u6559\u5b66\u4e2d\u7a7f\u63d2\u7b97\u6cd5\u8fd0\u7528\u5b9e\u4f8b\uff0c\u5b66\u751f\u5728\u62d3\u5c55\u7b97\u6cd5\u77e5\u8bc6\u7684\u540c\u65f6\u4e5f\u80fd\u7aa5\u89c1\u7406\u8bba\u7684\u529b\u91cf\u548c\u9b45\u529b\u3002 UCB CS70 : discrete Math and probability theory \u548c UCB CS126 : Probability theory \u662f UC Berkeley \u7684\u6982\u7387\u8bba\u8bfe\u7a0b\uff0c\u524d\u8005\u8986\u76d6\u4e86\u79bb\u6563\u6570\u5b66\u548c\u6982\u7387\u8bba\u57fa\u7840\uff0c\u540e\u8005\u5219\u6d89\u53ca\u968f\u673a\u8fc7\u7a0b\u4ee5\u53ca\u6df1\u5165\u7684\u7406\u8bba\u5185\u5bb9\u3002\u4e24\u8005\u90fd\u975e\u5e38\u6ce8\u91cd\u7406\u8bba\u548c\u5b9e\u8df5\u7684\u7ed3\u5408\uff0c\u6709\u4e30\u5bcc\u7684\u7b97\u6cd5\u5b9e\u9645\u8fd0\u7528\u5b9e\u4f8b\uff0c\u540e\u8005\u8fd8\u6709\u5927\u91cf\u7684 Python \u7f16\u7a0b\u4f5c\u4e1a\u6765\u8ba9\u5b66\u751f\u8fd0\u7528\u6982\u7387\u8bba\u7684\u77e5\u8bc6\u89e3\u51b3\u5b9e\u9645\u95ee\u9898\u3002 \u6570\u503c\u5206\u6790 \u4f5c\u4e3a\u8ba1\u7b97\u673a\u7cfb\u7684\u5b66\u751f\uff0c\u57f9\u517b\u8ba1\u7b97\u601d\u7ef4\u662f\u5f88\u91cd\u8981\u7684\uff0c\u5b9e\u9645\u95ee\u9898\u7684\u5efa\u6a21\u3001\u79bb\u6563\u5316\uff0c\u8ba1\u7b97\u673a\u7684\u6a21\u62df\u3001\u5206\u6790\uff0c\u662f\u4e00\u9879\u5f88\u91cd\u8981\u7684\u80fd\u529b\u3002\u800c\u8fd9\u4e24\u5e74\u5f00\u59cb\u98ce\u9761\u7684\uff0c\u7531 MIT \u6253\u9020\u7684 Julia \u7f16\u7a0b\u8bed\u8a00\u4ee5\u5176 C \u4e00\u6837\u7684\u901f\u5ea6\u548c Python \u4e00\u6837\u53cb\u597d\u7684\u8bed\u6cd5\u5728\u6570\u503c\u8ba1\u7b97\u9886\u57df\u6709\u4e00\u7edf\u5929\u4e0b\u4e4b\u52bf\uff0cMIT \u7684\u8bb8\u591a\u6570\u5b66\u8bfe\u7a0b\u4e5f\u5f00\u59cb\u7528 Julia \u4f5c\u4e3a\u6559\u5b66\u5de5\u5177\uff0c\u628a\u8270\u6df1\u7684\u6570\u5b66\u7406\u8bba\u7528\u76f4\u89c2\u6e05\u6670\u7684\u4ee3\u7801\u5c55\u793a\u51fa\u6765\u3002 ComputationalThinking \u662f MIT \u5f00\u8bbe\u7684\u4e00\u95e8\u8ba1\u7b97\u601d\u7ef4\u5165\u95e8\u8bfe\uff0c\u6240\u6709\u8bfe\u7a0b\u5185\u5bb9\u5168\u90e8\u5f00\u6e90\uff0c\u53ef\u4ee5\u5728\u8bfe\u7a0b\u7f51\u7ad9\u76f4\u63a5\u8bbf\u95ee\u3002\u8fd9\u95e8\u8bfe\u5229\u7528 Julia \u7f16\u7a0b\u8bed\u8a00\uff0c\u5728\u56fe\u50cf\u5904\u7406\u3001\u793e\u4f1a\u79d1\u5b66\u4e0e\u6570\u636e\u79d1\u5b66\u3001\u6c14\u5019\u5b66\u5efa\u6a21\u4e09\u4e2a topic \u4e0b\u5e26\u9886\u5b66\u751f\u7406\u89e3\u7b97\u6cd5\u3001\u6570\u5b66\u5efa\u6a21\u3001\u6570\u636e\u5206\u6790\u3001\u4ea4\u4e92\u8bbe\u8ba1\u3001\u56fe\u4f8b\u5c55\u793a\uff0c\u8ba9\u5b66\u751f\u4f53\u9a8c\u8ba1\u7b97\u4e0e\u79d1\u5b66\u7684\u7f8e\u5999\u7ed3\u5408\u3002\u5185\u5bb9\u867d\u7136\u4e0d\u96be\uff0c\u4f46\u7ed9\u6211\u6700\u6df1\u523b\u7684\u611f\u53d7\u5c31\u662f\uff0c\u79d1\u5b66\u7684\u9b45\u529b\u5e76\u4e0d\u662f\u6545\u5f04\u7384\u865a\u7684\u8270\u6df1\u7406\u8bba\uff0c\u4e0d\u662f\u8bd8\u5c48\u8071\u7259\u7684\u672f\u8bed\u884c\u8bdd\uff0c\u800c\u662f\u7528\u76f4\u89c2\u751f\u52a8\u7684\u6848\u4f8b\uff0c\u7528\u7b80\u7ec3\u6df1\u523b\u7684\u8bed\u8a00\uff0c\u8ba9\u6bcf\u4e2a\u666e\u901a\u4eba\u90fd\u80fd\u7406\u89e3\u3002 \u4e0a\u5b8c\u4e0a\u9762\u7684\u4f53\u9a8c\u8bfe\u4e4b\u540e\uff0c\u5982\u679c\u610f\u72b9\u672a\u5c3d\u7684\u8bdd\uff0c\u4e0d\u59a8\u8bd5\u8bd5 MIT \u7684 18.330 : Introduction to numerical analysis \uff0c\u8fd9\u95e8\u8bfe\u7684\u7f16\u7a0b\u4f5c\u4e1a\u540c\u6837\u4f1a\u7528 Julia \u7f16\u7a0b\u8bed\u8a00\uff0c\u4e0d\u8fc7\u96be\u5ea6\u548c\u6df1\u5ea6\u4e0a\u90fd\u4e0a\u4e86\u4e00\u4e2a\u53f0\u9636\u3002\u5185\u5bb9\u6d89\u53ca\u4e86\u6d6e\u70b9\u7f16\u7801\u3001Root finding\u3001\u7ebf\u6027\u7cfb\u7edf\u3001\u5fae\u5206\u65b9\u7a0b\u7b49\u7b49\u65b9\u9762\uff0c\u6574\u95e8\u8bfe\u7684\u4e3b\u65e8\u5c31\u662f\u8ba9\u4f60\u5229\u7528\u79bb\u6563\u5316\u7684\u8ba1\u7b97\u673a\u8868\u793a\u53bb\u4f30\u8ba1\u548c\u903c\u8fd1\u4e00\u4e2a\u6570\u5b66\u4e0a\u8fde\u7eed\u7684\u6982\u5ff5\u3002\u8fd9\u95e8\u8bfe\u7684\u6559\u6388\u8fd8\u4e13\u95e8\u64b0\u5199\u4e86\u4e00\u672c\u914d\u5957\u7684\u5f00\u6e90\u6559\u6750 Fundamentals of Numerical Computation \uff0c\u91cc\u9762\u9644\u6709\u4e30\u5bcc\u7684 Julia \u4ee3\u7801\u5b9e\u4f8b\u548c\u4e25\u8c28\u7684\u516c\u5f0f\u63a8\u5bfc\u3002 \u5982\u679c\u4f60\u8fd8\u610f\u72b9\u672a\u5c3d\u7684\u8bdd\uff0c\u8fd8\u6709 MIT \u7684\u6570\u503c\u5206\u6790\u7814\u7a76\u751f\u8bfe\u7a0b 18.335: Introduction to numerical method \u4f9b\u4f60\u53c2\u8003\u3002 \u5fae\u5206\u65b9\u7a0b \u5982\u679c\u4e16\u95f4\u4e07\u7269\u7684\u8fd0\u52a8\u53d1\u5c55\u90fd\u80fd\u7528\u65b9\u7a0b\u6765\u523b\u753b\u548c\u63cf\u8ff0\uff0c\u8fd9\u662f\u4e00\u4ef6\u591a\u4e48\u9177\u7684\u4e8b\u60c5\u5440\uff01\u867d\u7136\u51e0\u4e4e\u4efb\u4f55\u4e00\u6240\u5b66\u6821\u7684 CS \u57f9\u517b\u65b9\u6848\u4e2d\u90fd\u6ca1\u6709\u5fae\u5206\u65b9\u7a0b\u76f8\u5173\u7684\u5fc5\u4fee\u8bfe\u7a0b\uff0c\u4f46\u6211\u8fd8\u662f\u89c9\u5f97\u638c\u63e1\u5b83\u4f1a\u8d4b\u4e88\u4f60\u4e00\u4e2a\u65b0\u7684\u89c6\u89d2\u6765\u5ba1\u89c6\u8fd9\u4e2a\u4e16\u754c\u3002 \u7531\u4e8e\u5fae\u5206\u65b9\u7a0b\u4e2d\u5f80\u5f80\u4f1a\u7528\u5230\u5f88\u591a\u590d\u53d8\u51fd\u6570\u7684\u77e5\u8bc6\uff0c\u6240\u4ee5\u5927\u5bb6\u53ef\u4ee5\u53c2\u8003 MIT18.04: Complex variables functions \u7684\u8bfe\u7a0b notes \u6765\u8865\u9f50\u5148\u4fee\u77e5\u8bc6\u3002 MIT18.03: differential equations \u4e3b\u8981\u8986\u76d6\u4e86\u5e38\u5fae\u5206\u65b9\u7a0b\u7684\u6c42\u89e3\uff0c\u5728\u6b64\u57fa\u7840\u4e4b\u4e0a MIT18.152: Partial differential equations \u5219\u4f1a\u6df1\u5165\u504f\u5fae\u5206\u65b9\u7a0b\u7684\u5efa\u6a21\u4e0e\u6c42\u89e3\u3002\u638c\u63e1\u4e86\u5fae\u5206\u65b9\u7a0b\u8fd9\u4e00\u6709\u529b\u5de5\u5177\uff0c\u76f8\u4fe1\u5bf9\u4e8e\u4f60\u7684\u5b9e\u9645\u95ee\u9898\u7684\u5efa\u6a21\u80fd\u529b\u4ee5\u53ca\u4ece\u4f17\u591a\u566a\u58f0\u53d8\u91cf\u4e2d\u628a\u63e1\u672c\u8d28\u7684\u76f4\u89c9\u90fd\u4f1a\u6709\u5f88\u5927\u5e2e\u52a9\u3002 \u6570\u5b66\u9ad8\u9636 \u4f5c\u4e3a\u8ba1\u7b97\u673a\u7cfb\u7684\u5b66\u751f\uff0c\u6211\u7ecf\u5e38\u542c\u5230\u6570\u5b66\u65e0\u7528\u8bba\u7684\u8bba\u65ad\uff0c\u5bf9\u6b64\u6211\u4e0d\u6562\u82df\u540c\u4f46\u4e5f\u65e0\u6743\u53cd\u5bf9\uff0c\u4f46\u82e5\u51e1\u4e8b\u90fd\u786c\u8981\u4e89\u51fa\u4e2a\u6709\u7528\u548c\u65e0\u7528\u7684\u533a\u522b\u6765\uff0c\u5012\u4e5f\u7740\u5b9e\u65e0\u8da3\uff0c\u56e0\u6b64\u4e0b\u9762\u8fd9\u4e9b\u9762\u5411\u9ad8\u5e74\u7ea7\u751a\u81f3\u7814\u7a76\u751f\u7684\u6570\u5b66\u8bfe\u7a0b\uff0c\u5927\u5bb6\u6309\u5174\u8da3\u81ea\u53d6\u6240\u9700\u3002 \u51f8\u4f18\u5316 Standford EE364A: Convex Optimization \u4fe1\u606f\u8bba MIT6.441: Information Theory \u5e94\u7528\u7edf\u8ba1\u5b66 MIT18.650: Statistics for Applications \u521d\u7b49\u6570\u8bba MIT18.781: Theory of Numbers \u5bc6\u7801\u5b66 Standford CS255: Cryptography \u7f16\u7a0b\u5165\u95e8 Languages are tools, you choose the right tool to do the right thing. Since there's no universally perfect tool, there's no universally perfect language. Shell MIT-Missing-Semester Python Harvard CS50: This is CS50x UCB CS61A: Structure and Interpretation of Computer Programs C++ Stanford CS106B/X: Programming Abstractions Stanford CS106L: Standard C++ Programming Rust Stanford CS110L: Safety in Systems Programming OCaml Cornell CS3110 textbook: Functional Programming in OCaml \u7535\u5b50\u57fa\u7840 \u7535\u8def\u57fa\u7840 \u4f5c\u4e3a\u8ba1\u7b97\u673a\u7cfb\u7684\u5b66\u751f\uff0c\u4e86\u89e3\u4e00\u4e9b\u57fa\u7840\u7684\u7535\u8def\u77e5\u8bc6\uff0c\u611f\u53d7\u4ece\u4f20\u611f\u5668\u6536\u96c6\u6570\u636e\u5230\u6570\u636e\u5206\u6790\u518d\u5230\u7b97\u6cd5\u9884\u6d4b\u6574\u6761\u6d41\u6c34\u7ebf\uff0c\u5bf9\u4e8e\u540e\u7eed\u77e5\u8bc6\u7684\u5b66\u4e60\u4ee5\u53ca\u8ba1\u7b97\u601d\u7ef4\u7684\u57f9\u517b\u8fd8\u662f\u5f88\u6709\u5e2e\u52a9\u7684\u3002 EE16A&B: Designing Information Devices and Systems I&II \u662f\u4f2f\u514b\u5229 EE \u5b66\u751f\u7684\u5927\u4e00\u5165\u95e8\u8bfe\uff0c\u5176\u4e2d EE16A \u6ce8\u91cd\u901a\u8fc7\u7535\u8def\u4ece\u5b9e\u9645\u73af\u5883\u4e2d\u6536\u96c6\u548c\u5206\u6790\u6570\u636e\uff0c\u800c EE16B \u5219\u4fa7\u91cd\u4ece\u8fd9\u4e9b\u6536\u96c6\u5230\u7684\u6570\u636e\u8fdb\u884c\u5206\u6790\u5e76\u505a\u51fa\u9884\u6d4b\u884c\u4e3a\u3002 \u4fe1\u53f7\u4e0e\u7cfb\u7edf \u4fe1\u53f7\u4e0e\u7cfb\u7edf\u662f\u4e00\u95e8\u6211\u89c9\u5f97\u975e\u5e38\u503c\u5f97\u4e00\u4e0a\u7684\u8bfe\uff0c\u6700\u521d\u5b66\u5b83\u53ea\u662f\u4e3a\u4e86\u6ee1\u8db3\u6211\u5bf9\u5085\u91cc\u53f6\u53d8\u6362\u7684\u597d\u5947\uff0c\u4f46\u5b66\u5b8c\u4e4b\u540e\u6211\u624d\u4e0d\u7981\u611f\u53f9\uff0c\u5085\u7acb\u53f6\u53d8\u6362\u7ed9\u6211\u63d0\u4f9b\u4e86\u4e00\u4e2a\u5168\u65b0\u7684\u89c6\u89d2\u53bb\u770b\u5f85\u8fd9\u4e2a\u4e16\u754c\uff0c\u5c31\u5982\u540c\u5fae\u5206\u65b9\u7a0b\u4e00\u6837\uff0c\u8ba9\u4f60\u6c89\u6d78\u5728\u7528\u6570\u5b66\u53bb\u7cbe\u786e\u63cf\u7ed8\u548c\u523b\u753b\u8fd9\u4e2a\u4e16\u754c\u7684\u4f18\u96c5\u4e0e\u795e\u5947\u4e4b\u4e2d\u3002 MIT 6.003: signal and systems \u63d0\u4f9b\u4e86\u5168\u90e8\u7684\u8bfe\u7a0b\u5f55\u5f71\u3001\u4e66\u9762\u4f5c\u4e1a\u4ee5\u53ca\u7b54\u6848\u3002\u4e5f\u53ef\u4ee5\u53bb\u770b\u8fd9\u95e8\u8bfe\u7684 \u8fdc\u53e4\u7248\u672c \u800c UCB EE120: Signal and Systems \u5173\u4e8e\u5085\u7acb\u53f6\u53d8\u6362\u7684 notes \u5199\u5f97\u975e\u5e38\u597d\uff0c\u5e76\u4e14\u63d0\u4f9b\u4e866 \u4e2a\u975e\u5e38\u6709\u8da3\u7684 Python \u7f16\u7a0b\u4f5c\u4e1a\uff0c\u8ba9\u4f60\u5b9e\u8df5\u4e2d\u8fd0\u7528\u4fe1\u53f7\u4e0e\u7cfb\u7edf\u7684\u7406\u8bba\u4e0e\u7b97\u6cd5\u3002 \u6570\u636e\u7ed3\u6784\u4e0e\u7b97\u6cd5 \u7b97\u6cd5\u662f\u8ba1\u7b97\u673a\u79d1\u5b66\u7684\u6838\u5fc3\uff0c\u4e5f\u662f\u51e0\u4e4e\u4e00\u5207\u4e13\u4e1a\u8bfe\u7a0b\u7684\u57fa\u7840\u3002\u5982\u4f55\u5c06\u5b9e\u9645\u95ee\u9898\u901a\u8fc7\u6570\u5b66\u62bd\u8c61\u8f6c\u5316\u4e3a\u7b97\u6cd5\u95ee\u9898\uff0c\u5e76\u9009\u7528\u5408\u9002\u7684\u6570\u636e\u7ed3\u6784\u5728\u65f6\u95f4\u548c\u5185\u5b58\u5927\u5c0f\u7684\u9650\u5236\u4e0b\u5c06\u5176\u89e3\u51b3\u662f\u7b97\u6cd5\u8bfe\u7684\u6c38\u6052\u4e3b\u9898\u3002\u5982\u679c\u4f60\u53d7\u591f\u4e86\u8001\u5e08\u7684\u7167\u672c\u5ba3\u79d1\uff0c\u90a3\u4e48\u6211\u5f3a\u70c8\u63a8\u8350\u4f2f\u514b\u5229\u7684 UCB CS61B: Data Structures and Algorithms \u548c\u666e\u6797\u65af\u987f\u7684 Coursera: Algorithms I & II \uff0c\u8fd9\u4e24\u95e8\u8bfe\u7684\u90fd\u8bb2\u5f97\u6df1\u5165\u6d45\u51fa\u5e76\u4e14\u4f1a\u6709\u4e30\u5bcc\u4e14\u6709\u8da3\u7684\u7f16\u7a0b\u5b9e\u9a8c\u5c06\u7406\u8bba\u4e0e\u77e5\u8bc6\u7ed3\u5408\u8d77\u6765\u3002\u6b64\u5916\uff0c\u5bf9\u4e00\u4e9b\u66f4\u9ad8\u7ea7\u7684\u7b97\u6cd5\u4ee5\u53ca NP \u95ee\u9898\u611f\u5174\u8da3\u7684\u540c\u5b66\u53ef\u4ee5\u5b66\u4e60\u4f2f\u514b\u5229\u7684\u7b97\u6cd5\u8bbe\u8ba1\u4e0e\u5206\u6790\u8bfe\u7a0b UCB CS170: Efficient Algorithms and Intractable Problems \u3002 \u8f6f\u4ef6\u5de5\u7a0b \u5165\u95e8\u8bfe \u4e00\u4efd\u201c\u80fd\u8dd1\u201d\u7684\u4ee3\u7801\uff0c\u548c\u4e00\u4efd\u9ad8\u8d28\u91cf\u7684\u5de5\u4e1a\u7ea7\u4ee3\u7801\u662f\u6709\u672c\u8d28\u533a\u522b\u7684\u3002\u56e0\u6b64\u6211\u975e\u5e38\u63a8\u8350\u4f4e\u5e74\u7ea7\u7684\u540c\u5b66\u5b66\u4e60\u4e00\u4e0b MIT 6.031: Software Construction \u8fd9\u95e8\u8bfe\uff0c\u5b83\u4f1a\u4ee5 Java \u8bed\u8a00\u4e3a\u57fa\u7840\uff0c\u4ee5\u4e30\u5bcc\u7ec6\u81f4\u7684\u9605\u8bfb\u6750\u6599\u548c\u7cbe\u5fc3\u8bbe\u8ba1\u7684\u7f16\u7a0b\u7ec3\u4e60\u4f20\u6388\u5982\u4f55\u7f16\u5199 \u4e0d\u6613\u51fa bug\u3001\u7b80\u660e\u6613\u61c2\u3001\u6613\u4e8e\u7ef4\u62a4\u4fee\u6539 \u7684\u9ad8\u8d28\u91cf\u4ee3\u7801\u3002\u5927\u5230\u5b8f\u89c2\u6570\u636e\u7ed3\u6784\u8bbe\u8ba1\uff0c\u5c0f\u5230\u5982\u4f55\u5199\u6ce8\u91ca\uff0c\u9075\u5faa\u8fd9\u4e9b\u524d\u4eba\u603b\u7ed3\u7684\u7ec6\u8282\u548c\u7ecf\u9a8c\uff0c\u5bf9\u4e8e\u4f60\u6b64\u540e\u7684\u7f16\u7a0b\u751f\u6daf\u5927\u6709\u88e8\u76ca\u3002 \u4e13\u4e1a\u8bfe \u5f53\u7136\uff0c\u5982\u679c\u4f60\u60f3\u7cfb\u7edf\u6027\u5730\u4e0a\u4e00\u95e8\u8f6f\u4ef6\u5de5\u7a0b\u7684\u8bfe\u7a0b\uff0c\u90a3\u6211\u63a8\u8350\u7684\u662f\u4f2f\u514b\u5229\u7684 UCB CS169: software engineering \u3002\u4f46\u9700\u8981\u63d0\u9192\u7684\u662f\uff0c\u548c\u5927\u591a\u5b66\u6821\uff08\u5305\u62ec\u8d35\u6821\uff09\u7684\u8f6f\u4ef6\u5de5\u7a0b\u8bfe\u7a0b\u4e0d\u540c\uff0c\u8fd9\u95e8\u8bfe\u4e0d\u4f1a\u6d89\u53ca\u4f20\u7edf\u7684 design and document \u6a21\u5f0f\uff0c\u5373\u5f3a\u8c03\u5404\u79cd\u7c7b\u56fe\u3001\u6d41\u7a0b\u56fe\u53ca\u6587\u6863\u8bbe\u8ba1\uff0c\u800c\u662f\u91c7\u7528\u8fd1\u4e9b\u5e74\u6d41\u884c\u8d77\u6765\u7684\u5c0f\u56e2\u961f\u5feb\u901f\u8fed\u4ee3 Agile Develepment \u5f00\u53d1\u6a21\u5f0f\u4ee5\u53ca\u5229\u7528\u4e91\u5e73\u53f0\u7684 Software as a service \u670d\u52a1\u6a21\u5f0f\u3002 \u4f53\u7cfb\u7ed3\u6784 \u5165\u95e8\u8bfe \u4ece\u5c0f\u6211\u5c31\u4e00\u76f4\u542c\u8bf4\uff0c\u8ba1\u7b97\u673a\u7684\u4e16\u754c\u662f\u7531 01 \u6784\u6210\u7684\uff0c\u6211\u4e0d\u7406\u89e3\u4f46\u5927\u53d7\u9707\u64bc\u3002\u5982\u679c\u4f60\u7684\u5185\u5fc3\u4e5f\u6000\u6709\u8fd9\u4efd\u597d\u5947\uff0c\u4e0d\u59a8\u82b1\u4e00\u5230\u4e24\u4e2a\u6708\u7684\u65f6\u95f4\u5b66\u4e60 Coursera: Nand2Tetris \u8fd9\u95e8\u65e0\u95e8\u69db\u7684\u8ba1\u7b97\u673a\u8bfe\u7a0b\u3002\u8fd9\u95e8\u9ebb\u96c0\u867d\u5c0f\u4e94\u810f\u4ff1\u5168\u7684\u8bfe\u7a0b\u4f1a\u4ece 01 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MIT6.033: System Engineering \u662f MIT \u7684\u7cfb\u7edf\u5165\u95e8\u8bfe\uff0c\u4e3b\u9898\u6d89\u53ca\u4e86\u64cd\u4f5c\u7cfb\u7edf\u3001\u7f51\u7edc\u3001\u5206\u5e03\u5f0f\u548c\u7cfb\u7edf\u5b89\u5168\uff0c\u9664\u4e86\u77e5\u8bc6\u70b9\u7684\u4f20\u6388\u5916\uff0c\u8fd9\u95e8\u8bfe\u8fd8\u4f1a\u8bb2\u6388\u4e00\u4e9b\u5199\u4f5c\u548c\u8868\u8fbe\u4e0a\u7684\u6280\u5de7\uff0c\u8ba9\u4f60\u5b66\u4f1a\u5982\u4f55\u8bbe\u8ba1\u5e76\u5411\u522b\u4eba\u4ecb\u7ecd\u548c\u5206\u6790\u81ea\u5df1\u7684\u7cfb\u7edf\u3002\u8fd9\u672c\u4e66\u914d\u5957\u7684\u6559\u6750 Principles of Computer System Design: An Introduction \u4e5f\u5199\u5f97\u975e\u5e38\u597d\uff0c\u63a8\u8350\u5927\u5bb6\u9605\u8bfb\u3002 CMU 15-213: Introduction to Computer System \u662f CMU \u7684\u7cfb\u7edf\u5165\u95e8\u8bfe\uff0c\u5185\u5bb9\u8986\u76d6\u4e86\u4f53\u7cfb\u7ed3\u6784\u3001\u64cd\u4f5c\u7cfb\u7edf\u3001\u94fe\u63a5\u3001\u5e76\u884c\u3001\u7f51\u7edc\u7b49\u7b49\uff0c\u517c\u5177\u5e7f\u5ea6\u548c\u6df1\u5ea6\uff0c\u914d\u5957\u7684\u6559\u6750 Computer Systems: A Programmer's Perspective \u4e5f\u662f\u8d28\u91cf\u6781\u9ad8\uff0c\u5f3a\u70c8\u5efa\u8bae\u9605\u8bfb\u3002 \u64cd\u4f5c\u7cfb\u7edf \u6ca1\u6709\u4ec0\u4e48\u80fd\u6bd4\u81ea\u5df1\u5199\u4e2a\u5185\u6838\u66f4\u80fd\u52a0\u6df1\u5bf9\u64cd\u4f5c\u7cfb\u7edf\u7684\u7406\u89e3\u4e86\u3002 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CMU 15-418/Stanford CS149: Parallel Computing \u5206\u5e03\u5f0f\u7cfb\u7edf MIT 6.824: Distributed System \u7cfb\u7edf\u5b89\u5168 \u4e0d\u77e5\u9053\u4f60\u5f53\u5e74\u9009\u62e9\u8ba1\u7b97\u673a\u662f\u4e0d\u662f\u56e0\u4e3a\u6000\u7740\u4e00\u4e2a\u4e2d\u4e8c\u7684\u9ed1\u5ba2\u68a6\u60f3\uff0c\u4f46\u73b0\u5b9e\u5374\u662f\u6210\u4e3a\u9ed1\u5ba2\u9053\u963b\u4e14\u957f\u3002 \u7406\u8bba\u8bfe\u7a0b UCB CS161: Computer Security \u662f\u4f2f\u514b\u5229\u7684\u7cfb\u7edf\u5b89\u5168\u8bfe\u7a0b\uff0c\u4f1a\u6db5\u76d6\u6808\u653b\u51fb\u3001\u5bc6\u7801\u5b66\u3001\u7f51\u7ad9\u5b89\u5168\u3001\u7f51\u7edc\u5b89\u5168\u7b49\u7b49\u5185\u5bb9\u3002 \u5b9e\u8df5\u8bfe\u7a0b \u638c\u63e1\u8fd9\u4e9b\u7406\u8bba\u77e5\u8bc6\u4e4b\u540e\uff0c\u8fd8\u9700\u8981\u5728\u5b9e\u8df5\u4e2d\u57f9\u517b\u548c\u953b\u70bc\u8fd9\u4e9b\u201c\u9ed1\u5ba2\u7d20\u517b\u201d\u3002 CTF \u593a\u65d7\u8d5b 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\uff0c\u8fd9\u95e8\u8bfe\u7684\u7f16\u7a0b\u4f5c\u4e1a\u540c\u6837\u4f1a\u7528 Julia \u7f16\u7a0b\u8bed\u8a00\uff0c\u4e0d\u8fc7\u96be\u5ea6\u548c\u6df1\u5ea6\u4e0a\u90fd\u4e0a\u4e86\u4e00\u4e2a\u53f0\u9636\u3002\u5185\u5bb9\u6d89\u53ca\u4e86\u6d6e\u70b9\u7f16\u7801\u3001Root finding\u3001\u7ebf\u6027\u7cfb\u7edf\u3001\u5fae\u5206\u65b9\u7a0b\u7b49\u7b49\u65b9\u9762\uff0c\u6574\u95e8\u8bfe\u7684\u4e3b\u65e8\u5c31\u662f\u8ba9\u4f60\u5229\u7528\u79bb\u6563\u5316\u7684\u8ba1\u7b97\u673a\u8868\u793a\u53bb\u4f30\u8ba1\u548c\u903c\u8fd1\u4e00\u4e2a\u6570\u5b66\u4e0a\u8fde\u7eed\u7684\u6982\u5ff5\u3002\u8fd9\u95e8\u8bfe\u7684\u6559\u6388\u8fd8\u4e13\u95e8\u64b0\u5199\u4e86\u4e00\u672c\u914d\u5957\u7684\u5f00\u6e90\u6559\u6750 Fundamentals of Numerical Computation \uff0c\u91cc\u9762\u9644\u6709\u4e30\u5bcc\u7684 Julia \u4ee3\u7801\u5b9e\u4f8b\u548c\u4e25\u8c28\u7684\u516c\u5f0f\u63a8\u5bfc\u3002 \u5982\u679c\u4f60\u8fd8\u610f\u72b9\u672a\u5c3d\u7684\u8bdd\uff0c\u8fd8\u6709 MIT \u7684\u6570\u503c\u5206\u6790\u7814\u7a76\u751f\u8bfe\u7a0b 18.335: Introduction to numerical method \u4f9b\u4f60\u53c2\u8003\u3002","title":"\u6570\u503c\u5206\u6790"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_12","text":"\u5982\u679c\u4e16\u95f4\u4e07\u7269\u7684\u8fd0\u52a8\u53d1\u5c55\u90fd\u80fd\u7528\u65b9\u7a0b\u6765\u523b\u753b\u548c\u63cf\u8ff0\uff0c\u8fd9\u662f\u4e00\u4ef6\u591a\u4e48\u9177\u7684\u4e8b\u60c5\u5440\uff01\u867d\u7136\u51e0\u4e4e\u4efb\u4f55\u4e00\u6240\u5b66\u6821\u7684 CS \u57f9\u517b\u65b9\u6848\u4e2d\u90fd\u6ca1\u6709\u5fae\u5206\u65b9\u7a0b\u76f8\u5173\u7684\u5fc5\u4fee\u8bfe\u7a0b\uff0c\u4f46\u6211\u8fd8\u662f\u89c9\u5f97\u638c\u63e1\u5b83\u4f1a\u8d4b\u4e88\u4f60\u4e00\u4e2a\u65b0\u7684\u89c6\u89d2\u6765\u5ba1\u89c6\u8fd9\u4e2a\u4e16\u754c\u3002 \u7531\u4e8e\u5fae\u5206\u65b9\u7a0b\u4e2d\u5f80\u5f80\u4f1a\u7528\u5230\u5f88\u591a\u590d\u53d8\u51fd\u6570\u7684\u77e5\u8bc6\uff0c\u6240\u4ee5\u5927\u5bb6\u53ef\u4ee5\u53c2\u8003 MIT18.04: Complex variables functions \u7684\u8bfe\u7a0b notes \u6765\u8865\u9f50\u5148\u4fee\u77e5\u8bc6\u3002 MIT18.03: differential equations \u4e3b\u8981\u8986\u76d6\u4e86\u5e38\u5fae\u5206\u65b9\u7a0b\u7684\u6c42\u89e3\uff0c\u5728\u6b64\u57fa\u7840\u4e4b\u4e0a MIT18.152: Partial differential equations \u5219\u4f1a\u6df1\u5165\u504f\u5fae\u5206\u65b9\u7a0b\u7684\u5efa\u6a21\u4e0e\u6c42\u89e3\u3002\u638c\u63e1\u4e86\u5fae\u5206\u65b9\u7a0b\u8fd9\u4e00\u6709\u529b\u5de5\u5177\uff0c\u76f8\u4fe1\u5bf9\u4e8e\u4f60\u7684\u5b9e\u9645\u95ee\u9898\u7684\u5efa\u6a21\u80fd\u529b\u4ee5\u53ca\u4ece\u4f17\u591a\u566a\u58f0\u53d8\u91cf\u4e2d\u628a\u63e1\u672c\u8d28\u7684\u76f4\u89c9\u90fd\u4f1a\u6709\u5f88\u5927\u5e2e\u52a9\u3002","title":"\u5fae\u5206\u65b9\u7a0b"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_13","text":"\u4f5c\u4e3a\u8ba1\u7b97\u673a\u7cfb\u7684\u5b66\u751f\uff0c\u6211\u7ecf\u5e38\u542c\u5230\u6570\u5b66\u65e0\u7528\u8bba\u7684\u8bba\u65ad\uff0c\u5bf9\u6b64\u6211\u4e0d\u6562\u82df\u540c\u4f46\u4e5f\u65e0\u6743\u53cd\u5bf9\uff0c\u4f46\u82e5\u51e1\u4e8b\u90fd\u786c\u8981\u4e89\u51fa\u4e2a\u6709\u7528\u548c\u65e0\u7528\u7684\u533a\u522b\u6765\uff0c\u5012\u4e5f\u7740\u5b9e\u65e0\u8da3\uff0c\u56e0\u6b64\u4e0b\u9762\u8fd9\u4e9b\u9762\u5411\u9ad8\u5e74\u7ea7\u751a\u81f3\u7814\u7a76\u751f\u7684\u6570\u5b66\u8bfe\u7a0b\uff0c\u5927\u5bb6\u6309\u5174\u8da3\u81ea\u53d6\u6240\u9700\u3002","title":"\u6570\u5b66\u9ad8\u9636"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_14","text":"Standford EE364A: Convex Optimization","title":"\u51f8\u4f18\u5316"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_15","text":"MIT6.441: Information Theory","title":"\u4fe1\u606f\u8bba"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_16","text":"MIT18.650: Statistics for Applications","title":"\u5e94\u7528\u7edf\u8ba1\u5b66"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_17","text":"MIT18.781: Theory of Numbers","title":"\u521d\u7b49\u6570\u8bba"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_18","text":"Standford CS255: Cryptography","title":"\u5bc6\u7801\u5b66"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_19","text":"Languages are tools, you choose the right tool to do the right thing. Since there's no universally perfect tool, there's no universally perfect language.","title":"\u7f16\u7a0b\u5165\u95e8"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#shell","text":"MIT-Missing-Semester","title":"Shell"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#python","text":"Harvard CS50: This is CS50x UCB CS61A: Structure and Interpretation of Computer Programs","title":"Python"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#c","text":"Stanford CS106B/X: Programming Abstractions Stanford CS106L: Standard C++ Programming","title":"C++"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#rust","text":"Stanford CS110L: Safety in Systems Programming","title":"Rust"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#ocaml","text":"Cornell CS3110 textbook: Functional Programming in OCaml","title":"OCaml"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_20","text":"","title":"\u7535\u5b50\u57fa\u7840"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_21","text":"\u4f5c\u4e3a\u8ba1\u7b97\u673a\u7cfb\u7684\u5b66\u751f\uff0c\u4e86\u89e3\u4e00\u4e9b\u57fa\u7840\u7684\u7535\u8def\u77e5\u8bc6\uff0c\u611f\u53d7\u4ece\u4f20\u611f\u5668\u6536\u96c6\u6570\u636e\u5230\u6570\u636e\u5206\u6790\u518d\u5230\u7b97\u6cd5\u9884\u6d4b\u6574\u6761\u6d41\u6c34\u7ebf\uff0c\u5bf9\u4e8e\u540e\u7eed\u77e5\u8bc6\u7684\u5b66\u4e60\u4ee5\u53ca\u8ba1\u7b97\u601d\u7ef4\u7684\u57f9\u517b\u8fd8\u662f\u5f88\u6709\u5e2e\u52a9\u7684\u3002 EE16A&B: Designing Information Devices and Systems I&II \u662f\u4f2f\u514b\u5229 EE \u5b66\u751f\u7684\u5927\u4e00\u5165\u95e8\u8bfe\uff0c\u5176\u4e2d EE16A \u6ce8\u91cd\u901a\u8fc7\u7535\u8def\u4ece\u5b9e\u9645\u73af\u5883\u4e2d\u6536\u96c6\u548c\u5206\u6790\u6570\u636e\uff0c\u800c EE16B \u5219\u4fa7\u91cd\u4ece\u8fd9\u4e9b\u6536\u96c6\u5230\u7684\u6570\u636e\u8fdb\u884c\u5206\u6790\u5e76\u505a\u51fa\u9884\u6d4b\u884c\u4e3a\u3002","title":"\u7535\u8def\u57fa\u7840"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_22","text":"\u4fe1\u53f7\u4e0e\u7cfb\u7edf\u662f\u4e00\u95e8\u6211\u89c9\u5f97\u975e\u5e38\u503c\u5f97\u4e00\u4e0a\u7684\u8bfe\uff0c\u6700\u521d\u5b66\u5b83\u53ea\u662f\u4e3a\u4e86\u6ee1\u8db3\u6211\u5bf9\u5085\u91cc\u53f6\u53d8\u6362\u7684\u597d\u5947\uff0c\u4f46\u5b66\u5b8c\u4e4b\u540e\u6211\u624d\u4e0d\u7981\u611f\u53f9\uff0c\u5085\u7acb\u53f6\u53d8\u6362\u7ed9\u6211\u63d0\u4f9b\u4e86\u4e00\u4e2a\u5168\u65b0\u7684\u89c6\u89d2\u53bb\u770b\u5f85\u8fd9\u4e2a\u4e16\u754c\uff0c\u5c31\u5982\u540c\u5fae\u5206\u65b9\u7a0b\u4e00\u6837\uff0c\u8ba9\u4f60\u6c89\u6d78\u5728\u7528\u6570\u5b66\u53bb\u7cbe\u786e\u63cf\u7ed8\u548c\u523b\u753b\u8fd9\u4e2a\u4e16\u754c\u7684\u4f18\u96c5\u4e0e\u795e\u5947\u4e4b\u4e2d\u3002 MIT 6.003: signal and systems \u63d0\u4f9b\u4e86\u5168\u90e8\u7684\u8bfe\u7a0b\u5f55\u5f71\u3001\u4e66\u9762\u4f5c\u4e1a\u4ee5\u53ca\u7b54\u6848\u3002\u4e5f\u53ef\u4ee5\u53bb\u770b\u8fd9\u95e8\u8bfe\u7684 \u8fdc\u53e4\u7248\u672c \u800c UCB EE120: Signal and Systems \u5173\u4e8e\u5085\u7acb\u53f6\u53d8\u6362\u7684 notes \u5199\u5f97\u975e\u5e38\u597d\uff0c\u5e76\u4e14\u63d0\u4f9b\u4e866 \u4e2a\u975e\u5e38\u6709\u8da3\u7684 Python \u7f16\u7a0b\u4f5c\u4e1a\uff0c\u8ba9\u4f60\u5b9e\u8df5\u4e2d\u8fd0\u7528\u4fe1\u53f7\u4e0e\u7cfb\u7edf\u7684\u7406\u8bba\u4e0e\u7b97\u6cd5\u3002","title":"\u4fe1\u53f7\u4e0e\u7cfb\u7edf"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_23","text":"\u7b97\u6cd5\u662f\u8ba1\u7b97\u673a\u79d1\u5b66\u7684\u6838\u5fc3\uff0c\u4e5f\u662f\u51e0\u4e4e\u4e00\u5207\u4e13\u4e1a\u8bfe\u7a0b\u7684\u57fa\u7840\u3002\u5982\u4f55\u5c06\u5b9e\u9645\u95ee\u9898\u901a\u8fc7\u6570\u5b66\u62bd\u8c61\u8f6c\u5316\u4e3a\u7b97\u6cd5\u95ee\u9898\uff0c\u5e76\u9009\u7528\u5408\u9002\u7684\u6570\u636e\u7ed3\u6784\u5728\u65f6\u95f4\u548c\u5185\u5b58\u5927\u5c0f\u7684\u9650\u5236\u4e0b\u5c06\u5176\u89e3\u51b3\u662f\u7b97\u6cd5\u8bfe\u7684\u6c38\u6052\u4e3b\u9898\u3002\u5982\u679c\u4f60\u53d7\u591f\u4e86\u8001\u5e08\u7684\u7167\u672c\u5ba3\u79d1\uff0c\u90a3\u4e48\u6211\u5f3a\u70c8\u63a8\u8350\u4f2f\u514b\u5229\u7684 UCB CS61B: Data Structures and Algorithms \u548c\u666e\u6797\u65af\u987f\u7684 Coursera: Algorithms I & II \uff0c\u8fd9\u4e24\u95e8\u8bfe\u7684\u90fd\u8bb2\u5f97\u6df1\u5165\u6d45\u51fa\u5e76\u4e14\u4f1a\u6709\u4e30\u5bcc\u4e14\u6709\u8da3\u7684\u7f16\u7a0b\u5b9e\u9a8c\u5c06\u7406\u8bba\u4e0e\u77e5\u8bc6\u7ed3\u5408\u8d77\u6765\u3002\u6b64\u5916\uff0c\u5bf9\u4e00\u4e9b\u66f4\u9ad8\u7ea7\u7684\u7b97\u6cd5\u4ee5\u53ca NP \u95ee\u9898\u611f\u5174\u8da3\u7684\u540c\u5b66\u53ef\u4ee5\u5b66\u4e60\u4f2f\u514b\u5229\u7684\u7b97\u6cd5\u8bbe\u8ba1\u4e0e\u5206\u6790\u8bfe\u7a0b UCB CS170: Efficient Algorithms and Intractable Problems 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2021\u5e7412\u670812\u65e5\u5199\u4e8e\u71d5\u56ed","title":"\u540e\u8bb0"},{"location":"en/%E5%9F%B9%E5%85%BB%E6%96%B9%E6%A1%88Pro/","text":"under construction.","title":"\u57f9\u517b\u65b9\u6848Pro"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/","text":"\u597d\u4e66\u63a8\u8350 \u7531\u4e8e\u7248\u6743\u539f\u56e0\uff0c\u4e0b\u9762\u5217\u4e3e\u7684\u56fe\u4e66\u4e2d\u9664\u4e86\u5f00\u6e90\u8d44\u6e90\u63d0\u4f9b\u4e86\u94fe\u63a5\uff0c\u5176\u4ed6\u7684\u8d44\u6e90\u8bf7\u5927\u5bb6\u81ea\u884c\u901a\u8fc7 libgen \u6216 z-lib \u67e5\u627e\u3002 \u8d44\u6e90\u6c47\u603b Free Programming Books : \u5f00\u6e90\u7f16\u7a0b\u4e66\u7c4d\u8d44\u6e90\u6c47\u603b CS Textbook Recommendations : \u8ba1\u7b97\u673a\u79d1\u5b66\u65b9\u5411\u63a8\u8350\u6559\u6750\u5217\u8868 C Book Guide and List : C\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868 C++ Book Guide and List : C++\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868 Python Book Guide and List : Python\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868 \u7cfb\u7edf\u5165\u95e8 Computer Systems: A Programmer's Perspective [ \u8c46\u74e3 ] Principles of Computer System Design: An Introduction [ \u8c46\u74e3 ] \u64cd\u4f5c\u7cfb\u7edf \u73b0\u4ee3\u64cd\u4f5c\u7cfb\u7edf: \u539f\u7406\u4e0e\u5b9e\u73b0 [ \u8c46\u74e3 ] Operating Systems: Three Easy Pieces [ \u8c46\u74e3 ] Modern Operating Systems [ \u8c46\u74e3 ] Operating Systems: Principles and Practice [ \u8c46\u74e3 ] \u8ba1\u7b97\u673a\u7f51\u7edc Computer Networks: A Systems Approach [ \u8c46\u74e3 ] Computer Networking: A Top-Down Approach [ \u8c46\u74e3 ] \u5206\u5e03\u5f0f\u7cfb\u7edf Patterns of Distributed System (Blog) Distributed Systems for Fun and Profit (Blog) Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems [ \u8c46\u74e3 ] \u6570\u636e\u5e93\u7cfb\u7edf Architecture of a Database System [ \u8c46\u74e3 ] Readings in Database Systems [ \u8c46\u74e3 ] Database System Concepts [ \u8c46\u74e3 ] \u7f16\u8bd1\u539f\u7406 Engineering a Compiler [ \u8c46\u74e3 ] Compilers: Principles, Techniques, and Tools [ \u8c46\u74e3 ] \u8ba1\u7b97\u673a\u7f16\u7a0b\u8bed\u8a00 \u8ba1\u7b97\u673a\u7a0b\u5e8f\u7684\u6784\u9020\u548c\u89e3\u91ca [ \u8c46\u74e3 ] Essentials of Programming Languages [ \u8c46\u74e3 ] Practical Foundations for Programming Languages [ \u8c46\u74e3 ] Software Foundations [ \u8c46\u74e3 ] [ \u5317\u5927\u76f8\u5173\u8bfe\u7a0b ] Types and Programming Languages [ \u8c46\u74e3 ] [ \u5317\u5927\u76f8\u5173\u8bfe\u7a0b ] \u4f53\u7cfb\u7ed3\u6784 \u8d85\u6807\u91cf\u5904\u7406\u5668\u8bbe\u8ba1: Superscalar RISC Processor Design [ \u8c46\u74e3 ] Computer Organization and Design RISC-V Edition [ \u8c46\u74e3 ] Computer Organization and Design: The Hardware/Software Interface [ \u8c46\u74e3 ] Computer Architecture: A Quantitative Approach [ \u8c46\u74e3 ] \u7406\u8bba\u8ba1\u7b97\u673a\u79d1\u5b66 Introduction to the Theory of Computation [ \u8c46\u74e3 ] \u5bc6\u7801\u5b66 Cryptography Engineering: Design Principles and Practical Applications [ \u8c46\u74e3 ] Introduction to Modern Cryptography [ \u8c46\u74e3 ] \u9006\u5411\u5de5\u7a0b \u9006\u5411\u5de5\u7a0b\u6838\u5fc3\u539f\u7406 [ \u8c46\u74e3 ] \u52a0\u5bc6\u4e0e\u89e3\u5bc6 [ \u8c46\u74e3 ] \u8ba1\u7b97\u673a\u56fe\u5f62\u5b66 Monte Carlo theory, methods and examples Advanced Global Illumination [ \u8c46\u74e3 ] Fundamentals of Computer Graphics [ \u8c46\u74e3 ] Fluid Simulation for Computer Graphics [ \u8c46\u74e3 ] Physically Based Rendering: From Theory To Implementation [ \u8c46\u74e3 ] Real-Time Rendering [ \u8c46\u74e3 ] \u6e38\u620f\u5f15\u64ce \u6e38\u620f\u7f16\u7a0b\u6a21\u5f0f: Game Programming Patterns [ \u8c46\u74e3 ] \u5b9e\u65f6\u78b0\u649e\u68c0\u6d4b\u7b97\u6cd5\u6280\u672f [ \u8c46\u74e3 ] Game AI Pro Series [ \u8c46\u74e3 ] Artificial Intelligence for Games [ \u8c46\u74e3 ] Game Engine Architecture [ \u8c46\u74e3 ] Game Programming Gems Series [ \u8c46\u74e3 ] \u8f6f\u4ef6\u5de5\u7a0b Software Engineering at Google [ \u8c46\u74e3 ] \u8bbe\u8ba1\u6a21\u5f0f \u8bbe\u8ba1\u6a21\u5f0f: \u53ef\u590d\u7528\u9762\u5411\u5bf9\u8c61\u8f6f\u4ef6\u7684\u57fa\u7840 [ \u8c46\u74e3 ] \u5927\u8bdd\u8bbe\u8ba1\u6a21\u5f0f [ \u8c46\u74e3 ] Head First \u8bbe\u8ba1\u6a21\u5f0f [ \u8c46\u74e3 ] \u6df1\u5ea6\u5b66\u4e60 \u52a8\u624b\u5b66\u6df1\u5ea6\u5b66\u4e60 [ \u8c46\u74e3 ] \u795e\u7ecf\u7f51\u7edc\u4e0e\u6df1\u5ea6\u5b66\u4e60 [ \u8c46\u74e3 ] \u6df1\u5ea6\u5b66\u4e60\u5165\u95e8 [ \u8c46\u74e3 ] \u7b80\u5355\u7c97\u66b4 TensorFlow 2 (Tutorial) Speech and Language Processing [ \u8c46\u74e3 ] \u8ba1\u7b97\u673a\u89c6\u89c9 Multiple View Geometry in Computer Vision [ \u8c46\u74e3 ] \u673a\u5668\u4eba Probabilistic Robotics [ \u8c46\u74e3 ] \u9762\u8bd5 \u5251\u6307 Offer\uff1a\u540d\u4f01\u9762\u8bd5\u5b98\u7cbe\u8bb2\u5178\u578b\u7f16\u7a0b\u9898 [ \u8c46\u74e3 ] Cracking The Coding Interview [ \u8c46\u74e3 ]","title":"Book Recommendation"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_1","text":"\u7531\u4e8e\u7248\u6743\u539f\u56e0\uff0c\u4e0b\u9762\u5217\u4e3e\u7684\u56fe\u4e66\u4e2d\u9664\u4e86\u5f00\u6e90\u8d44\u6e90\u63d0\u4f9b\u4e86\u94fe\u63a5\uff0c\u5176\u4ed6\u7684\u8d44\u6e90\u8bf7\u5927\u5bb6\u81ea\u884c\u901a\u8fc7 libgen \u6216 z-lib \u67e5\u627e\u3002","title":"\u597d\u4e66\u63a8\u8350"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_2","text":"Free Programming Books : \u5f00\u6e90\u7f16\u7a0b\u4e66\u7c4d\u8d44\u6e90\u6c47\u603b CS Textbook Recommendations : \u8ba1\u7b97\u673a\u79d1\u5b66\u65b9\u5411\u63a8\u8350\u6559\u6750\u5217\u8868 C Book Guide and List : C\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868 C++ Book Guide and List : C++\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868 Python Book Guide and List : Python\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_3","text":"Computer Systems: A Programmer's Perspective [ \u8c46\u74e3 ] Principles of Computer System Design: An Introduction [ \u8c46\u74e3 ]","title":"\u7cfb\u7edf\u5165\u95e8"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_4","text":"\u73b0\u4ee3\u64cd\u4f5c\u7cfb\u7edf: \u539f\u7406\u4e0e\u5b9e\u73b0 [ \u8c46\u74e3 ] Operating Systems: Three Easy Pieces [ \u8c46\u74e3 ] Modern Operating Systems [ \u8c46\u74e3 ] Operating Systems: Principles and Practice [ \u8c46\u74e3 ]","title":"\u64cd\u4f5c\u7cfb\u7edf"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_5","text":"Computer Networks: A Systems Approach [ \u8c46\u74e3 ] Computer Networking: A Top-Down Approach [ \u8c46\u74e3 ]","title":"\u8ba1\u7b97\u673a\u7f51\u7edc"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_6","text":"Patterns of Distributed System (Blog) Distributed Systems for Fun and Profit (Blog) Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems [ \u8c46\u74e3 ]","title":"\u5206\u5e03\u5f0f\u7cfb\u7edf"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_7","text":"Architecture of a Database System [ \u8c46\u74e3 ] Readings in Database Systems [ \u8c46\u74e3 ] Database System Concepts [ \u8c46\u74e3 ]","title":"\u6570\u636e\u5e93\u7cfb\u7edf"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_8","text":"Engineering a Compiler [ \u8c46\u74e3 ] Compilers: Principles, Techniques, and Tools [ \u8c46\u74e3 ]","title":"\u7f16\u8bd1\u539f\u7406"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_9","text":"\u8ba1\u7b97\u673a\u7a0b\u5e8f\u7684\u6784\u9020\u548c\u89e3\u91ca [ \u8c46\u74e3 ] Essentials of Programming Languages [ \u8c46\u74e3 ] Practical Foundations for Programming Languages [ \u8c46\u74e3 ] Software Foundations [ \u8c46\u74e3 ] [ \u5317\u5927\u76f8\u5173\u8bfe\u7a0b ] Types and Programming Languages [ \u8c46\u74e3 ] [ \u5317\u5927\u76f8\u5173\u8bfe\u7a0b ]","title":"\u8ba1\u7b97\u673a\u7f16\u7a0b\u8bed\u8a00"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_10","text":"\u8d85\u6807\u91cf\u5904\u7406\u5668\u8bbe\u8ba1: Superscalar RISC Processor Design [ \u8c46\u74e3 ] Computer Organization and Design RISC-V Edition [ \u8c46\u74e3 ] Computer Organization and Design: The Hardware/Software Interface [ \u8c46\u74e3 ] Computer Architecture: A Quantitative Approach [ \u8c46\u74e3 ]","title":"\u4f53\u7cfb\u7ed3\u6784"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_11","text":"Introduction to the Theory of Computation [ \u8c46\u74e3 ]","title":"\u7406\u8bba\u8ba1\u7b97\u673a\u79d1\u5b66"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_12","text":"Cryptography Engineering: Design Principles and Practical Applications [ \u8c46\u74e3 ] Introduction to Modern Cryptography [ \u8c46\u74e3 ]","title":"\u5bc6\u7801\u5b66"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_13","text":"\u9006\u5411\u5de5\u7a0b\u6838\u5fc3\u539f\u7406 [ \u8c46\u74e3 ] \u52a0\u5bc6\u4e0e\u89e3\u5bc6 [ \u8c46\u74e3 ]","title":"\u9006\u5411\u5de5\u7a0b"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_14","text":"Monte Carlo theory, methods and examples Advanced Global Illumination [ \u8c46\u74e3 ] Fundamentals of Computer Graphics [ \u8c46\u74e3 ] Fluid Simulation for Computer Graphics [ \u8c46\u74e3 ] Physically Based Rendering: From Theory To Implementation [ \u8c46\u74e3 ] Real-Time Rendering [ \u8c46\u74e3 ]","title":"\u8ba1\u7b97\u673a\u56fe\u5f62\u5b66"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_15","text":"\u6e38\u620f\u7f16\u7a0b\u6a21\u5f0f: Game Programming Patterns [ \u8c46\u74e3 ] \u5b9e\u65f6\u78b0\u649e\u68c0\u6d4b\u7b97\u6cd5\u6280\u672f [ \u8c46\u74e3 ] Game AI Pro Series [ \u8c46\u74e3 ] Artificial Intelligence for Games [ \u8c46\u74e3 ] Game Engine Architecture [ \u8c46\u74e3 ] Game Programming Gems Series [ \u8c46\u74e3 ]","title":"\u6e38\u620f\u5f15\u64ce"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_16","text":"Software Engineering at Google [ \u8c46\u74e3 ]","title":"\u8f6f\u4ef6\u5de5\u7a0b"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_17","text":"\u8bbe\u8ba1\u6a21\u5f0f: \u53ef\u590d\u7528\u9762\u5411\u5bf9\u8c61\u8f6f\u4ef6\u7684\u57fa\u7840 [ \u8c46\u74e3 ] \u5927\u8bdd\u8bbe\u8ba1\u6a21\u5f0f [ \u8c46\u74e3 ] Head First \u8bbe\u8ba1\u6a21\u5f0f [ \u8c46\u74e3 ]","title":"\u8bbe\u8ba1\u6a21\u5f0f"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_18","text":"\u52a8\u624b\u5b66\u6df1\u5ea6\u5b66\u4e60 [ \u8c46\u74e3 ] \u795e\u7ecf\u7f51\u7edc\u4e0e\u6df1\u5ea6\u5b66\u4e60 [ \u8c46\u74e3 ] \u6df1\u5ea6\u5b66\u4e60\u5165\u95e8 [ \u8c46\u74e3 ] \u7b80\u5355\u7c97\u66b4 TensorFlow 2 (Tutorial) Speech and Language Processing [ \u8c46\u74e3 ]","title":"\u6df1\u5ea6\u5b66\u4e60"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_19","text":"Multiple View Geometry in Computer Vision [ \u8c46\u74e3 ]","title":"\u8ba1\u7b97\u673a\u89c6\u89c9"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_20","text":"Probabilistic Robotics [ \u8c46\u74e3 ]","title":"\u673a\u5668\u4eba"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_21","text":"\u5251\u6307 Offer\uff1a\u540d\u4f01\u9762\u8bd5\u5b98\u7cbe\u8bb2\u5178\u578b\u7f16\u7a0b\u9898 [ \u8c46\u74e3 ] Cracking The Coding Interview [ \u8c46\u74e3 ]","title":"\u9762\u8bd5"},{"location":"en/Web%E5%BC%80%E5%8F%91/CS142/","text":"Stanford CS142: Web Applications Descriptions Offered by: Stanford Prerequisites: CS107 and CS108 Programming Languages: JavaScript/HTML/CSS Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 100 hours This is Stanford's Web Application course covers HTML, CSS, JavaScript, ReactJs, NodeJS, ExpressJS, Web Security, and more. Eight projects will enhance your web development skills in practice. Course Resources Course Website: https://web.stanford.edu/class/cs142/index.html Recordings: https://web.stanford.edu/class/cs142/lectures.html Assignments: https://web.stanford.edu/class/cs142/projects.html","title":"Stanford CS142: Web Applications"},{"location":"en/Web%E5%BC%80%E5%8F%91/CS142/#stanford-cs142-web-applications","text":"","title":"Stanford CS142: Web Applications"},{"location":"en/Web%E5%BC%80%E5%8F%91/CS142/#descriptions","text":"Offered by: Stanford Prerequisites: CS107 and CS108 Programming Languages: JavaScript/HTML/CSS Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 100 hours This is Stanford's Web Application course covers HTML, CSS, JavaScript, ReactJs, NodeJS, ExpressJS, Web Security, and more. Eight projects will enhance your web development skills in practice.","title":"Descriptions"},{"location":"en/Web%E5%BC%80%E5%8F%91/CS142/#course-resources","text":"Course Website: https://web.stanford.edu/class/cs142/index.html Recordings: https://web.stanford.edu/class/cs142/lectures.html Assignments: https://web.stanford.edu/class/cs142/projects.html","title":"Course Resources"},{"location":"en/Web%E5%BC%80%E5%8F%91/fullstackopen/","text":"University of Helsinki: Full Stack open 2022 Descriptions Offered by: University of Helsinki Prerequisites: Good programming skills, basic knowledge of web programming and databases, and have mastery of the Git version management system. Programming Languages: JavaScript/HTML/CSS/NoSQL/SQL Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: Varying according to the learner This course serves as an introduction to modern web application development with JavaScript. 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The main focus is on building single page applications with ReactJS that use REST APIs built with Node.js. The course also contains a section on GraphQL, a modern alternative to REST APIs. The course covers testing, configuration and environment management, and the use of MongoDB for storing the application\u2019s data.","title":"Descriptions"},{"location":"en/Web%E5%BC%80%E5%8F%91/fullstackopen/#resources","text":"Course Website: https://fullstackopen.com/en/ Assignments: refer to the course website Course group on Discord: https://study.cs.helsinki.fi/discord/join/fullstack/ Course group on Telegram: https://t.me/fullstackcourse/","title":"Resources"},{"location":"en/Web%E5%BC%80%E5%8F%91/mitweb/","text":"MIT Web Development Crash Course \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u638c\u63e1\u81f3\u5c11\u4e00\u95e8\u7f16\u7a0b\u8bed\u8a00 \u7f16\u7a0b\u8bed\u8a00\uff1aJavaScript/HTML/CSS/NoSQL \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a\u56e0\u4eba\u800c\u5f02 MIT \u5728\u6bcf\u5e74 1 \u6708\u4efd\u4f1a\u6709\u4e00\u4e2a\u4e3a\u671f 4 \u5468\u7684 Independent Activities 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https://weblab.mit.edu/schedule/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9\u94fe\u63a5 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u53c2\u89c1\u8bfe\u7a0b Schedule","title":"MIT web development course"},{"location":"en/Web%E5%BC%80%E5%8F%91/mitweb/#mit-web-development-crash-course","text":"","title":"MIT Web Development Crash Course"},{"location":"en/Web%E5%BC%80%E5%8F%91/mitweb/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u638c\u63e1\u81f3\u5c11\u4e00\u95e8\u7f16\u7a0b\u8bed\u8a00 \u7f16\u7a0b\u8bed\u8a00\uff1aJavaScript/HTML/CSS/NoSQL \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a\u56e0\u4eba\u800c\u5f02 MIT \u5728\u6bcf\u5e74 1 \u6708\u4efd\u4f1a\u6709\u4e00\u4e2a\u4e3a\u671f 4 \u5468\u7684 Independent Activities Period (IAP)\uff0c\u5728\u8fd9\u4e2a\u6708\u91cc\uff0cMIT 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\u8fd9\u95e8\u8bfe\uff0c\u4f46\u5176\u5b9e CSAPP \u6240\u6709\u7684\u8bfe\u7a0b\u8d44\u6e90\u548c\u5b9e\u9a8c\u4ee3\u7801\u90fd\u80fd\u5728\u5b83\u7684\u5b98\u65b9\u4e3b\u9875\u4e0a\u8bbf\u95ee\u5230\uff08\u5177\u4f53\u53c2\u89c1\u4e0b\u65b9\u94fe\u63a5\uff09\u3002 \u8fd9\u95e8\u8bfe\u7531\u4e8e\u8fc7\u4e8e\u51fa\u540d\uff0c\u5168\u4e16\u754c\u7684\u7801\u519c\u4e89\u76f8\u5b66\u4e60\uff0c\u5bfc\u81f4\u5176 Project \u7684\u7b54\u6848\u5728\u7f51\u4e0a\u51e0\u4e4e\u553e\u624b\u53ef\u5f97\u3002\u4f46\u5982\u679c\u4f60\u771f\u7684\u60f3\u953b\u70bc\u81ea\u5df1\u7684\u4ee3\u7801\u80fd\u529b\uff0c\u5e0c\u671b\u4f60\u4e0d\u8981\u501f\u9274\u4efb\u4f55\u7b2c\u4e09\u65b9\u4ee3\u7801\u3002 \u8ba4\u771f\u5b66\u5b8c\u8fd9\u4e00\u95e8\u8bfe\uff0c\u4f60\u5bf9\u8ba1\u7b97\u673a\u7cfb\u7edf\u7684\u7406\u89e3\u7edd\u5bf9\u4f1a\u4e0a\u5347\u4e00\u4e2a\u53f0\u9636\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://csapp.cs.cmu.edu/ \u8bfe\u7a0b\u89c6\u9891\uff1a 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Edition) \u8bfe\u7a0b\u5b9e\u9a8c\uff1a9 \u4e2a\u5b9e\u9a8c\u4ece\u96f6\u5f00\u59cb\u8bbe\u8ba1 MIPS CPU\uff0c\u8be6\u89c1\u8bfe\u7a0b\u7f51\u7ad9","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E4%BD%93%E7%B3%BB%E7%BB%93%E6%9E%84/N2T/","text":"Coursera: Nand2Tetris Descriptions Offered by: Hebrew University of Jerusalem Prerequisites: None Programming Languages: Chosen by the course taker Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 40 hours As one of the most popular courses on Coursera , tens of thousands of people give it a full score, and over four hundred colleges and high schools teach it. It guides the students who may have no preparatory knowledge in computer science to build a whole computer from Nand logic gates and finally run the Tetris game on it. Sounds cool, right? It's even cooler when you implement it! The course is divided into hardware modules and software modules respectively. In the hardware modules, you will dive into a world based on 0 and 1, create various logic gates from Nand gates, and construct a CPU step by step to run a simplified instruction set designed by the course instructors. In the software modules, you will first write a compiler to compile a high-level language Jack which is designed by the instructors into byte codes that can run on virtual machines. Then you will further translate the byte codes into assembly language that can run on the CPU you create in the hardware modules. You will also develop a simple operating system that enables your computer to support GUI. Finally, you can use Jack to create the Tetris game, compile it into assembly language, run it on your self-made CPU, and interact with it through the OS built by yourself. After taking this course, you will have a comprehensive and profound understanding of the entire computer architecture, which might be extremely helpful to your subsequent learning. You may think that the course is too difficult. Don't worry, because it is completely designed for laymen. In the instructors' expectations, even high school students can understand the content. So as long as you keep pace with the syllabus, you can finish it within a month. This course extracts the essence of computers while omitting the tedious and complex details in modern computer systems that are designed for efficiency and performance. Surely you will enjoy the elegance and magic of computers in a relaxing and jolly journey. Course Resources Course Website\uff1a Nand2Tetris I , Nand2Tetris II Recordings\uff1aRefer to course website Textbook: The Elements of Computing Systems: Building a Modern Computer from First Principles (CN-zh version) Assignments\uff1a10 projects to construct a computer, refer to the course website for more details Personal Resources All the resources and assignments used by @PKUFlyingPig are maintained in PKUFlyingPig/NandToTetris - GitHub .","title":"Coursera: Nand2Tetris"},{"location":"en/%E4%BD%93%E7%B3%BB%E7%BB%93%E6%9E%84/N2T/#coursera-nand2tetris","text":"","title":"Coursera: Nand2Tetris"},{"location":"en/%E4%BD%93%E7%B3%BB%E7%BB%93%E6%9E%84/N2T/#descriptions","text":"Offered by: Hebrew University of Jerusalem Prerequisites: None Programming Languages: Chosen by the course taker Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 40 hours As one of the most popular courses on Coursera , tens of thousands of people give it a full score, and over four hundred colleges and high schools teach it. It guides the students who may have no preparatory knowledge in computer science to build a whole computer from Nand logic gates and finally run the Tetris game on it. Sounds cool, right? It's even cooler when you implement it! The course is divided into hardware modules and software modules respectively. In the hardware modules, you will dive into a world based on 0 and 1, create various logic gates from Nand gates, and construct a CPU step by step to run a simplified instruction set designed by the course instructors. In the software modules, you will first write a compiler to compile a high-level language Jack which is designed by the instructors into byte codes that can run on virtual machines. Then you will further translate the byte codes into assembly language that can run on the CPU you create in the hardware modules. You will also develop a simple operating system that enables your computer to support GUI. Finally, you can use Jack to create the Tetris game, compile it into assembly language, run it on your self-made CPU, and interact with it through the OS built by yourself. After taking this course, you will have a comprehensive and profound understanding of the entire computer architecture, which might be extremely helpful to your subsequent learning. You may think that the course is too difficult. Don't worry, because it is completely designed for laymen. In the instructors' expectations, even high school students can understand the content. So as long as you keep pace with the syllabus, you can finish it within a month. This course extracts the essence of computers while omitting the tedious and complex details in modern computer systems that are designed for efficiency and performance. Surely you will enjoy the elegance and magic of computers in a relaxing and jolly journey.","title":"Descriptions"},{"location":"en/%E4%BD%93%E7%B3%BB%E7%BB%93%E6%9E%84/N2T/#course-resources","text":"Course Website\uff1a Nand2Tetris I , Nand2Tetris II Recordings\uff1aRefer to course website Textbook: The Elements of Computing Systems: Building a Modern Computer from First Principles (CN-zh version) Assignments\uff1a10 projects to construct a computer, refer to the course website for more details","title":"Course Resources"},{"location":"en/%E4%BD%93%E7%B3%BB%E7%BB%93%E6%9E%84/N2T/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig are maintained in PKUFlyingPig/NandToTetris - GitHub .","title":"Personal Resources"},{"location":"en/%E5%B9%B6%E8%A1%8C%E4%B8%8E%E5%88%86%E5%B8%83%E5%BC%8F%E7%B3%BB%E7%BB%9F/CS149/","text":"CMU 15-418/Stanford CS149: Parallel Computing \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aCMU \u548c Stanford \u5148\u4fee\u8981\u6c42\uff1a\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784\uff0c\u719f\u6089 C++ \u7f16\u7a0b\u8bed\u8a00\uff1aC++ \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 Kayvon Fatahalian \u6559\u6388\u6b64\u524d\u5728 CMU \u5f00\u4e86 15-418 \u8fd9\u95e8\u8bfe\uff0c\u540e\u6765\u4ed6\u6210\u4e3a Stanford \u7684\u52a9\u7406\u6559\u6388\u540e\u53c8\u5f00\u4e86\u7c7b\u4f3c\u7684\u8bfe\u7a0b CS149\u3002\u4f46\u603b\u4f53\u6765\u8bf4\uff0c15-418 \u5305\u542b\u7684\u8bfe\u7a0b\u5185\u5bb9\u66f4\u4e30\u5bcc\uff0c\u5e76\u4e14\u6709\u8bfe\u7a0b\u56de\u653e\uff0c\u4f46 CS149 \u7684\u7f16\u7a0b\u4f5c\u4e1a\u66f4 fashion \u4e00\u4e9b\u3002\u6211\u4e2a\u4eba\u662f\u89c2\u770b\u7684 15-418 \u7684\u8bfe\u7a0b\u5f55\u5f71\u4f46\u5b8c\u6210\u7684 CS149 \u7684\u4f5c\u4e1a\u3002 \u8fd9\u95e8\u8bfe\u4f1a\u5e26\u4f60\u6df1\u5165\u7406\u89e3\u73b0\u4ee3\u5e76\u884c\u8ba1\u7b97\u67b6\u6784\u7684\u8bbe\u8ba1\u539f\u5219\u4e0e\u5fc5\u8981\u6743\u8861\uff0c\u5e76\u5b66\u4f1a\u5982\u4f55\u5145\u5206\u5229\u7528\u786c\u4ef6\u8d44\u6e90\u4ee5\u53ca\u8f6f\u4ef6\u7f16\u7a0b\u6846\u67b6\uff08\u4f8b\u5982 CUDA\uff0cMPI\uff0cOpenMP \u7b49\uff09\u7f16\u5199\u9ad8\u6027\u80fd\u7684\u5e76\u884c\u7a0b\u5e8f\u3002\u7531\u4e8e\u5e76\u884c\u8ba1\u7b97\u67b6\u6784\u7684\u590d\u6742\u6027\uff0c\u8fd9\u95e8\u8bfe\u4f1a\u6d89\u53ca\u8bf8\u591a\u9ad8\u7ea7\u4f53\u7cfb\u7ed3\u6784\u4e0e\u7f51\u7edc\u901a\u4fe1\u7684\u5185\u5bb9\uff0c\u77e5\u8bc6\u70b9\u76f8\u5f53\u5e95\u5c42\u4e14\u786c\u6838\u3002\u4e0e\u6b64\u540c\u65f6\uff0c5 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a\u5219\u662f\u4ece\u8f6f\u4ef6\u7684\u5c42\u9762\u57f9\u517b\u5b66\u751f\u5bf9\u4e0a\u5c42\u62bd\u8c61\u7684\u7406\u89e3\u4e0e\u8fd0\u7528\uff0c\u5177\u4f53\u4f1a\u8ba9\u4f60\u5206\u6790\u5e76\u884c\u7a0b\u5e8f\u7684\u74f6\u9888\u3001\u7f16\u5199\u591a\u7ebf\u7a0b\u540c\u6b65\u4ee3\u7801\u3001\u5b66\u4e60 CUDA \u7f16\u7a0b\u3001OpenMP \u7f16\u7a0b\u4ee5\u53ca\u524d\u6bb5\u65f6\u95f4\u5927\u70ed\u7684 Spark \u6846\u67b6\u7b49\u7b49\u3002\u771f\u6b63\u610f\u4e49\u4e0a\u5c06\u7406\u8bba\u4e0e\u5b9e\u8df5\u5b8c\u7f8e\u5730\u7ed3\u5408\u5728\u4e86\u4e00\u8d77\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a CMU15418 , CS149 \u8bfe\u7a0b\u89c6\u9891\uff1a http://15418.courses.cs.cmu.edu/spring2016/lectures \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://gfxcourses.stanford.edu/cs149/fall21 \uff0c5 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS149-parallel-computing - 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It uses CMakeLists.txt to define build configuration, and have more functionalities compared to GNU make. It is highly recommanded to learn GNU Make and get familiar with Makefile first before learning CMake.","title":"Why CMake"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/CMake/#how-to-learn-cmake","text":"Compare to Makefile , CMakeLists.txt is more obscure and difficult to understand and use. Nowadays many IDEs (e.g., Visual Studio, CLion) offer functionalities to generate CMakeLists.txt automaticly, but it's still necessary to manage basic usage of CMakeLists.txt . Besides Official CMake Tutorial , this one-hour video tutorial (in Chinese) presented by IPADS group at SJTU is also a good learning resource.","title":"How to learn CMake"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Docker/","text":"Docker \u4e3a\u4ec0\u4e48\u4f7f\u7528 Docker \u4f7f\u7528\u522b\u4eba\u5199\u597d\u7684\u8f6f\u4ef6/\u5de5\u5177\u6700\u5927\u7684\u969c\u788d\u662f\u4ec0\u4e48\u2014\u2014\u5fc5\u7136\u662f\u914d\u73af\u5883\u3002\u914d\u73af\u5883\u5e26\u6765\u7684\u6298\u78e8\u4f1a\u6781\u5927\u5730\u6d88\u89e3\u4f60\u5bf9\u8f6f\u4ef6\u3001\u7f16\u7a0b\u672c\u8eab\u7684\u5174\u8da3\u3002\u865a\u62df\u673a\u53ef\u4ee5\u89e3\u51b3\u914d\u73af\u5883\u7684\u4e00\u90e8\u5206\u95ee\u9898\uff0c\u4f46\u5b83\u5e9e\u5927\u7b28\u91cd\uff0c\u4e14\u4e3a\u4e86\u67d0\u4e2a\u5e94\u7528\u7684\u73af\u5883\u914d\u7f6e\u597d\u50cf\u4e5f\u4e0d\u503c\u5f97\u6a21\u62df\u4e00\u4e2a\u5168\u65b0\u7684\u64cd\u4f5c\u7cfb\u7edf\u3002 Docker 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The father of Linux, Linus Torvalds developed Git to maintain the version control of Linux, replacing the centralized version control tools which were difficult and costly to use. The design of Git is very elegant, but beginners usually find it very difficult to use without understanding its internal logic. It is very easy to mess up the version history if misusing the commands. Git is a powerful tool and when you finally master it, you will find all the effort paid off. How to learn Git Different from Vim, I don't suggest beginners use Git rashly without fully understanding it, because its inner logic can not be acquainted by practicing. Here is my recommended learning path: Read this Git tutorial in English, or you can watch this Git tutorial (by \u5c1a\u7845\u8c37) in Chinese. Read Chap1 - Chap5 of this open source book Pro Git . Yes, to learn Git, you need to read a book. Now that you have understood its principles and most of its usages, it's time to consolidate those commands by practicing. How to use Git properly is a kind of philosophy. I recommend reading this blog How to Write a Git Commit Message . You are now in love with Git and are not content with only using it, you want to build a Git by yourself! Great, that's exactly what I was thinking. This tutorial will satisfy you! What? Building your own Git is not enough? Seems that you are also passionate about reinventing the wheels. These two GitHub projects, build-your-own-x and project-based-learning , collected many wheel-reinventing tutorials, e.g., text editor, virtual machine, docker, TCP and so on.","title":"Git"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Git/#git","text":"","title":"Git"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Git/#why-git","text":"Git is a distributed version control system. The father of Linux, Linus Torvalds developed Git to maintain the version control of Linux, replacing the centralized version control tools which were difficult and costly to use. The design of Git is very elegant, but beginners usually find it very difficult to use without understanding its internal logic. It is very easy to mess up the version history if misusing the commands. Git is a powerful tool and when you finally master it, you will find all the effort paid off.","title":"Why Git"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Git/#how-to-learn-git","text":"Different from Vim, I don't suggest beginners use Git rashly without fully understanding it, because its inner logic can not be acquainted by practicing. Here is my recommended learning path: Read this Git tutorial in English, or you can watch this Git tutorial (by \u5c1a\u7845\u8c37) in Chinese. Read Chap1 - Chap5 of this open source book Pro Git . Yes, to learn Git, you need to read a book. Now that you have understood its principles and most of its usages, it's time to consolidate those commands by practicing. How to use Git properly is a kind of philosophy. I recommend reading this blog How to Write a Git Commit Message . You are now in love with Git and are not content with only using it, you want to build a Git by yourself! Great, that's exactly what I was thinking. This tutorial will satisfy you! What? Building your own Git is not enough? Seems that you are also passionate about reinventing the wheels. These two GitHub projects, build-your-own-x and project-based-learning , collected many wheel-reinventing tutorials, e.g., text editor, virtual machine, docker, TCP and so on.","title":"How to learn Git"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/GitHub/","text":"GitHub GitHub \u662f\u4ec0\u4e48 \u4ece\u529f\u80fd\u4e0a\u6765\u8bf4\uff0cGitHub \u662f\u4e00\u4e2a\u5728\u7ebf\u4ee3\u7801\u6258\u7ba1\u5e73\u53f0\u3002\u4f60\u53ef\u4ee5\u5c06\u4f60\u7684\u672c\u5730 Git \u4ed3\u5e93\u6258\u7ba1\u5230 GitHub \u4e0a\uff0c\u4f9b\u591a\u4eba\u540c\u65f6\u5f00\u53d1\u6d4f\u89c8\u3002\u4f46\u73b0\u5982\u4eca GitHub \u7684\u610f\u4e49\u5df2\u8fdc\u4e0d\u6b62\u5982\u6b64\uff0c\u5b83\u5df2\u7ecf\u6f14\u53d8\u4e3a\u4e00\u4e2a\u975e\u5e38\u6d3b\u8dc3\u4e14\u8d44\u6e90\u6781\u4e3a\u4e30\u5bcc\u7684\u5f00\u6e90\u4ea4\u6d41\u793e\u533a\u3002\u5168\u4e16\u754c\u7684\u8f6f\u4ef6\u5f00\u53d1\u8005\u5728 GitHub 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\u5373\u53ef\u8fdb\u5165\u7ec3\u4e60\u7a0b\u5e8f\u3002 \u6700\u540e\u5c31\u662f\u5f3a\u8feb\u81ea\u5df1\u4f7f\u7528 Vim \u8fdb\u884c\u5f00\u53d1\uff0cIDE \u91cc\u53ef\u4ee5\u5b89\u88c5 Vim \u63d2\u4ef6\u3002 \u7b49\u4f60\u5b8c\u5168\u9002\u5e94 Vim \u4e4b\u540e\u65b0\u7684\u4e16\u754c\u4fbf\u5411\u4f60\u655e\u5f00\u4e86\u5927\u95e8\uff0c\u4f60\u53ef\u4ee5\u6309\u9700\u914d\u7f6e\u81ea\u5df1\u7684 Vim\uff08\u4fee\u6539 .vimrc \u6587\u4ef6\uff09\uff0c\u7f51\u4e0a\u6709\u6570\u4e0d\u80dc\u6570\u7684\u8d44\u6e90\u53ef\u4ee5\u501f\u9274\u3002 \u63a8\u8350\u53c2\u8003\u8d44\u6599 Neil, Drew. Practical Vim: Edit Text at the Speed of Thought. N.p., Pragmatic Bookshelf, 2015. Neil, Drew. Modern Vim: Craft Your Development Environment with Vim 8 and Neovim. United States, Pragmatic Bookshelf.","title":"Vim"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Vim/#vim","text":"","title":"Vim"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Vim/#vim_1","text":"\u5728\u6211\u770b\u6765 Vim \u7f16\u8f91\u5668\u6709\u5982\u4e0b\u7684\u597d\u5904\uff1a \u8ba9\u4f60\u7684\u6574\u4e2a\u5f00\u53d1\u8fc7\u7a0b\u624b\u6307\u4e0d\u9700\u8981\u79bb\u5f00\u952e\u76d8\uff0c\u800c\u4e14\u5149\u6807\u7684\u79fb\u52a8\u4e0d\u9700\u8981\u65b9\u5411\u952e\u4f7f\u5f97\u4f60\u7684\u624b\u6307\u4e00\u76f4\u5904\u5728\u6253\u5b57\u7684\u6700\u4f73\u4f4d\u7f6e\u3002 \u65b9\u4fbf\u7684\u6587\u4ef6\u5207\u6362\u4ee5\u53ca\u9762\u677f\u63a7\u5236\u53ef\u4ee5\u8ba9\u4f60\u540c\u65f6\u5f00\u53d1\u591a\u4efd\u6587\u4ef6\u751a\u81f3\u540c\u4e00\u4e2a\u6587\u4ef6\u7684\u4e0d\u540c\u4f4d\u7f6e\u3002 Vim \u7684\u5b8f\u64cd\u4f5c\u53ef\u4ee5\u6279\u91cf\u5316\u5904\u7406\u91cd\u590d\u64cd\u4f5c\uff08\u4f8b\u5982\u591a\u884c tab\uff0c\u6279\u91cf\u52a0\u53cc\u5f15\u53f7\u7b49\u7b49\uff09 Vim \u662f\u5f88\u591a\u670d\u52a1\u5668\u81ea\u5e26\u7684\u547d\u4ee4\u884c\u7f16\u8f91\u5668\uff0c\u5f53\u4f60\u901a\u8fc7 ssh \u8fde\u63a5\u8fdc\u7a0b\u670d\u52a1\u5668\u4e4b\u540e\uff0c\u7531\u4e8e\u6ca1\u6709\u56fe\u5f62\u754c\u9762\uff0c\u53ea\u80fd\u5728\u547d\u4ee4\u884c\u91cc\u8fdb\u884c\u5f00\u53d1\uff08\u5f53\u7136\u73b0\u5728\u5f88\u591a IDE \u5982 VS Code \u63d0\u4f9b\u4e86 ssh \u63d2\u4ef6\u53ef\u4ee5\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\uff09\u3002 \u5f02\u5e38\u4e30\u5bcc\u7684\u63d2\u4ef6\u751f\u6001\uff0c\u8ba9\u4f60\u62e5\u6709\u4e16\u754c\u4e0a\u6700\u82b1\u91cc\u80e1\u54e8\u7684\u547d\u4ee4\u884c\u7f16\u8f91\u5668\u3002","title":"\u4e3a\u4ec0\u4e48\u5b66\u4e60 Vim"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Vim/#vim_2","text":"\u4e0d\u5e78\u7684\u662f Vim \u7684\u5b66\u4e60\u66f2\u7ebf\u786e\u5b9e\u76f8\u5f53\u9661\u5ced\uff0c\u6211\u82b1\u4e86\u597d\u51e0\u4e2a\u661f\u671f\u624d\u6162\u6162\u9002\u5e94\u4e86\u7528 Vim \u8fdb\u884c\u5f00\u53d1\u7684\u8fc7\u7a0b\u3002\u6700\u5f00\u59cb\u4f60\u4f1a\u89c9\u5f97\u975e\u5e38\u4e0d\u9002\u5e94\uff0c\u4f46\u4e00\u65e6\u71ac\u8fc7\u4e86\u521d\u59cb\u9636\u6bb5\uff0c\u76f8\u4fe1\u6211\uff0c\u4f60\u4f1a\u7231\u4e0a Vim\u3002 Vim \u7684\u5b66\u4e60\u8d44\u6599\u6d69\u5982\u70df\u6d77\uff0c\u4f46\u638c\u63e1\u5b83\u6700\u597d\u7684\u65b9\u5f0f\u8fd8\u662f\u5c06\u5b83\u7528\u5728\u65e5\u5e38\u7684\u5f00\u53d1\u8fc7\u7a0b\u4e2d\uff0c\u800c\u4e0d\u662f\u4e00\u4e0a\u6765\u5c31\u53bb\u5b66\u5404\u79cd\u82b1\u91cc\u80e1\u54e8\u7684\u9ad8\u7ea7 Vim \u6280\u5de7\u3002\u4e2a\u4eba\u63a8\u8350\u7684\u5b66\u4e60\u8def\u7ebf\u5982\u4e0b\uff1a \u5148\u9605\u8bfb \u8fd9\u7bc7 tutorial \uff0c\u638c\u63e1\u57fa\u672c\u7684 Vim \u6982\u5ff5\u548c\u4f7f\u7528\u65b9\u5f0f\u3002 \u7528 Vim \u81ea\u5e26\u7684 vimtutor \u8fdb\u884c\u7ec3\u4e60\uff0c\u5b89\u88c5\u5b8c Vim \u4e4b\u540e\u76f4\u63a5\u5728\u547d\u4ee4\u884c\u91cc\u8f93\u5165 vimtutor \u5373\u53ef\u8fdb\u5165\u7ec3\u4e60\u7a0b\u5e8f\u3002 \u6700\u540e\u5c31\u662f\u5f3a\u8feb\u81ea\u5df1\u4f7f\u7528 Vim \u8fdb\u884c\u5f00\u53d1\uff0cIDE \u91cc\u53ef\u4ee5\u5b89\u88c5 Vim \u63d2\u4ef6\u3002 \u7b49\u4f60\u5b8c\u5168\u9002\u5e94 Vim \u4e4b\u540e\u65b0\u7684\u4e16\u754c\u4fbf\u5411\u4f60\u655e\u5f00\u4e86\u5927\u95e8\uff0c\u4f60\u53ef\u4ee5\u6309\u9700\u914d\u7f6e\u81ea\u5df1\u7684 Vim\uff08\u4fee\u6539 .vimrc \u6587\u4ef6\uff09\uff0c\u7f51\u4e0a\u6709\u6570\u4e0d\u80dc\u6570\u7684\u8d44\u6e90\u53ef\u4ee5\u501f\u9274\u3002","title":"\u5982\u4f55\u5b66\u4e60 Vim"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Vim/#_1","text":"Neil, Drew. Practical Vim: Edit Text at the Speed of Thought. N.p., Pragmatic Bookshelf, 2015. Neil, Drew. Modern Vim: Craft Your Development Environment with Vim 8 and Neovim. United States, Pragmatic Bookshelf.","title":"\u63a8\u8350\u53c2\u8003\u8d44\u6599"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/thesis/","text":"\u6bd5\u4e1a\u8bba\u6587 \u4e3a\u4ec0\u4e48\u5199\u8fd9\u4efd\u6559\u7a0b 2022\u5e74\uff0c\u6211\u672c\u79d1\u6bd5\u4e1a\u4e86\u3002\u5728\u5f00\u59cb\u52a8\u624b\u5199\u6bd5\u4e1a\u8bba\u6587\u7684\u65f6\u5019\uff0c\u6211\u5c34\u5c2c\u5730\u53d1\u73b0\uff0c\u6211\u5bf9 Word \u7684\u638c\u63e1\u7a0b\u5ea6\u4ec5\u9650\u4e8e\u8c03\u8282\u5b57\u4f53\u3001\u4fdd\u5b58\u5bfc\u51fa\u8fd9\u4e9b\u50bb\u74dc\u529f\u80fd\u3002\u66fe\u60f3\u8f6c\u6218 Latex\uff0c\u4f46\u8bba\u6587\u7684\u6bb5\u843d\u683c\u5f0f\u8981\u6c42\u8c03\u6574\u8d77\u6765\u8fd8\u662f\u7528 Word 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\u786e\u5b9a\u8bba\u6587\u7684\u683c\u5f0f\u8981\u6c42\uff1a\u901a\u5e38\u5b66\u9662\u90fd\u4f1a\u4e0b\u53d1\u6bd5\u4e1a\u8bba\u6587\u7684\u683c\u5f0f\u8981\u6c42\uff08\u5404\u7ea7\u6807\u9898\u7684\u5b57\u4f53\u5b57\u53f7\u3001\u56fe\u4f8b\u548c\u5f15\u7528\u7684\u683c\u5f0f\u7b49\u7b49\uff09\uff0c\u5982\u679c\u66f4\u4e3a\u8d34\u5fc3\u7684\u8bdd\u751a\u81f3\u4f1a\u76f4\u63a5\u7ed9\u51fa\u8bba\u6587\u6a21\u7248\uff08\u5982\u662f\u6b64\u60c5\u51b5\u8bf7\u76f4\u63a5\u8df3\u8f6c\u5230\u4e0b\u4e00\u6b65\uff09\u3002\u5f88\u4e0d\u5e78\u7684\u662f\uff0c\u6211\u7684\u5b66\u9662\u5e76\u6ca1\u6709\u4e0b\u53d1\u6807\u51c6\u7684\u8bba\u6587\u683c\u5f0f\u8981\u6c42\uff0c\u8fd8\u63d0\u4f9b\u4e86\u4e00\u4efd\u683c\u5f0f\u6df7\u4e71\u51e0\u4e4e\u6beb\u65e0\u7528\u5904\u7684\u8bba\u6587\u6a21\u7248\u8188\u5e94\u6211\uff0c\u88ab\u903c\u65e0\u5948\u4e4b\u4e0b\u6211\u627e\u5230\u4e86\u5317\u4eac\u5927\u5b66\u7814\u7a76\u751f\u7684 \u8bba\u6587\u683c\u5f0f\u8981\u6c42 \uff0c\u5e76\u6309\u7167\u5176\u8981\u6c42\u5236\u4f5c\u4e86 \u4e00\u4efd\u6a21\u7248 \uff0c\u5927\u5bb6\u9700\u8981\u7684\u8bdd\u81ea\u53d6\uff0c\u672c\u4eba\u4e0d\u627f\u62c5\u65e0\u6cd5\u6bd5\u4e1a\u7b49\u4efb\u4f55\u8d23\u4efb\u3002 \u5b66\u4e60 Word \u6392\u7248\uff1a\u5230\u8fbe\u8fd9\u4e00\u6b65\u7684\u7ae5\u978b\u5206\u4e3a\u4e24\u7c7b\uff0c\u4e00\u662f\u5df2\u7ecf\u62e5\u6709\u4e86\u5b66\u9662\u63d0\u4f9b\u7684\u6807\u51c6\u6a21\u7248\uff0c\u4e8c\u662f\u53ea\u6709\u4e00\u4efd\u865a\u65e0\u7f25\u7f08\u7684\u683c\u5f0f\u8981\u6c42\u3002\u90a3\u73b0\u5728\u5f53\u52a1\u4e4b\u6025\u5c31\u662f\u5b66\u4e60\u57fa\u7840\u7684 Word \u6392\u7248\u6280\u672f\uff0c\u5bf9\u4e8e\u524d\u8005\u53ef\u4ee5\u5b66\u4f1a\u4f7f\u7528\u6a21\u7248\uff0c\u5bf9\u4e8e\u540e\u8005\u5219\u53ef\u4ee5\u5b66\u4f1a\u5236\u4f5c\u6a21\u7248\u3002\u6b64\u65f6\u5207\u8bb0\u4e0d\u8981\u96c4\u5fc3\u52c3\u52c3\u5730\u9009\u62e9\u4e00\u4e2a\u5341\u51e0\u4e2a\u5c0f\u65f6\u7684 Word \u6559\u5b66\u89c6\u9891\u5f00\u59cb\u5934\u60ac\u6881\u9525\u523a\u80a1\uff0c\u56e0\u4e3a\u751f\u4ea7\u4e00\u4efd\u5e94\u4ed8\u6bd5\u4e1a\u7684\u5b66\u672f\u5783\u573e\u53ea\u8981\u5b66\u534a\u5c0f\u65f6\u80fd\u4e0a\u624b\u5c31\u591f\u4e86\u3002\u6211\u5f53\u65f6\u770b\u7684 \u4e00\u4e2a B \u7ad9\u7684\u6559\u5b66\u89c6\u9891 \uff0c\u77ed\u5c0f\u7cbe\u608d\u975e\u5e38\u5b9e\u7528\uff0c\u5168\u957f\u534a\u5c0f\u65f6\u6781\u901f\u5165\u95e8\u3002 \u751f\u4ea7\u5b66\u672f\u5783\u573e\uff1a\u6700\u5bb9\u6613\u7684\u4e00\u6b65\uff0c\u5927\u5bb6\u516b\u4ed9\u8fc7\u6d77\uff0c\u5404\u663e\u795e\u901a\u5427\uff0c\u795d\u5927\u5bb6\u6bd5\u4e1a\u987a\u5229\uff5e\uff5e","title":"\u5982\u4f55\u7528 Word \u5199\u6bd5\u4e1a\u8bba\u6587"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/","text":"\u5b9e\u7528\u5de5\u5177\u7bb1 \u4e0b\u8f7d\u5de5\u5177 Libgen : PDF\u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9\u3002 z-epub : ePub\u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9\u3002 bitdownloader : \u6cb9\u7ba1\u89c6\u9891\u4e0b\u8f7d\u5668\u3002 zlibrary : \u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9(\u53ef\u80fd\u9700\u8981\u7ffb\u5899)\u3002 \u8bbe\u8ba1\u5de5\u5177 excalidraw : \u4e00\u6b3e\u624b\u7ed8\u98ce\u683c\u7684\u7ed8\u56fe\u5de5\u5177\uff0c\u975e\u5e38\u9002\u5408\u7ed8\u5236\u8bfe\u7a0b\u62a5\u544a\u6216\u8005PPT\u5185\u7684\u793a\u610f\u56fe\u3002 origamiway : \u624b\u628a\u624b\u6559\u4f60\u600e\u4e48\u6298\u7eb8\u3002 thingiverse : \u56ca\u62ec\u5404\u7c7b 2D/3D \u8bbe\u8ba1\u8d44\u6e90\uff0c\u5176 STL \u6587\u4ef6\u4e0b\u8f7d\u53ef\u76f4\u63a5 3D \u6253\u5370\u3002 iconfont : \u56fd\u5185\u6700\u5927\u7684\u56fe\u6807\u548c\u63d2\u753b\u8d44\u6e90\u5e93\uff0c\u53ef\u7528\u4e8e\u5f00\u53d1\u6216\u7ed8\u5236\u7cfb\u7edf\u67b6\u6784\u56fe\u3002 turbosquid : \u53ef\u4ee5\u8d2d\u4e70\u5404\u5f0f\u5404\u6837\u7684\u6a21\u578b\u3002 \u7f16\u7a0b\u76f8\u5173 sqlfiddle : \u4e00\u4e2a\u7b80\u6613\u7684\u5728\u7ebf SQL Playground\u3002 godbolt : \u975e\u5e38\u65b9\u4fbf\u7684\u7f16\u8bd1\u5668\u63a2\u7d22\u5de5\u5177\u3002\u4f60\u53ef\u4ee5\u5199\u4e00\u6bb5 C/C++ \u4ee3\u7801\uff0c\u9009\u62e9\u4e00\u6b3e\u7f16\u8bd1\u5668\uff0c\u7136\u540e\u4fbf\u53ef\u4ee5\u89c2\u5bdf\u751f\u6210\u7684\u5177\u4f53\u6c47\u7f16\u4ee3\u7801\u3002 explainshell : \u4f60\u662f\u5426\u66fe\u4e3a\u4e00\u6bb5 shell \u4ee3\u7801\u7684\u5177\u4f53\u542b\u4e49\u611f\u5230\u56f0\u6270\uff1fmanpage \u770b\u534a\u5929\u8fd8\u662f\u4e0d\u660e\u6240\u4ee5\uff1f\u8bd5\u8bd5\u8fd9\u4e2a\u7f51\u7ad9\uff01 regex101 : \u6b63\u5219\u8868\u8fbe\u5f0f\u8c03\u8bd5\u7f51\u7ad9\uff0c\u652f\u6301\u5404\u79cd\u7f16\u7a0b\u8bed\u8a00\u7684\u5339\u914d\u6807\u51c6\u3002 typingtom : \u9488\u5bf9\u7a0b\u5e8f\u5458\u7684\u6253\u5b57\u7ec3\u4e60/\u6d4b\u901f\u7f51\u7ad9\u3002 \u5b66\u4e60\u7f51\u7ad9 HFS : \u5404\u7c7b\u8f6f\u4ef6\u6559\u7a0b\u3002 os-wiki : \u64cd\u4f5c\u7cfb\u7edf\u6280\u672f\u8d44\u6e90\u767e\u79d1\u5168\u4e66\u3002 Shadertoy : \u7f16\u5199\u5404\u5f0f\u5404\u6837\u7684 shader\u3002 \u6742\u9879 tophub : \u65b0\u95fb\u70ed\u699c\u5408\u96c6\uff08\u7efc\u5408\u4e86\u77e5\u4e4e\u3001\u5fae\u535a\u3001\u767e\u5ea6\u3001\u5fae\u4fe1\u7b49\uff09\u3002 speedtest : \u5728\u7ebf\u7f51\u7edc\u6d4b\u901f\u7f51\u7ad9\u3002 public-apis : \u516c\u5171 API \u5408\u96c6\u5217\u8868\u3002","title":"\u5b9e\u7528\u5de5\u5177\u7bb1"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_1","text":"","title":"\u5b9e\u7528\u5de5\u5177\u7bb1"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_2","text":"Libgen : PDF\u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9\u3002 z-epub : ePub\u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9\u3002 bitdownloader : \u6cb9\u7ba1\u89c6\u9891\u4e0b\u8f7d\u5668\u3002 zlibrary : \u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9(\u53ef\u80fd\u9700\u8981\u7ffb\u5899)\u3002","title":"\u4e0b\u8f7d\u5de5\u5177"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_3","text":"excalidraw : \u4e00\u6b3e\u624b\u7ed8\u98ce\u683c\u7684\u7ed8\u56fe\u5de5\u5177\uff0c\u975e\u5e38\u9002\u5408\u7ed8\u5236\u8bfe\u7a0b\u62a5\u544a\u6216\u8005PPT\u5185\u7684\u793a\u610f\u56fe\u3002 origamiway : \u624b\u628a\u624b\u6559\u4f60\u600e\u4e48\u6298\u7eb8\u3002 thingiverse : \u56ca\u62ec\u5404\u7c7b 2D/3D \u8bbe\u8ba1\u8d44\u6e90\uff0c\u5176 STL \u6587\u4ef6\u4e0b\u8f7d\u53ef\u76f4\u63a5 3D \u6253\u5370\u3002 iconfont : \u56fd\u5185\u6700\u5927\u7684\u56fe\u6807\u548c\u63d2\u753b\u8d44\u6e90\u5e93\uff0c\u53ef\u7528\u4e8e\u5f00\u53d1\u6216\u7ed8\u5236\u7cfb\u7edf\u67b6\u6784\u56fe\u3002 turbosquid : \u53ef\u4ee5\u8d2d\u4e70\u5404\u5f0f\u5404\u6837\u7684\u6a21\u578b\u3002","title":"\u8bbe\u8ba1\u5de5\u5177"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_4","text":"sqlfiddle : \u4e00\u4e2a\u7b80\u6613\u7684\u5728\u7ebf SQL Playground\u3002 godbolt : \u975e\u5e38\u65b9\u4fbf\u7684\u7f16\u8bd1\u5668\u63a2\u7d22\u5de5\u5177\u3002\u4f60\u53ef\u4ee5\u5199\u4e00\u6bb5 C/C++ \u4ee3\u7801\uff0c\u9009\u62e9\u4e00\u6b3e\u7f16\u8bd1\u5668\uff0c\u7136\u540e\u4fbf\u53ef\u4ee5\u89c2\u5bdf\u751f\u6210\u7684\u5177\u4f53\u6c47\u7f16\u4ee3\u7801\u3002 explainshell : \u4f60\u662f\u5426\u66fe\u4e3a\u4e00\u6bb5 shell \u4ee3\u7801\u7684\u5177\u4f53\u542b\u4e49\u611f\u5230\u56f0\u6270\uff1fmanpage \u770b\u534a\u5929\u8fd8\u662f\u4e0d\u660e\u6240\u4ee5\uff1f\u8bd5\u8bd5\u8fd9\u4e2a\u7f51\u7ad9\uff01 regex101 : \u6b63\u5219\u8868\u8fbe\u5f0f\u8c03\u8bd5\u7f51\u7ad9\uff0c\u652f\u6301\u5404\u79cd\u7f16\u7a0b\u8bed\u8a00\u7684\u5339\u914d\u6807\u51c6\u3002 typingtom : \u9488\u5bf9\u7a0b\u5e8f\u5458\u7684\u6253\u5b57\u7ec3\u4e60/\u6d4b\u901f\u7f51\u7ad9\u3002","title":"\u7f16\u7a0b\u76f8\u5173"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_5","text":"HFS : \u5404\u7c7b\u8f6f\u4ef6\u6559\u7a0b\u3002 os-wiki : \u64cd\u4f5c\u7cfb\u7edf\u6280\u672f\u8d44\u6e90\u767e\u79d1\u5168\u4e66\u3002 Shadertoy : \u7f16\u5199\u5404\u5f0f\u5404\u6837\u7684 shader\u3002","title":"\u5b66\u4e60\u7f51\u7ad9"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_6","text":"tophub : \u65b0\u95fb\u70ed\u699c\u5408\u96c6\uff08\u7efc\u5408\u4e86\u77e5\u4e4e\u3001\u5fae\u535a\u3001\u767e\u5ea6\u3001\u5fae\u4fe1\u7b49\uff09\u3002 speedtest : \u5728\u7ebf\u7f51\u7edc\u6d4b\u901f\u7f51\u7ad9\u3002 public-apis : \u516c\u5171 API \u5408\u96c6\u5217\u8868\u3002","title":"\u6742\u9879"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/%E7%BF%BB%E5%A2%99/","text":"\u7ffb\u5899 \u6b64\u94fe\u63a5 \u51fa\u73b0\u5728\u8fd9\u91cc\u7eaf\u5c5e\u4e8c\u8fdb\u5236 bit \u7684\u968f\u610f\u7ec4\u5408\uff0c\u4e0e\u672c\u4eba\u6beb\u65e0\u5173\u7cfb\u3002","title":"\u7ffb\u5899"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/%E7%BF%BB%E5%A2%99/#_1","text":"\u6b64\u94fe\u63a5 \u51fa\u73b0\u5728\u8fd9\u91cc\u7eaf\u5c5e\u4e8c\u8fdb\u5236 bit \u7684\u968f\u610f\u7ec4\u5408\uff0c\u4e0e\u672c\u4eba\u6beb\u65e0\u5173\u7cfb\u3002","title":"\u7ffb\u5899"},{"location":"en/%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/CS162/","text":"CS162: Operating System \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS61A, CS61B, CS61C \u7f16\u7a0b\u8bed\u8a00\uff1aC, x86\u6c47\u7f16 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a200 \u5c0f\u65f6+\uff0c\u4e0a\u4e0d\u5c01\u9876 \u8fd9\u95e8\u8bfe\u8ba9\u6211\u8bb0\u5fc6\u72b9\u65b0\u7684\u6709\u4e24\u4e2a\u90e8\u5206\uff1a \u9996\u5148\u662f\u6559\u6750\uff0c\u8fd9\u672c\u4e66\u7528\u7684\u6559\u6750 Operating Systems: Principles and Practice (2nd Edition) \u4e00\u5171\u56db\u5377\uff0c\u5199\u5f97\u975e\u5e38\u6df1\u5165\u6d45\u51fa\uff0c\u5f88\u597d\u5730\u5f25\u8865\u4e86 MIT6.S081 \u5728\u7406\u8bba\u77e5\u8bc6\u4e0a\u7684\u4e9b\u8bb8\u7a7a\u767d\uff0c\u975e\u5e38\u5efa\u8bae\u5927\u5bb6\u9605\u8bfb\u3002\u76f8\u5173\u8d44\u6e90\u4f1a\u5206\u4eab\u5728\u672c\u4e66\u7684\u7ecf\u5178\u4e66\u7c4d\u63a8\u8350\u6a21\u5757\u3002 \u5176\u6b21\u662f\u8fd9\u95e8\u8bfe\u7684 Project \u2014\u2014 Pintos\u3002Pintos \u662f\u7531 Ben Pfaff \u7b49\u4eba\u5728 x86 \u5e73\u53f0\u4e0a\u7f16\u5199\u7684\u6559\u5b66\u7528\u64cd\u4f5c\u7cfb\u7edf\uff0cBen Pfaff \u751a\u81f3\u4e13\u95e8\u53d1\u4e86\u7bc7 paper \u6765\u9610\u8ff0 Pintos \u7684\u8bbe\u8ba1\u601d\u60f3\u3002 \u548c MIT \u7684 xv6 \u5c0f\u800c\u7cbe\u7684 lab \u8bbe\u8ba1\u7406\u5ff5\u4e0d\u540c\uff0cPintos \u66f4\u6ce8\u91cd\u7cfb\u7edf\u7684 Design and Implementation\u3002Pintos \u672c\u8eab\u4ec5\u4e00\u4e07\u884c\u5de6\u53f3\uff0c\u53ea\u63d0\u4f9b\u4e86\u64cd\u4f5c\u7cfb\u7edf\u6700\u57fa\u672c\u7684\u529f\u80fd\u3002\u800c 4 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Project\uff0c\u5177\u4f53\u8981\u6c42\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/CS162/#_3","text":"\u7531\u4e8e\u5317\u5927\u7684\u64cd\u7edf\u5b9e\u9a8c\u73ed\u91c7\u7528\u4e86\u8be5\u8bfe\u7a0b\u7684 Project\uff0c\u4e3a\u4e86\u9632\u6b62\u4ee3\u7801\u6284\u88ad\uff0c\u6211\u7684\u4ee3\u7801\u5b9e\u73b0\u6ca1\u6709\u5f00\u6e90\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/MIT6.S081/","text":"MIT 6.S081: Operating System Engineering Descriptions Offered by: MIT Prerequisites: Computer Architecture + Solid C Programming Skills + RISC-V Assembly Programming Languages: C, RISC-V Difficulty\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour\uff1a150 hours This is the undergraduate operating system course at MIT, offered by the well-known PDOS Group. One of the instructors, Robert Morris, was once a famous hacker who created 'Morris', the first worm virus in the world. The predecessor of this course was the famous MIT6.828. The same instructors at MIT created an educational operating system called JOS based on x86, which has been adopted by many other famous universities. While after the birth of RISC-V, they implemented it based on RISC-V, and offered MIT 6.S081. RISC-V is lightweight and user-friendly, so students don't have to struggle with the confusing legacy features in x86 as in JOS, but focus on the operating system design and implementation. The instructors have also written a tutorial , elaborately explaining the ideas of design and details of the implementation of xv6 operating system. The teaching style of this course is also interesting, the instructors guided the students to understand the numerous technical challenges and design principles in the operating systems by going through the xv6 source code, instead of merely teaching theoretical knowledge. Weekly Labs will let you add new features to xv6, which focus on enhancing students' practical skills. There are 11 labs in total during the whole semester which give you the chance to understand every aspect of the operating systems, bringing a great sense of achievement. Each lab has a complete framework for testing, some tests are more than a thousand lines of code, which shows how much effort the instructors have made to teach this course well. In the second half of the course, the instructors will discuss a couple of classic papers in the operating system field, covering file systems, system security, networking, virtualization, and so on, giving you a chance to have a taste of the cutting edge research directions in the academic field. Course Resources Course Website: https://pdos.csail.mit.edu/6.828/2021/schedule.html Lecture Videos\uff1a https://www.youtube.com/watch?v=L6YqHxYHa7A , videos for each lecture can be found on the course website. Translated documentation(Chinese) of Lecture videos: https://mit-public-courses-cn-translatio.gitbook.io/mit6-s081/ Text Book: https://pdos.csail.mit.edu/6.828/2021/xv6/book-riscv-rev2.pdf Assignments: https://pdos.csail.mit.edu/6.828/2021/schedule.html , 11 labs, can be found on the course website. xv6 Resources Detailed Explanation of xv6 xv6 Documentation(Chinese) Complementary Resources All resources used and assignments implemented by @PKUFlyingPig when learning this course are in PKUFlyingPig/MIT6.S081-2020fall - GitHub . @ KuangjuX documented his solutions with detailed explanations and complementary knowledge. Moreover, @ KuangjuX has reimplemented the xv6 operating system in Rust which contains more detailed reviews and discussions about xv6. Some Blogs for References doraemonzzz Xiao Fan (\u6a0a\u6f47) Miigon's blog Zhou Fang Yichun's Blog \u89e3\u6790Ta PKUFlyingPig \u661f\u9065\u89c1","title":"MIT 6.S081: Operating System Engineering"},{"location":"en/%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/MIT6.S081/#mit-6s081-operating-system-engineering","text":"","title":"MIT 6.S081: Operating System Engineering"},{"location":"en/%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/MIT6.S081/#descriptions","text":"Offered by: MIT Prerequisites: Computer Architecture + Solid C Programming Skills + RISC-V Assembly Programming Languages: C, RISC-V Difficulty\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour\uff1a150 hours This is the undergraduate operating system course at MIT, offered by the well-known PDOS Group. One of the instructors, Robert Morris, was once a famous hacker who created 'Morris', the first worm virus in the world. The predecessor of this course was the famous MIT6.828. The same instructors at MIT created an educational operating system called JOS based on x86, which has been adopted by many other famous universities. While after the birth of RISC-V, they implemented it based on RISC-V, and offered MIT 6.S081. RISC-V is lightweight and user-friendly, so students don't have to struggle with the confusing legacy features in x86 as in JOS, but focus on the operating system design and implementation. The instructors have also written a tutorial , elaborately explaining the ideas of design and details of the implementation of xv6 operating system. The teaching style of this course is also interesting, the instructors guided the students to understand the numerous technical challenges and design principles in the operating systems by going through the xv6 source code, instead of merely teaching theoretical knowledge. Weekly Labs will let you add new features to xv6, which focus on enhancing students' practical skills. There are 11 labs in total during the whole semester which give you the chance to understand every aspect of the operating systems, bringing a great sense of achievement. Each lab has a complete framework for testing, some tests are more than a thousand lines of code, which shows how much effort the instructors have made to teach this course well. In the second half of the course, the instructors will discuss a couple of classic papers in the operating system field, covering file systems, system security, networking, virtualization, and so on, giving you a chance to have a taste of the cutting edge research directions in the academic field.","title":"Descriptions"},{"location":"en/%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/MIT6.S081/#course-resources","text":"Course Website: https://pdos.csail.mit.edu/6.828/2021/schedule.html Lecture Videos\uff1a https://www.youtube.com/watch?v=L6YqHxYHa7A , videos for each lecture can be found on the course website. Translated documentation(Chinese) of Lecture videos: https://mit-public-courses-cn-translatio.gitbook.io/mit6-s081/ Text Book: https://pdos.csail.mit.edu/6.828/2021/xv6/book-riscv-rev2.pdf Assignments: https://pdos.csail.mit.edu/6.828/2021/schedule.html , 11 labs, can be found on the course website.","title":"Course Resources"},{"location":"en/%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/MIT6.S081/#xv6-resources","text":"Detailed Explanation of xv6 xv6 Documentation(Chinese)","title":"xv6 Resources"},{"location":"en/%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/MIT6.S081/#complementary-resources","text":"All resources used and assignments implemented by @PKUFlyingPig when learning this course are in PKUFlyingPig/MIT6.S081-2020fall - GitHub . @ KuangjuX documented his solutions with detailed explanations and complementary knowledge. Moreover, @ KuangjuX has reimplemented the xv6 operating system in Rust which contains more detailed reviews and discussions about xv6.","title":"Complementary Resources"},{"location":"en/%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/MIT6.S081/#some-blogs-for-references","text":"doraemonzzz Xiao Fan (\u6a0a\u6f47) Miigon's blog Zhou Fang Yichun's Blog \u89e3\u6790Ta PKUFlyingPig \u661f\u9065\u89c1","title":"Some Blogs for References"},{"location":"en/%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/NJUOS/","text":"NJU OS: Operating System Design and Implementation \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1a\u5357\u4eac\u5927\u5b66 \u5148\u4fee\u8981\u6c42\uff1a\u4f53\u7cfb\u7ed3\u6784 + \u624e\u5b9e\u7684 C \u8bed\u8a00\u529f\u5e95 \u7f16\u7a0b\u8bed\u8a00\uff1aC \u8bed\u8a00 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 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\u90fd\u80fd\u975e\u5e38\u65b9\u4fbf\u5730\u8fdb\u884c\u672c\u5730\u6d4b\u8bd5\uff0c\u5c31\u7b97\u6ca1\u6709\u8bc4\u6d4b\u673a\u4e5f\u4e0d\u5f71\u54cd\u81ea\u5b66\uff0c\u56e0\u6b64\u5e0c\u671b\u5927\u5bb6\u4e0d\u8981\u805a\u4f17\u201c\u9a9a\u6270\u201d\u8001\u5e08\u4ee5\u56fe\u8e6d\u8bfe\u3002 \u6700\u540e\u518d\u6b21\u611f\u8c22\u848b\u8001\u5e08\u8bbe\u8ba1\u5e76\u5f00\u653e\u4e86\u8fd9\u6837\u4e00\u95e8\u975e\u5e38\u68d2\u7684\u64cd\u4f5c\u7cfb\u7edf\u8bfe\u7a0b\uff0c\u8fd9\u4e5f\u662f\u672c\u4e66\u6536\u5f55\u7684\u7b2c\u4e00\u95e8\u56fd\u5185\u9ad8\u6821\u81ea\u4e3b\u5f00\u8bbe\u7684\u8ba1\u7b97\u673a\u8bfe\u7a0b\u3002\u6b63\u662f\u6709\u848b\u8001\u5e08\u8fd9\u4e9b\u5e74\u8f7b\u7684\u65b0\u751f\u4ee3\u6559\u5e08\u5728\u7e41\u91cd\u7684 Tenure \u8003\u6838\u4e4b\u4f59\u7684\u7528\u7231\u53d1\u7535\uff0c\u624d\u8ba9\u65e0\u6570\u5b66\u5b50\u6536\u83b7\u4e86\u96be\u5fd8\u7684\u672c\u79d1\u751f\u6daf\u3002\u4e5f\u671f\u5f85\u56fd\u5185\u80fd\u6709\u66f4\u591a\u8fd9\u6837\u7684\u826f\u5fc3\u597d\u8bfe\uff0c\u6211\u4e5f\u4f1a\u7b2c\u4e00\u65f6\u95f4\u6536\u5f55\u8fdb\u672c\u4e66\u4e2d\u8ba9\u66f4\u591a\u4eba\u53d7\u76ca\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://jyywiki.cn/OS/2022/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://space.bilibili.com/202224425/channel/collectiondetail?sid=192498 \u8bfe\u7a0b\u6559\u6750\uff1a http://pages.cs.wisc.edu/~remzi/OSTEP/ \u8bfe\u7a0b\u4f5c\u4e1a\uff1a http://jyywiki.cn/OS/2022/ \u8d44\u6e90\u6c47\u603b \u6309\u848b\u8001\u5e08\u7684\u8981\u6c42\uff0c\u6211\u7684\u4f5c\u4e1a\u5b9e\u73b0\u6ca1\u6709\u5f00\u6e90\u3002","title":"NJU OS: Operating System Design and Implementation"},{"location":"en/%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/NJUOS/#nju-os-operating-system-design-and-implementation","text":"","title":"NJU OS: Operating System Design and Implementation"},{"location":"en/%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/NJUOS/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1a\u5357\u4eac\u5927\u5b66 \u5148\u4fee\u8981\u6c42\uff1a\u4f53\u7cfb\u7ed3\u6784 + \u624e\u5b9e\u7684 C \u8bed\u8a00\u529f\u5e95 \u7f16\u7a0b\u8bed\u8a00\uff1aC \u8bed\u8a00 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 \u4e4b\u524d\u4e00\u76f4\u542c\u8bf4\u5357\u5927\u7684\u848b\u708e\u5ca9\u8001\u5e08\u5f00\u8bbe\u7684\u64cd\u4f5c\u7cfb\u7edf\u8bfe\u7a0b\u8bb2\u5f97\u5f88\u597d\uff0c\u4e45\u95fb\u4e0d\u5982\u4e00\u89c1\uff0c\u8fd9\u5b66\u671f\u6709\u5e78\u5728 B 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\u8003\u6838\u4e4b\u4f59\u7684\u7528\u7231\u53d1\u7535\uff0c\u624d\u8ba9\u65e0\u6570\u5b66\u5b50\u6536\u83b7\u4e86\u96be\u5fd8\u7684\u672c\u79d1\u751f\u6daf\u3002\u4e5f\u671f\u5f85\u56fd\u5185\u80fd\u6709\u66f4\u591a\u8fd9\u6837\u7684\u826f\u5fc3\u597d\u8bfe\uff0c\u6211\u4e5f\u4f1a\u7b2c\u4e00\u65f6\u95f4\u6536\u5f55\u8fdb\u672c\u4e66\u4e2d\u8ba9\u66f4\u591a\u4eba\u53d7\u76ca\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/NJUOS/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://jyywiki.cn/OS/2022/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://space.bilibili.com/202224425/channel/collectiondetail?sid=192498 \u8bfe\u7a0b\u6559\u6750\uff1a http://pages.cs.wisc.edu/~remzi/OSTEP/ \u8bfe\u7a0b\u4f5c\u4e1a\uff1a http://jyywiki.cn/OS/2022/","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/NJUOS/#_3","text":"\u6309\u848b\u8001\u5e08\u7684\u8981\u6c42\uff0c\u6211\u7684\u4f5c\u4e1a\u5b9e\u73b0\u6ca1\u6709\u5f00\u6e90\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITLA/","text":"MIT18.06: Linear Algebra Descriptions Offered by: MIT Prerequisites: English Programming languages: None Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: Varying from person to person Gilbert Strang, a great mathematician at MIT, still insists on teaching in his eighties. His classic text book Introduction to Linear Algebra has been adopted as an official textbook by Tsinghua University. After reading the PDF version, I felt deeply guilty and spent more than 200 yuan to purchase a genuine version in English as collection. The cover of this book is attached below. If you can fully understand the mathematical meaning of the cover picture, then your understanding of linear algebra will definitely reach a new height. In addition to the course materials, the famous Youtuber 3Blue1Brown 's video series The Essence of Linear Algebra are also great learning resources. Resources Course Website: https://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/syllabus/ Recordings: refer to the course website Textbook: Introduction to Linear Algebra, Gilbert Strang Assignments: refer to the course website","title":"MIT18.06: Linear Algebra"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITLA/#mit1806-linear-algebra","text":"","title":"MIT18.06: Linear Algebra"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITLA/#descriptions","text":"Offered by: MIT Prerequisites: English Programming languages: None Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: Varying from person to person Gilbert Strang, a great mathematician at MIT, still insists on teaching in his eighties. His classic text book Introduction to Linear Algebra has been adopted as an official textbook by Tsinghua University. After reading the PDF version, I felt deeply guilty and spent more than 200 yuan to purchase a genuine version in English as collection. The cover of this book is attached below. If you can fully understand the mathematical meaning of the cover picture, then your understanding of linear algebra will definitely reach a new height. In addition to the course materials, the famous Youtuber 3Blue1Brown 's video series The Essence of Linear Algebra are also great learning resources.","title":"Descriptions"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITLA/#resources","text":"Course Website: https://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/syllabus/ Recordings: refer to the course website Textbook: Introduction to Linear Algebra, Gilbert Strang Assignments: refer to the course website","title":"Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITmaths/","text":"MIT Calculus Course Descriptions Offered by: MIT Prerequisites: English Programming Languages: None Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: Varying from person to person The calculus course at MIT consists of MIT18.01: Single Variable Calculus and MIT18.02: Multivariable Calculus. If you are confident in your math, you can just read the course notes, which are written in a very simple and vivid way, so that you will not be tired of doing homework but can really see the essence of calculus. In addition to the course materials, the famous Youtuber 3Blue1Brown 's video series The Essence of Calculus are also great learning resources. Course Resources Course Website: 18.01 , 18.02 Recordings: refer to course website Textbook: refer to course website Assignments: refer to course website","title":"MIT18.01/18.02: Calculus"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITmaths/#mit-calculus-course","text":"","title":"MIT Calculus Course"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITmaths/#descriptions","text":"Offered by: MIT Prerequisites: English Programming Languages: None Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: Varying from person to person The calculus course at MIT consists of MIT18.01: Single Variable Calculus and MIT18.02: Multivariable Calculus. If you are confident in your math, you can just read the course notes, which are written in a very simple and vivid way, so that you will not be tired of doing homework but can really see the essence of calculus. In addition to the course materials, the famous Youtuber 3Blue1Brown 's video series The Essence of Calculus are also great learning resources.","title":"Descriptions"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITmaths/#course-resources","text":"Course Website: 18.01 , 18.02 Recordings: refer to course website Textbook: refer to course website Assignments: refer to course website","title":"Course Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/information/","text":"MIT6.050J: Information theory and Entropy Descriptions Offered by: MIT Prerequisites: None Programming Languages: None Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 100 hours This is MIT's introductory information theory course for freshmen, Professor Penfield has written a special textbook for this course as course notes, which is in-depth and interesting. Course Resources Course Website: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/index.htm Textbook: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/syllabus/MIT6_050JS08_textbook.pdf Assignments: see the course website for details, including written assignments and Matlab programming assignments.","title":"MIT6.050J: Information theory and Entropy"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/information/#mit6050j-information-theory-and-entropy","text":"","title":"MIT6.050J: Information theory and Entropy"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/information/#descriptions","text":"Offered by: MIT Prerequisites: None Programming Languages: None Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 100 hours This is MIT's introductory information theory course for freshmen, Professor Penfield has written a special textbook for this course as course notes, which is in-depth and interesting.","title":"Descriptions"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/information/#course-resources","text":"Course Website: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/index.htm Textbook: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/syllabus/MIT6_050JS08_textbook.pdf Assignments: see the course website for details, including written assignments and Matlab programming assignments.","title":"Course Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/6.042J/","text":"MIT 6.042J: Mathematics for Computer Science Descriptions Offered by: MIT Prerequisites: Calculus, Linear Algebra Programming Languages: Python preferred Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 50-70 hours This is MIT\u2018s discrete mathematics and probability course taught by the notable Tom Leighton (co-founder of Akamai). It is very useful for learning algorithms subsequently. Course Resources Course Website: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/ Recordings: https://www.youtube.com/playlist?list=PLB7540DEDD482705B Assignments: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/assignments/","title":"MIT 6.042J: Mathematics for Computer Science"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/6.042J/#mit-6042j-mathematics-for-computer-science","text":"","title":"MIT 6.042J: Mathematics for Computer Science"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/6.042J/#descriptions","text":"Offered by: MIT Prerequisites: Calculus, Linear Algebra Programming Languages: Python preferred Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 50-70 hours This is MIT\u2018s discrete mathematics and probability course taught by the notable Tom Leighton (co-founder of Akamai). It is very useful for learning algorithms subsequently.","title":"Descriptions"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/6.042J/#course-resources","text":"Course Website: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/ Recordings: https://www.youtube.com/playlist?list=PLB7540DEDD482705B Assignments: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/assignments/","title":"Course Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS126/","text":"UCB CS126 : Probability theory Descriptions Offered by: UC Berkeley Prerequisites: CS70, Calculus, Linear Algebra Programming Languages: Python Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 100 hours This is Berkeley's advanced probability course, which involves relatively advanced theoretical content such as statistics and stochastic processes, so a solid mathematical foundation is required. But as long as you stick with it you will certainly take your mastery of probability theory to a new level. The course is designed by Professor Jean Walrand, who has written an accompanying textbook, Probability in Electrical Engineering and Computer Science , in which each chapter uses a specific algorithm as a practical example to demonstrate the application of theory in practice. Such as PageRank, Route Planing, Speech Recognition, etc. The book is open source and can be downloaded as a free PDF or Epub version. Jean Walrand has also created accompanying Python implementations of the examples throughout the book, which are published online as Jupyter Notebook that readers can modify, debug and run them online interactively. In addition to the Homework, nine Labs will allow you to use probability theory to solve practical problems in Python. Course Resources Course Website: https://inst.eecs.berkeley.edu/~ee126/fa20/content.html Textbook: PDF , Epub , Jupyter Notebook Assignments: refer to the course website. Personal Resources All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/EECS126 - GitHub","title":"UCB CS126: probability theory"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS126/#ucb-cs126-probability-theory","text":"","title":"UCB CS126 : Probability theory"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS126/#descriptions","text":"Offered by: UC Berkeley Prerequisites: CS70, Calculus, Linear Algebra Programming Languages: Python Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 100 hours This is Berkeley's advanced probability course, which involves relatively advanced theoretical content such as statistics and stochastic processes, so a solid mathematical foundation is required. But as long as you stick with it you will certainly take your mastery of probability theory to a new level. The course is designed by Professor Jean Walrand, who has written an accompanying textbook, Probability in Electrical Engineering and Computer Science , in which each chapter uses a specific algorithm as a practical example to demonstrate the application of theory in practice. Such as PageRank, Route Planing, Speech Recognition, etc. The book is open source and can be downloaded as a free PDF or Epub version. Jean Walrand has also created accompanying Python implementations of the examples throughout the book, which are published online as Jupyter Notebook that readers can modify, debug and run them online interactively. In addition to the Homework, nine Labs will allow you to use probability theory to solve practical problems in Python.","title":"Descriptions"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS126/#course-resources","text":"Course Website: https://inst.eecs.berkeley.edu/~ee126/fa20/content.html Textbook: PDF , Epub , Jupyter Notebook Assignments: refer to the course website.","title":"Course Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS126/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/EECS126 - GitHub","title":"Personal Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS70/","text":"UCB CS70: Discrete Math and Probability Theory Descriptions Offered by: UC Berkeley Prerequisites: None Programming Languages: None Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 60 hours This is Berkeley's introductory discrete mathematics course. The biggest highlight of this course is that it not only teaches you theoretical knowledge, but also introduce the applications of theoretical knowledge in practical algorithms in each module. In this way, students majoring in CS can understand the essence of theoretical knowledge and use it in practice rather than struggle with cold formal mathematical symbols. Specific theory-algorithm correspondences are listed below. Logic proof: stable matching algorithm Graph theory: network topology design Basic number theory: RSA algorithm Polynomial ring: error-correcting code design Probability theory: Hash table design, load balancing, etc. The course notes are also written in a very in-depth manner, with derivations of formulas and practical examples, providing a good reading experience. Course Resources Course Website: http://www.eecs70.org/ Textbook: refer to the course website Assignments: refer to the course website Personal Resources All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/UCB-CS70 - GitHub","title":"UCB CS70: discrete Math and probability theory"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS70/#ucb-cs70-discrete-math-and-probability-theory","text":"","title":"UCB CS70: Discrete Math and Probability Theory"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS70/#descriptions","text":"Offered by: UC Berkeley Prerequisites: None Programming Languages: None Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 60 hours This is Berkeley's introductory discrete mathematics course. The biggest highlight of this course is that it not only teaches you theoretical knowledge, but also introduce the applications of theoretical knowledge in practical algorithms in each module. In this way, students majoring in CS can understand the essence of theoretical knowledge and use it in practice rather than struggle with cold formal mathematical symbols. Specific theory-algorithm correspondences are listed below. Logic proof: stable matching algorithm Graph theory: network topology design Basic number theory: RSA algorithm Polynomial ring: error-correcting code design Probability theory: Hash table design, load balancing, etc. The course notes are also written in a very in-depth manner, with derivations of formulas and practical examples, providing a good reading experience.","title":"Descriptions"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS70/#course-resources","text":"Course Website: http://www.eecs70.org/ Textbook: refer to the course website Assignments: refer to the course website","title":"Course Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS70/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/UCB-CS70 - GitHub","title":"Personal Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/The_Information_Theory_Pattern_Recognition_and_Neural_Networks/","text":"The Information Theory, Patter Recognition, and Neural Networks Descriptions Offered by: Cambridge Prerequisites: Calculus, Linear Algebra, Probabilities and Statistics Programming Languages: Anything would be OK, Python preferred Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 30-50 hours This is a course on information theory taught by Sir David MacKay at the University of Cambridge. The professor is a very famous scholar in information theory and neural networks, and the textbook for the course is a classic work in the field of information theory. Unfortunately, those whom God loves die young ... Course Resources Course Website: http://www.inference.org.uk/mackay/itila/ Recordings: https://www.youtube.com/playlist?list=PLruBu5BI5n4aFpG32iMbdWoRVAA-Vcso6 Textbooks: Information Theory, Inference, and Learning Algorithms Assignments: At the end of each lesson video, there are post-lesson exercises from the textbook R.I.P Prof. David MacKay","title":"The Information Theory, Patter Recognition, and Neural Networks"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/The_Information_Theory_Pattern_Recognition_and_Neural_Networks/#the-information-theory-patter-recognition-and-neural-networks","text":"","title":"The Information Theory, Patter Recognition, and Neural Networks"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/The_Information_Theory_Pattern_Recognition_and_Neural_Networks/#descriptions","text":"Offered by: Cambridge Prerequisites: Calculus, Linear Algebra, Probabilities and Statistics Programming Languages: Anything would be OK, Python preferred Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 30-50 hours This is a course on information theory taught by Sir David MacKay at the University of Cambridge. The professor is a very famous scholar in information theory and neural networks, and the textbook for the course is a classic work in the field of information theory. Unfortunately, those whom God loves die young ...","title":"Descriptions"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/The_Information_Theory_Pattern_Recognition_and_Neural_Networks/#course-resources","text":"Course Website: http://www.inference.org.uk/mackay/itila/ Recordings: https://www.youtube.com/playlist?list=PLruBu5BI5n4aFpG32iMbdWoRVAA-Vcso6 Textbooks: Information Theory, Inference, and Learning Algorithms Assignments: At the end of each lesson video, there are post-lesson exercises from the textbook","title":"Course Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/The_Information_Theory_Pattern_Recognition_and_Neural_Networks/#rip-prof-david-mackay","text":"","title":"R.I.P Prof. David MacKay"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/convex/","text":"Stanford EE364A: Convex Optimization Descriptions Offered by: Stanford Prerequisites: Python, Calculus, Linear Algebra, Probability Theory, Numerical Analysis Programming Languages: Python Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 150 hours Professor Stephen Boyd is a great expert in the field of convex optimization and his textbook Convex Optimization has been adopted by many prestigious universities. His team has also developed a programming framework for solving common convex optimization problems in Python, Julia, and other popular programming languages, and its homework assignments also use this programming framework to solve real-life convex optimization problems. In practice, you will deeply understand that for the same problem, a small change in the modeling process can make a world of difference in the difficulty of solving the equation. It is an art to make the equations you formulate \"convex\". Course Resources Course Website: http://stanford.edu/class/ee364a/index.html Recordings: https://www.youtube.com/watch?v=VNON98dKjno&list=PLoCMsyE1cvdXeoqd1hGaMBsCAQQ6otUtO Textbook: Convex Optimization Assignments: refer to the course website Personal Resources All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPic/Standford_CVX101 - GitHub","title":"Standford EE364A: Convex Optimization"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/convex/#stanford-ee364a-convex-optimization","text":"","title":"Stanford EE364A: Convex Optimization"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/convex/#descriptions","text":"Offered by: Stanford Prerequisites: Python, Calculus, Linear Algebra, Probability Theory, Numerical Analysis Programming Languages: Python Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 150 hours Professor Stephen Boyd is a great expert in the field of convex optimization and his textbook Convex Optimization has been adopted by many prestigious universities. His team has also developed a programming framework for solving common convex optimization problems in Python, Julia, and other popular programming languages, and its homework assignments also use this programming framework to solve real-life convex optimization problems. In practice, you will deeply understand that for the same problem, a small change in the modeling process can make a world of difference in the difficulty of solving the equation. It is an art to make the equations you formulate \"convex\".","title":"Descriptions"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/convex/#course-resources","text":"Course Website: http://stanford.edu/class/ee364a/index.html Recordings: https://www.youtube.com/watch?v=VNON98dKjno&list=PLoCMsyE1cvdXeoqd1hGaMBsCAQQ6otUtO Textbook: Convex Optimization Assignments: refer to the course website","title":"Course Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/convex/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPic/Standford_CVX101 - GitHub","title":"Personal Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/numerical/","text":"MIT18.330 : Introduction to numerical analysis Descriptions Offered by: MIT Prerequisites:Calculus, Linear Algebra, Probability theory Programming Languages: Julia Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 150 hours While the computational power of computers has been helping people to push boundaries of science, there is a natural barrier between the discrete nature of computers and this continuous world, and how to use discrete representations to estimate and approximate those mathematically continuous concepts is an important theme in numerical analysis. This course will explore various numerical analysis methods in the areas of floating-point representation, equation solving, linear algebra, calculus, and differential equations, allowing you to understand (1) how to design estimation (2) how to estimate errors (3) how to implement algorithms in Julia. There are also plenty of programming assignments to practice these ideas. The designers of this course have also written an open source textbook for this course (see the link below) with plenty of Julia examples. Course Resources Course Website: https://github.com/mitmath/18330 Textbook: https://fncbook.github.io/fnc/frontmatter.html Assignments: 10 problem sets Personal Resources All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPic/MIT18.330 - GitHub","title":"MIT18.330: Introduction to numerical analysis"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/numerical/#mit18330-introduction-to-numerical-analysis","text":"","title":"MIT18.330 : Introduction to numerical analysis"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/numerical/#descriptions","text":"Offered by: MIT Prerequisites:Calculus, Linear Algebra, Probability theory Programming Languages: Julia Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 150 hours While the computational power of computers has been helping people to push boundaries of science, there is a natural barrier between the discrete nature of computers and this continuous world, and how to use discrete representations to estimate and approximate those mathematically continuous concepts is an important theme in numerical analysis. This course will explore various numerical analysis methods in the areas of floating-point representation, equation solving, linear algebra, calculus, and differential equations, allowing you to understand (1) how to design estimation (2) how to estimate errors (3) how to implement algorithms in Julia. There are also plenty of programming assignments to practice these ideas. The designers of this course have also written an open source textbook for this course (see the link below) with plenty of Julia examples.","title":"Descriptions"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/numerical/#course-resources","text":"Course Website: https://github.com/mitmath/18330 Textbook: https://fncbook.github.io/fnc/frontmatter.html Assignments: 10 problem sets","title":"Course Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/numerical/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPic/MIT18.330 - GitHub","title":"Personal Resources"},{"location":"en/%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/15445/","text":"CMU 15-445: Database Systems \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aCMU \u5148\u4fee\u8981\u6c42\uff1aC++\uff0c\u6570\u636e\u7ed3\u6784\u4e0e\u7b97\u6cd5 \u7f16\u7a0b\u8bed\u8a00\uff1aC++ 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\u6839\u636e\u8c13\u8bcd\u66f4\u65b0\u8ba1\u5212\u8282\u70b9\u8f93\u51fa\u7684\u5143\u7ec4\u7edf\u8ba1\u4fe1\u606f\u3002 \u5269\u4f59 Assignment \u548c Challenges \u53ef\u4ee5\u67e5\u770b\u8bfe\u7a0b\u4ecb\u7ecd\uff0c\u63a8\u8350\u4f7f\u7528 IDEA \u6253\u5f00\u5de5\u7a0b\uff0cMaven \u6784\u5efa\uff0c\u6ce8\u610f\u65e5\u5fd7\u76f8\u5173\u914d\u7f6e\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://courses.cms.caltech.edu/cs122/ \u8bfe\u7a0b\u4ee3\u7801\uff1a https://gitlab.caltech.edu/cs122-19wi \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a7 Assignments + 2 Challenges","title":"Caltech CS122: Database System Implementation"},{"location":"en/%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/CS122/#caltech-cs-122-database-system-implementation","text":"","title":"Caltech CS 122: Database System Implementation"},{"location":"en/%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/CS122/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aCaltech 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\u53ef\u4ee5\u67e5\u770b\u8bfe\u7a0b\u4ecb\u7ecd\uff0c\u63a8\u8350\u4f7f\u7528 IDEA \u6253\u5f00\u5de5\u7a0b\uff0cMaven \u6784\u5efa\uff0c\u6ce8\u610f\u65e5\u5fd7\u76f8\u5173\u914d\u7f6e\u3002","title":"Assignment3"},{"location":"en/%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/CS122/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://courses.cms.caltech.edu/cs122/ \u8bfe\u7a0b\u4ee3\u7801\uff1a https://gitlab.caltech.edu/cs122-19wi \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a7 Assignments + 2 Challenges","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/CS186/","text":"UCB CS186: Introduction to Database System Descriptions Offered by: UC Berkeley Prerequisites: CS61A, CS61B, CS61C Programming Languages: Java Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 150 hours How to write SQL queries? How are SQL commands disassembled, optimized, and transformed into on-disk query commands step by step? How to implement a high-concurrency database? How to implement database failure recovery? What is NoSQL? This course elaborates on the internal details of relational databases. Besides the theoretical knowledge, you will use Java to implement a real relational database that supports SQL concurrent query, B+ tree index, and failure recovery. From a practical point of view, you will have the opportunity to write SQL queries and NoSQL queries in course projects, which is very helpful for building full-stack projects. Course Resources Course Website: https://cs186berkeley.net/ Recordings: https://www.youtube.com/playlist?list=PLYp4IGUhNFmw8USiYMJvCUjZe79fvyYge Assignments: https://cs186.gitbook.io/project/ Personal Resources All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/CS186 - GitHub .","title":"UCB CS186: Introduction to Database System"},{"location":"en/%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/CS186/#ucb-cs186-introduction-to-database-system","text":"","title":"UCB CS186: Introduction to Database System"},{"location":"en/%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/CS186/#descriptions","text":"Offered by: UC Berkeley Prerequisites: CS61A, CS61B, CS61C Programming Languages: Java Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 150 hours How to write SQL queries? How are SQL commands disassembled, optimized, and transformed into on-disk query commands step by step? How to implement a high-concurrency database? How to implement database failure recovery? What is NoSQL? This course elaborates on the internal details of relational databases. Besides the theoretical knowledge, you will use Java to implement a real relational database that supports SQL concurrent query, B+ tree index, and failure recovery. From a practical point of view, you will have the opportunity to write SQL queries and NoSQL queries in course projects, which is very helpful for building full-stack projects.","title":"Descriptions"},{"location":"en/%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/CS186/#course-resources","text":"Course Website: https://cs186berkeley.net/ Recordings: https://www.youtube.com/playlist?list=PLYp4IGUhNFmw8USiYMJvCUjZe79fvyYge Assignments: https://cs186.gitbook.io/project/","title":"Course Resources"},{"location":"en/%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/CS186/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/CS186 - GitHub .","title":"Personal Resources"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6/Data100/","text":"UCB Data100: Principles and Techniques of Data Science Description Offered by: UC Berkeley Prerequisites: CS61A\uff0cLinear Algebra Programming Languages: Python Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 80 hours This is Berkeley's introductory course in data science, covering the basics of data cleaning, feature extraction, data visualization, machine learning and inference, as well as common data science tools such as Pandas, Numpy, and Matplotlib. The course is also rich in interesting programming assignments, which is one of the highlights of the course. Resources Course Website: https://ds100.org/fa21/ Records: refer to the course website Textbook: https://www.textbook.ds100.org/intro.html Assignments: refer to the course website","title":"UCB Data100: Principles and Techniques of Data Science"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6/Data100/#ucb-data100-principles-and-techniques-of-data-science","text":"","title":"UCB Data100: Principles and Techniques of Data Science"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6/Data100/#description","text":"Offered by: UC Berkeley Prerequisites: CS61A\uff0cLinear Algebra Programming Languages: Python Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 80 hours This is Berkeley's introductory course in data science, covering the basics of data cleaning, feature extraction, data visualization, machine learning and inference, as well as common data science tools such as Pandas, Numpy, and Matplotlib. The course is also rich in interesting programming assignments, which is one of the highlights of the course.","title":"Description"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6/Data100/#resources","text":"Course Website: https://ds100.org/fa21/ Records: refer to the course website Textbook: https://www.textbook.ds100.org/intro.html Assignments: refer to the course website","title":"Resources"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/Algo/","text":"Coursera: Algorithms I & II Descriptions Offered by: Princeton Prerequisites: CS61A Programming Languages: Java Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 60 hours This is the highest rated algorithms course on Coursera , and Robert Sedgewick has the magic to make even the most complex algorithms incredibly easy to understand. To be honest, the KMP and network flow algorithms that I have been struggling with for years were made clear to me in this course, and I can even write derivations and proofs for both of them two years later. Do you feel that you forget the algorithms quickly after learning them? I think the key to fully grasping an algorithm lies in understanding the three points as follows: Why should do this? (Correctness derivation, or the essence of the entire algorithm.) How to implement it? (Talk is cheap. Show me the code.) How to use it to solve practical problems? (Bridge the gap between theory and real life.) The composition of this course covers the three points above very well. Watching the course videos and reading the professor's textbook will help you understand the essence of the algorithm and allow you to tell others why the algorithm should look like this in very simple and vivid terms. After understanding the algorithms, you can read the professor's code implementation of all the data structures and algorithms taught in the course. Note that these codes are not demos, but production-ready, time-efficient implementations. They have extensive annotations and comments, and the modularization is also quite good. I learned a lot by just reading the codes. Finally, the most exciting part of the course is the 10 high-quality projects, all with real-world backgrounds, rich test cases, and an automated scoring system (code style is also a part of the scoring). You'll get a taste of algorithms in real life. Course Resources Course Website: Algorithm I , Algorithm II Recordings: Coursera: Algorithm I , Coursera: lgorithm II , CUvids: Algorithms, 4th Edition Textbooks: Algorithms, 4th Edition Assignments: 10 Projects, the course website has specific requirements Personal Resources All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/Princeton-Algorithm - GitHub .","title":"Coursera: Algorithms I & II"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/Algo/#coursera-algorithms-i-ii","text":"","title":"Coursera: Algorithms I & II"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/Algo/#descriptions","text":"Offered by: Princeton Prerequisites: CS61A Programming Languages: Java Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 60 hours This is the highest rated algorithms course on Coursera , and Robert Sedgewick has the magic to make even the most complex algorithms incredibly easy to understand. To be honest, the KMP and network flow algorithms that I have been struggling with for years were made clear to me in this course, and I can even write derivations and proofs for both of them two years later. Do you feel that you forget the algorithms quickly after learning them? I think the key to fully grasping an algorithm lies in understanding the three points as follows: Why should do this? (Correctness derivation, or the essence of the entire algorithm.) How to implement it? (Talk is cheap. Show me the code.) How to use it to solve practical problems? (Bridge the gap between theory and real life.) The composition of this course covers the three points above very well. Watching the course videos and reading the professor's textbook will help you understand the essence of the algorithm and allow you to tell others why the algorithm should look like this in very simple and vivid terms. After understanding the algorithms, you can read the professor's code implementation of all the data structures and algorithms taught in the course. Note that these codes are not demos, but production-ready, time-efficient implementations. They have extensive annotations and comments, and the modularization is also quite good. I learned a lot by just reading the codes. Finally, the most exciting part of the course is the 10 high-quality projects, all with real-world backgrounds, rich test cases, and an automated scoring system (code style is also a part of the scoring). You'll get a taste of algorithms in real life.","title":"Descriptions"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/Algo/#course-resources","text":"Course Website: Algorithm I , Algorithm II Recordings: Coursera: Algorithm I , Coursera: lgorithm II , CUvids: Algorithms, 4th Edition Textbooks: Algorithms, 4th Edition Assignments: 10 Projects, the course website has specific requirements","title":"Course Resources"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/Algo/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/Princeton-Algorithm - GitHub .","title":"Personal Resources"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS170/","text":"CS170: Efficient Algorithms and Intractable Problems Descriptions Offered by: UC Berkeley Prerequisites: CS61B, CS70 Programming Languages: LaTeX Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 60 hours This is Berkeley's algorithm design and analysis course. It focuses on the theoretical foundations and complexity analysis of algorithms, covering Divide-and-Conquer, Graph Algorithms, Shortest Paths, Spanning Trees, Greedy Algorithms, Dynamic programming, Union Finds, Linear Programming, Network Flows, NP-Completeness, Randomized Algorithms, Hashing, etc. The textbook for this course is well written and very suitable as a reference book. In addition, this class has written assignments and is recommended to use LaTeX. You can take this opportunity to practice your LaTeX skills. Course Resources Course Website: https://cs170.org/ Recordings: https://www.youtube.com/playlist?list=PLnocShPlK-Ft-o7NInBDw18be86dNaxlT Recordings: refer to the course website Assignments: refer to the course website Personal Resources All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/UCB-CS170 - GitHub","title":"UCB CS170: Efficient Algorithms and Intractable Problems"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS170/#cs170-efficient-algorithms-and-intractable-problems","text":"","title":"CS170: Efficient Algorithms and Intractable Problems"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS170/#descriptions","text":"Offered by: UC Berkeley Prerequisites: CS61B, CS70 Programming Languages: LaTeX Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 60 hours This is Berkeley's algorithm design and analysis course. It focuses on the theoretical foundations and complexity analysis of algorithms, covering Divide-and-Conquer, Graph Algorithms, Shortest Paths, Spanning Trees, Greedy Algorithms, Dynamic programming, Union Finds, Linear Programming, Network Flows, NP-Completeness, Randomized Algorithms, Hashing, etc. The textbook for this course is well written and very suitable as a reference book. In addition, this class has written assignments and is recommended to use LaTeX. You can take this opportunity to practice your LaTeX skills.","title":"Descriptions"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS170/#course-resources","text":"Course Website: https://cs170.org/ Recordings: https://www.youtube.com/playlist?list=PLnocShPlK-Ft-o7NInBDw18be86dNaxlT Recordings: refer to the course website Assignments: refer to the course website","title":"Course Resources"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS170/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/UCB-CS170 - GitHub","title":"Personal Resources"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS61B/","text":"CS61B: Data Structures and Algorithms Descriptions Offered by: UC Berkeley Prerequisites: CS61A Programming Languages: Java Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 60 hours It is the second course of UC Berkeley's CS61 series. It mainly focuses on the design of data structures and algorithms as well as giving students the opportunity to be exposed to thousands of lines of engineering code and gain a preliminary understanding of software engineering through Java. I took the version for 2018 Spring. Josh Hug, the instructor, generously made the autograder open-source. You can use gradescope invitation code published on the website for free and easily test your implementation. All programming assignments in this course are done in Java. Students without Java experience don't have to worry. There will be detailed tutorials in the course from the configuration of IDEA to the core syntax and features of Java. The quality of homework in this class is also unparalleled. The 14 labs will allow you to implement most of the data structures mentioned in the class by yourself, and the 10 homework will allow you to use data structures and algorithms to solve practical problems. In addition, there are 3 projects that give you the opportunity to be exposed to thousands of lines of engineering code and enhance your Java skills in practice. Resources Course Resources Course Website: https://sp18.datastructur.es/ Recordings: refer to the course website Textbook: None Assignments: Slightly different every year. In the spring semester of 2018, there are 14 Labs, 10 Homeworks and 3 Projects. Please refer to the course website for specific requirements. Personal resources All resources and homework implementations used by @PKUFlyingPig in this course are summarized in PKUFlyingPig/CS61B - GitHub .","title":"UCB CS61B: Data Structures and Algorithms"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS61B/#cs61b-data-structures-and-algorithms","text":"","title":"CS61B: Data Structures and Algorithms"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS61B/#descriptions","text":"Offered by: UC Berkeley Prerequisites: CS61A Programming Languages: Java Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 60 hours It is the second course of UC Berkeley's CS61 series. It mainly focuses on the design of data structures and algorithms as well as giving students the opportunity to be exposed to thousands of lines of engineering code and gain a preliminary understanding of software engineering through Java. I took the version for 2018 Spring. Josh Hug, the instructor, generously made the autograder open-source. You can use gradescope invitation code published on the website for free and easily test your implementation. All programming assignments in this course are done in Java. Students without Java experience don't have to worry. There will be detailed tutorials in the course from the configuration of IDEA to the core syntax and features of Java. The quality of homework in this class is also unparalleled. The 14 labs will allow you to implement most of the data structures mentioned in the class by yourself, and the 10 homework will allow you to use data structures and algorithms to solve practical problems. In addition, there are 3 projects that give you the opportunity to be exposed to thousands of lines of engineering code and enhance your Java skills in practice.","title":"Descriptions"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS61B/#resources","text":"","title":"Resources"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS61B/#course-resources","text":"Course Website: https://sp18.datastructur.es/ Recordings: refer to the course website Textbook: None Assignments: Slightly different every year. In the spring semester of 2018, there are 14 Labs, 10 Homeworks and 3 Projects. Please refer to the course website for specific requirements.","title":"Course Resources"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS61B/#personal-resources","text":"All resources and homework implementations used by @PKUFlyingPig in this course are summarized in PKUFlyingPig/CS61B - GitHub .","title":"Personal resources"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/CS189/","text":"CS189: Introduction to Machine Learning \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS188, CS70 \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 \u8fd9\u95e8\u8bfe\u6211\u6ca1\u6709\u7cfb\u7edf\u4e0a\u8fc7\uff0c\u53ea\u662f\u628a\u5b83\u7684\u8bfe\u7a0b notes \u4f5c\u4e3a\u5de5\u5177\u4e66\u67e5\u9605\u3002\u4e0d\u8fc7\u4ece\u8bfe\u7a0b\u7f51\u7ad9\u4e0a\u6765\u770b\uff0c\u5b83\u6bd4 CS229 \u597d\u7684\u662f\u5f00\u6e90\u4e86\u6240\u6709 homework \u7684\u4ee3\u7801\u4ee5\u53ca gradescope \u7684 autograder\u3002\u540c\u6837\uff0c\u8fd9\u95e8\u8bfe\u8bb2\u5f97\u76f8\u5f53\u7406\u8bba\u4e14\u6df1\u5165\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://www.eecs189.org/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.youtube.com/playlist?list=PLOOm2AoWIPEyZazQVnIcaK2KnezpGZV-X \u8bfe\u7a0b\u6559\u6750\uff1a https://www.eecs189.org/ \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://www.eecs189.org/","title":"UCB CS189: Introduction to Machine Learning"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/CS189/#cs189-introduction-to-machine-learning","text":"","title":"CS189: Introduction to Machine Learning"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/CS189/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS188, CS70 \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 \u8fd9\u95e8\u8bfe\u6211\u6ca1\u6709\u7cfb\u7edf\u4e0a\u8fc7\uff0c\u53ea\u662f\u628a\u5b83\u7684\u8bfe\u7a0b notes \u4f5c\u4e3a\u5de5\u5177\u4e66\u67e5\u9605\u3002\u4e0d\u8fc7\u4ece\u8bfe\u7a0b\u7f51\u7ad9\u4e0a\u6765\u770b\uff0c\u5b83\u6bd4 CS229 \u597d\u7684\u662f\u5f00\u6e90\u4e86\u6240\u6709 homework \u7684\u4ee3\u7801\u4ee5\u53ca gradescope \u7684 autograder\u3002\u540c\u6837\uff0c\u8fd9\u95e8\u8bfe\u8bb2\u5f97\u76f8\u5f53\u7406\u8bba\u4e14\u6df1\u5165\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/CS189/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a https://www.eecs189.org/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.youtube.com/playlist?list=PLOOm2AoWIPEyZazQVnIcaK2KnezpGZV-X \u8bfe\u7a0b\u6559\u6750\uff1a https://www.eecs189.org/ \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://www.eecs189.org/","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/CS229/","text":"CS229: Machine Learning \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u9ad8\u6570\uff0c\u6982\u7387\u8bba\uff0cPython\uff0c\u9700\u8981\u8f83\u6df1\u539a\u7684\u6570\u5b66\u529f\u5e95 \u7f16\u7a0b\u8bed\u8a00\uff1a\u65e0 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 \u540c\u6837\u662f\u5434\u6069\u8fbe\u8bb2\u6388\uff0c\u4f46\u662f\u8fd9\u662f\u4e00\u95e8\u7814\u7a76\u751f\u8bfe\u7a0b\uff0c\u6240\u4ee5\u66f4\u504f\u91cd\u6570\u5b66\u7406\u8bba\uff0c\u4e0d\u6ee1\u8db3\u4e8e\u8c03\u5305\u800c\u60f3\u6df1\u5165\u7406\u89e3\u7b97\u6cd5\u672c\u8d28\uff0c\u6216\u8005\u6709\u5fd7\u4e8e\u4ece\u4e8b\u673a\u5668\u5b66\u4e60\u7406\u8bba\u7814\u7a76\u7684\u540c\u5b66\u53ef\u4ee5\u5b66\u4e60\u8fd9\u95e8\u8bfe\u7a0b\u3002\u8bfe\u7a0b\u7f51\u7ad9\u4e0a\u63d0\u4f9b\u4e86\u6240\u6709\u7684\u8bfe\u7a0b notes\uff0c\u5199\u5f97\u975e\u5e38\u4e13\u4e1a\u4e14\u7406\u8bba\uff0c\u9700\u8981\u4e00\u5b9a\u7684\u6570\u5b66\u529f\u5e95\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://cs229.stanford.edu/syllabus.html \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1JE411w7Ub \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0\uff0c\u8bfe\u7a0b notes \u5199\u5f97\u975e\u5e38\u597d \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u4e0d\u5bf9\u516c\u4f17\u5f00\u653e \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS229 - GitHub \u4e2d\u3002","title":"Stanford CS229: Machine Learning"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/CS229/#cs229-machine-learning","text":"","title":"CS229: Machine Learning"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/CS229/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u9ad8\u6570\uff0c\u6982\u7387\u8bba\uff0cPython\uff0c\u9700\u8981\u8f83\u6df1\u539a\u7684\u6570\u5b66\u529f\u5e95 \u7f16\u7a0b\u8bed\u8a00\uff1a\u65e0 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 \u540c\u6837\u662f\u5434\u6069\u8fbe\u8bb2\u6388\uff0c\u4f46\u662f\u8fd9\u662f\u4e00\u95e8\u7814\u7a76\u751f\u8bfe\u7a0b\uff0c\u6240\u4ee5\u66f4\u504f\u91cd\u6570\u5b66\u7406\u8bba\uff0c\u4e0d\u6ee1\u8db3\u4e8e\u8c03\u5305\u800c\u60f3\u6df1\u5165\u7406\u89e3\u7b97\u6cd5\u672c\u8d28\uff0c\u6216\u8005\u6709\u5fd7\u4e8e\u4ece\u4e8b\u673a\u5668\u5b66\u4e60\u7406\u8bba\u7814\u7a76\u7684\u540c\u5b66\u53ef\u4ee5\u5b66\u4e60\u8fd9\u95e8\u8bfe\u7a0b\u3002\u8bfe\u7a0b\u7f51\u7ad9\u4e0a\u63d0\u4f9b\u4e86\u6240\u6709\u7684\u8bfe\u7a0b notes\uff0c\u5199\u5f97\u975e\u5e38\u4e13\u4e1a\u4e14\u7406\u8bba\uff0c\u9700\u8981\u4e00\u5b9a\u7684\u6570\u5b66\u529f\u5e95\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/CS229/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://cs229.stanford.edu/syllabus.html \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1JE411w7Ub \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0\uff0c\u8bfe\u7a0b notes \u5199\u5f97\u975e\u5e38\u597d \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u4e0d\u5bf9\u516c\u4f17\u5f00\u653e","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/CS229/#_3","text":"@PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS229 - GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/ML/","text":"Coursera: Machine Learning Descriptions Offered by: Stanford Prerequisites: entry level of AI and proficient in Python Programming Languages: Python Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 100 hours When it comes to Andrew Ng, no one in the AI community should be unaware of him. He is one of the founders of the famous online education platform Coursera , and also a famous professor at Stanford. This introductory machine learning course must be one of his famous works (the other is his deep learning course), and has hundreds of thousands of learners on Coursera (note that these are people who paid for the certificate, which costs several hundred dollars), and the number of nonpaying learners should be far more than that. The class is extremely friendly to novices, and Andrew has the ability to make machine learning as straightforward as 1+1=2. You'll learn about linear regression, logistic regression, support vector machines, unsupervised learning, dimensionality reduction, anomaly detection, and recommender systems, etc. and solidify your understanding with hands-on programming. The quality of the assignments needs no word to say. With detailed code frameworks and practical background, you can use what you've learned to solve real problems. Of course, as a public mooc, the difficulty of this course has been deliberately lowered, and many mathematical derivations are skimmed over. If you are interested in machine learning theory and want to investigate the mathematical theory behind these algorithms, you can refer to CS229 and CS189 . Course Resources Course Website: https://www.coursera.org/learn/machine-learning Recordings: refer to the course website Textbook: None Assignments: refer to the course website Personal Resources My implementation is lost in system reinstallation. However, the course is so famous that you can easily find related resoures online. Also, course material is available on Coursera.","title":"Coursera: Machine Learning"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/ML/#coursera-machine-learning","text":"","title":"Coursera: Machine Learning"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/ML/#descriptions","text":"Offered by: Stanford Prerequisites: entry level of AI and proficient in Python Programming Languages: Python Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 100 hours When it comes to Andrew Ng, no one in the AI community should be unaware of him. He is one of the founders of the famous online education platform Coursera , and also a famous professor at Stanford. This introductory machine learning course must be one of his famous works (the other is his deep learning course), and has hundreds of thousands of learners on Coursera (note that these are people who paid for the certificate, which costs several hundred dollars), and the number of nonpaying learners should be far more than that. The class is extremely friendly to novices, and Andrew has the ability to make machine learning as straightforward as 1+1=2. You'll learn about linear regression, logistic regression, support vector machines, unsupervised learning, dimensionality reduction, anomaly detection, and recommender systems, etc. and solidify your understanding with hands-on programming. The quality of the assignments needs no word to say. With detailed code frameworks and practical background, you can use what you've learned to solve real problems. Of course, as a public mooc, the difficulty of this course has been deliberately lowered, and many mathematical derivations are skimmed over. If you are interested in machine learning theory and want to investigate the mathematical theory behind these algorithms, you can refer to CS229 and CS189 .","title":"Descriptions"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/ML/#course-resources","text":"Course Website: https://www.coursera.org/learn/machine-learning Recordings: refer to the course website Textbook: None Assignments: refer to the course website","title":"Course Resources"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/ML/#personal-resources","text":"My implementation is lost in system reinstallation. However, the course is so famous that you can easily find related resoures online. Also, course material is available on Coursera.","title":"Personal Resources"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%B3%BB%E7%BB%9F/CMU10-414/","text":"CMU 10-414/714: Deep Learning Systems \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aCMU \u5148\u4fee\u8981\u6c42\uff1a\u7cfb\u7edf\u5165\u95e8(eg.15-213)\u3001\u6df1\u5ea6\u5b66\u4e60\u5165\u95e8\u3001\u57fa\u672c\u7684\u6570\u5b66\u77e5\u8bc6 \u7f16\u7a0b\u8bed\u8a00\uff1aN/A\uff08\u636e\u8bfe\u7a0b\u4e3b\u9875\uff0c\u8981\u6c42\u719f\u6089Python\u3001C/C++\uff09 \u8bfe\u7a0b\u96be\u5ea6\uff1aN/A \u9884\u8ba1\u5b66\u65f6\uff1aN/A \u8fd9\u662f CMU 2022\u5e74\u79cb\u5b63\u5b66\u671f\u5f00\u8bbe\u7684\u4e00\u95e8\u65b0\u8bfe\uff0c\u805a\u7126\u4e8e\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u7684\u5177\u4f53\u5b9e\u73b0\uff0c\u8bfe\u7a0b Project \u4f1a\u5b9e\u73b0\u4e00\u4e2a\u8ff7\u4f60\u7684\u7c7b\u4f3c\u4e8e Pytorch 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\u8fd9\u95e8\u8bfe\u662f\u673a\u5668\u5b66\u4e60\u7f16\u8bd1\u9886\u57df\u7684\u9876\u5c16\u5b66\u8005\u9648\u5929\u5947\u57282022\u5e74\u6691\u671f\u5f00\u8bbe\u7684\u4e00\u95e8\u5728\u7ebf\u8bfe\u7a0b\u3002\u5176\u5b9e\u673a\u5668\u5b66\u4e60\u7f16\u8bd1\u65e0\u8bba\u5728\u5de5\u4e1a\u754c\u8fd8\u662f\u5b66\u672f\u754c\u4ecd\u7136\u662f\u4e00\u4e2a\u975e\u5e38\u524d\u6cbf\u4e14\u5feb\u901f\u66f4\u8fed\u7684\u9886\u57df\uff0c\u56fd\u5185\u5916\u6b64\u524d\u8fd8\u6ca1\u6709\u4e3a\u8fd9\u4e2a\u65b9\u5411\u4e13\u95e8\u5f00\u8bbe\u7684\u76f8\u5173\u8bfe\u7a0b\u3002\u56e0\u6b64\u5982\u679c\u5bf9\u673a\u5668\u5b66\u4e60\u7f16\u8bd1\u611f\u5174\u8da3\u60f3\u6709\u4e2a\u5168\u8c8c\u6027\u7684\u611f\u77e5\u7684\u8bdd\uff0c\u53ef\u4ee5\u5b66\u4e60\u4e00\u4e0b\u8fd9\u95e8\u8bfe\u3002 \u672c\u8bfe\u7a0b\u4e3b\u8981\u4ee5 Apache TVM 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Compilation"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%B3%BB%E7%BB%9F/MLC/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aBilibili \u5927\u5b66 \u5148\u4fee\u8981\u6c42\uff1a\u673a\u5668\u5b66\u4e60/\u6df1\u5ea6\u5b66\u4e60\u57fa\u7840 \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a30\u5c0f\u65f6 \u8fd9\u95e8\u8bfe\u662f\u673a\u5668\u5b66\u4e60\u7f16\u8bd1\u9886\u57df\u7684\u9876\u5c16\u5b66\u8005\u9648\u5929\u5947\u57282022\u5e74\u6691\u671f\u5f00\u8bbe\u7684\u4e00\u95e8\u5728\u7ebf\u8bfe\u7a0b\u3002\u5176\u5b9e\u673a\u5668\u5b66\u4e60\u7f16\u8bd1\u65e0\u8bba\u5728\u5de5\u4e1a\u754c\u8fd8\u662f\u5b66\u672f\u754c\u4ecd\u7136\u662f\u4e00\u4e2a\u975e\u5e38\u524d\u6cbf\u4e14\u5feb\u901f\u66f4\u8fed\u7684\u9886\u57df\uff0c\u56fd\u5185\u5916\u6b64\u524d\u8fd8\u6ca1\u6709\u4e3a\u8fd9\u4e2a\u65b9\u5411\u4e13\u95e8\u5f00\u8bbe\u7684\u76f8\u5173\u8bfe\u7a0b\u3002\u56e0\u6b64\u5982\u679c\u5bf9\u673a\u5668\u5b66\u4e60\u7f16\u8bd1\u611f\u5174\u8da3\u60f3\u6709\u4e2a\u5168\u8c8c\u6027\u7684\u611f\u77e5\u7684\u8bdd\uff0c\u53ef\u4ee5\u5b66\u4e60\u4e00\u4e0b\u8fd9\u95e8\u8bfe\u3002 \u672c\u8bfe\u7a0b\u4e3b\u8981\u4ee5 Apache TVM 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\u76f8\u5173\u7684\u7f16\u7a0b\u5f00\u53d1\u7684\u8bdd\uff0c\u8fd9\u95e8\u8bfe\u6709\u4e30\u5bcc\u4e14\u89c4\u8303\u7684\u4ee3\u7801\u793a\u4f8b\u4ee5\u4f9b\u53c2\u8003\u3002 \u6240\u6709\u7684\u8bfe\u7a0b\u8d44\u6e90\u5168\u90e8\u5f00\u6e90\u5e76\u4e14\u6709\u4e2d\u6587\u548c\u82f1\u6587\u4e24\u4e2a\u7248\u672c\uff0cB\u7ad9\u548c\u6cb9\u7ba1\u5206\u522b\u6709\u4e2d\u6587\u548c\u82f1\u6587\u7684\u8bfe\u7a0b\u5f55\u5f71\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%B3%BB%E7%BB%9F/MLC/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a https://mlc.ai/summer22-zh/ \u8bfe\u7a0b\u89c6\u9891\uff1a Bilibili \u8bfe\u7a0b\u7b14\u8bb0\uff1a https://mlc.ai/zh/index.html \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://github.com/mlc-ai/notebooks/blob/main/assignment","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CMU10-708/","text":"CMU 10-708: Probabilistic Graphical Models \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aCMU \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Reinforcement Learning \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://sailinglab.github.io/pgm-spring-2019/ \u8fd9\u4e2a\u7f51\u7ad9\u5305\u542b\u4e86\u6240\u6709\u7684\u8d44\u6e90\uff1aslides, nots, video, homework, project \u8fd9\u95e8\u8bfe\u7a0b\u662f CMU \u7684\u56fe\u6a21\u578b\u57fa\u7840 + \u8fdb\u9636\u8bfe\uff0c\u6388\u8bfe\u8001\u5e08\u4e3a Eric P. Xing\uff0c\u6db5\u76d6\u4e86\u56fe\u6a21\u578b\u57fa\u7840\uff0c\u4e0e\u795e\u7ecf\u7f51\u7edc\u7684\u7ed3\u5408\uff0c\u5728\u5f3a\u5316\u5b66\u4e60\u4e2d\u7684\u5e94\u7528\uff0c\u4ee5\u53ca\u975e\u53c2\u6570\u65b9\u6cd5\u3002\u76f8\u5f53\u786c\u6838","title":"CMU 10-708: Probabilistic Graphical Models"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CMU10-708/#cmu-10-708-probabilistic-graphical-models","text":"","title":"CMU 10-708: Probabilistic Graphical Models"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CMU10-708/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aCMU \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Reinforcement Learning \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://sailinglab.github.io/pgm-spring-2019/ \u8fd9\u4e2a\u7f51\u7ad9\u5305\u542b\u4e86\u6240\u6709\u7684\u8d44\u6e90\uff1aslides, nots, video, homework, project \u8fd9\u95e8\u8bfe\u7a0b\u662f CMU \u7684\u56fe\u6a21\u578b\u57fa\u7840 + \u8fdb\u9636\u8bfe\uff0c\u6388\u8bfe\u8001\u5e08\u4e3a Eric P. Xing\uff0c\u6db5\u76d6\u4e86\u56fe\u6a21\u578b\u57fa\u7840\uff0c\u4e0e\u795e\u7ecf\u7f51\u7edc\u7684\u7ed3\u5408\uff0c\u5728\u5f3a\u5316\u5b66\u4e60\u4e2d\u7684\u5e94\u7528\uff0c\u4ee5\u53ca\u975e\u53c2\u6570\u65b9\u6cd5\u3002\u76f8\u5f53\u786c\u6838","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CS229M/","text":"STATS214 / CS229M: Machine Learning Theory \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Statistics \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/stats214/ \u7ecf\u5178\u5b66\u4e60\u7406\u8bba + \u6700\u65b0\u6df1\u5ea6\u5b66\u4e60\u7406\u8bba\uff0c\u975e\u5e38\u786c\u6838\u3002\u6388\u8bfe\u8001\u5e08\u4e4b\u524d\u662f Percy Liang\uff0c\u73b0\u5728\u662f Tengyu Ma","title":"Stanford STATS214 / CS229M: Machine Learning Theory"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CS229M/#stats214-cs229m-machine-learning-theory","text":"","title":"STATS214 / CS229M: Machine Learning Theory"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CS229M/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Statistics \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/stats214/ \u7ecf\u5178\u5b66\u4e60\u7406\u8bba + \u6700\u65b0\u6df1\u5ea6\u5b66\u4e60\u7406\u8bba\uff0c\u975e\u5e38\u786c\u6838\u3002\u6388\u8bfe\u8001\u5e08\u4e4b\u524d\u662f Percy Liang\uff0c\u73b0\u5728\u662f Tengyu Ma","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STA4273/","text":"STA 4273 Winter 2021: Minimizing Expectations \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aU Toronto \u5148\u4fee\u8981\u6c42\uff1aBayesian Inference, Reinforcement Learning \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://www.cs.toronto.edu/~cmaddis/courses/sta4273_w21/ \u8fd9\u662f\u4e00\u95e8\u8f83\u4e3a\u8fdb\u9636\u7684 Ph.D. \u7814\u7a76\u8bfe\u7a0b\uff0c\u6838\u5fc3\u5185\u5bb9\u662f inference \u548c control \u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u6388\u8bfe\u8001\u5e08\u4e3a Chris Maddison (AlphaGo founding member, NeurIPS 14 best paper)","title":"U Toronto STA 4273 Winter 2021: Minimizing Expectations"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STA4273/#sta-4273-winter-2021-minimizing-expectations","text":"","title":"STA 4273 Winter 2021: Minimizing Expectations"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STA4273/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aU Toronto \u5148\u4fee\u8981\u6c42\uff1aBayesian Inference, Reinforcement Learning \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://www.cs.toronto.edu/~cmaddis/courses/sta4273_w21/ \u8fd9\u662f\u4e00\u95e8\u8f83\u4e3a\u8fdb\u9636\u7684 Ph.D. \u7814\u7a76\u8bfe\u7a0b\uff0c\u6838\u5fc3\u5185\u5bb9\u662f inference \u548c control \u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u6388\u8bfe\u8001\u5e08\u4e3a Chris Maddison (AlphaGo founding member, NeurIPS 14 best paper)","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STAT8201/","text":"Columbia STAT 8201: Deep Generative Models \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aColumbia University \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Graphical Models \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://stat.columbia.edu/~cunningham/teaching/GR8201/ \u8fd9\u95e8\u8bfe\u662f\u4e00\u95e8 PhD \u8ba8\u8bba\u73ed\uff0c\u6bcf\u5468\u7684\u5185\u5bb9\u662f\u5c55\u793a + \u8ba8\u8bba\u8bba\u6587\uff0c\u6388\u8bfe\u8001\u5e08\u662f John Cunningham\u3002Deep Generative Models \uff08\u6df1\u5ea6\u751f\u6210\u6a21\u578b\uff09 \u662f\u56fe\u6a21\u578b\u4e0e\u795e\u7ecf\u7f51\u7edc\u7684\u7ed3\u5408\uff0c\u4e5f\u662f\u73b0\u4ee3\u673a\u5668\u5b66\u4e60\u6700\u91cd\u8981\u7684\u65b9\u5411\u4e4b\u4e00","title":"Columbia STAT 8201: Deep Generative Models"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STAT8201/#columbia-stat-8201-deep-generative-models","text":"","title":"Columbia STAT 8201: Deep Generative Models"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STAT8201/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aColumbia University \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Graphical Models \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://stat.columbia.edu/~cunningham/teaching/GR8201/ \u8fd9\u95e8\u8bfe\u662f\u4e00\u95e8 PhD \u8ba8\u8bba\u73ed\uff0c\u6bcf\u5468\u7684\u5185\u5bb9\u662f\u5c55\u793a + \u8ba8\u8bba\u8bba\u6587\uff0c\u6388\u8bfe\u8001\u5e08\u662f John Cunningham\u3002Deep Generative Models \uff08\u6df1\u5ea6\u751f\u6210\u6a21\u578b\uff09 \u662f\u56fe\u6a21\u578b\u4e0e\u795e\u7ecf\u7f51\u7edc\u7684\u7ed3\u5408\uff0c\u4e5f\u662f\u73b0\u4ee3\u673a\u5668\u5b66\u4e60\u6700\u91cd\u8981\u7684\u65b9\u5411\u4e4b\u4e00","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/","text":"\u673a\u5668\u5b66\u4e60\u8fdb\u9636 \u6b64\u8def\u7ebf\u56fe\u9002\u7528\u4e8e\u5df2\u7ecf\u5b66\u8fc7\u4e86\u57fa\u7840\u673a\u5668\u5b66\u4e60 (ML, NLP, CV, RL) \u7684\u540c\u5b66 (\u9ad8\u5e74\u7ea7\u672c\u79d1\u751f\u6216\u4f4e\u5e74\u7ea7\u7814\u7a76\u751f)\uff0c\u5df2\u7ecf\u53d1\u8868\u8fc7\u81f3\u5c11\u4e00\u7bc7\u9876\u4f1a\u8bba\u6587 (NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, CVPR, ICCV) \u60f3\u8981\u8d70\u673a\u5668\u5b66\u4e60\u79d1\u7814\u8def\u7ebf\u7684\u9009\u624b\u3002 \u6b64\u8def\u7ebf\u7684\u76ee\u6807\u662f\u4e3a\u8bfb\u61c2\u4e0e\u53d1\u8868\u673a\u5668\u5b66\u4e60\u9876\u4f1a\u8bba\u6587\u6253\u4e0b\u7406\u8bba\u57fa\u7840\uff0c\u7279\u522b\u662f Probabilistic Methods \u8fd9\u4e2a track \u4e0b\u7684\u6587\u7ae0 \u673a\u5668\u5b66\u4e60\u8fdb\u9636\u53ef\u80fd\u5b58\u5728\u591a\u79cd\u4e0d\u540c\u7684\u5b66\u4e60\u8def\u7ebf\uff0c\u6b64\u8def\u7ebf\u53ea\u80fd\u4ee3\u8868\u4f5c\u8005 Yao Fu \u6240\u7406\u89e3\u7684\u6700\u4f73\u8def\u5f84\uff0c\u4fa7\u91cd\u4e8e\u8d1d\u53f6\u65af\u5b66\u6d3e\u4e0b\u7684\u6982\u7387\u5efa\u6a21\u65b9\u6cd5\uff0c\u4e5f\u4f1a\u6d89\u53ca\u5230\u5404\u9879\u76f8\u5173\u5b66\u79d1\u7684\u4ea4\u53c9\u77e5\u8bc6\u3002 \u5fc5\u8bfb\u6559\u6750 PRML: Pattern Recognition and Machine Learning. Christopher Bishop \u7ecf\u5178\u8d1d\u53f6\u65af\u5b66\u6d3e\u6559\u6750 AoS: All of Statistics. Larry Wasserman \u7ecf\u5178\u9891\u7387\u5b66\u6d3e\u6559\u6750 \u6240\u4ee5\u8fd9\u4e24\u672c\u4e66\u521a\u597d\u76f8\u8f85\u76f8\u6210 \u5b57\u5178 MLAPP: Machine Learning: A Probabilistic Perspective. Kevin Murphy Convex Optimization. Stephen Boyd and Lieven Vandenberghe \u8fdb\u9636\u4e66\u7c4d W&J: Graphical Models, Exponential Families, and Variational Inference. Martin Wainwright and Michael Jordan Theory of Point Estimation. E. L. Lehmann and George Casella \u5982\u4f55\u9605\u8bfb Guidelines \u5fc5\u8bfb\u6559\u6750\u5c31\u662f\u4e00\u5b9a\u8981\u8bfb\u7684\u6559\u6750 \u5b57\u5178\u7684\u610f\u601d\u662f\uff0c\u4e00\u822c\u60c5\u51b5\u4e0b\u4e0d\u7ba1\u5b83\uff0c\u4f46\u5f53\u9047\u5230\u4e86\u4e0d\u61c2\u7684\u6982\u5ff5\u7684\u65f6\u5019\uff0c\u5c31\u53bb\u5b57\u5178\u91cc\u9762\u67e5\uff08\u800c\u4e0d\u662f\u7ef4\u57fa\u767e\u79d1\uff09 \u8fdb\u9636\u4e66\u7c4d\u5148\u4e0d\u8bfb\uff0c\u5148\u8bfb\u5b8c\u5fc5\u8bfb\u4e66\u7c4d\u3002\u5fc5\u8bfb\u4e66\u7c4d\u4e00\u822c\u90fd\u662f\u8981\u524d\u524d\u540e\u540e\u53cd\u590d\u770b\u8fc7 N \u904d\u624d\u7b97\u8bfb\u5b8c \u8bfb\u7684\u8fc7\u7a0b\u4e2d\uff0c\u6700\u91cd\u8981\u7684\u8bfb\u6cd5\u5c31\u662f\u5bf9\u6bd4\u9605\u8bfb (contrastive-comparative reading)\uff1a\u540c\u65f6\u6253\u5f00\u4e24\u672c\u4e66\u8bb2\u540c\u4e00\u4e3b\u9898\u7684\u7ae0\u8282\uff0c\u7136\u540e\u5bf9\u6bd4\u76f8\u540c\u70b9\u548c\u4e0d\u540c\u70b9\u548c\u8054\u7cfb \u8bfb\u7684\u8fc7\u7a0b\u4e2d\uff0c\u5c3d\u91cf\u53bb\u56de\u60f3\u4e4b\u524d\u8bfb\u8fc7\u7684\u8bba\u6587\uff0c\u6bd4\u8f83\u8bba\u6587\u548c\u6559\u6750\u7684\u76f8\u540c\u70b9\u4e0e\u4e0d\u540c\u70b9 \u57fa\u7840\u8def\u5f84 \u5148\u8bfb AoS \u7b2c\u516d\u7ae0: Models, Statistical Inference and Learning\uff0c\u8fd9\u4e00\u90e8\u5206\u662f\u6700\u57fa\u7840\u7684\u79d1\u666e \u7136\u540e\u8bfb PRML \u7b2c 10, 11 \u7ae0 \u7b2c 10 \u7ae0\u7684\u5185\u5bb9\u662f Variational Inference, \u7b2c 11 \u7ae0\u7684\u5185\u5bb9\u662f MCMC, \u8fd9\u4e24\u79cd\u65b9\u6cd5\u662f\u8d1d\u53f6\u65af\u63a8\u65ad\u7684\u4e24\u6761\u6700\u4e3b\u8981\u8def\u7ebf \u5982\u679c\u5728\u8bfb PRML \u7684\u8fc7\u7a0b\u4e2d\u53d1\u73b0\u6709\u4efb\u4f55\u4e0d\u61c2\u7684\u540d\u8bcd\uff0c\u5c31\u53bb\u7ffb\u524d\u9762\u7684\u7ae0\u8282\u3002\u5f88\u5927\u6982\u7387\u80fd\u591f\u5728\u7b2c 3\uff0c4 \u7ae0\u627e\u5230\u76f8\u5bf9\u5e94\u7684\u5b9a\u4e49\uff1b\u5982\u679c\u627e\u4e0d\u5230\u6216\u8005\u4e0d\u591f\u8be6\u7ec6\uff0c\u5c31\u53bb\u67e5 MLAPP AoS \u7b2c 8 \u7ae0 (Parametric Inference) \u548c\u7b2c 11 \u7ae0 (Bayesian Inference) \u4e5f\u53ef\u4ee5\u4f5c\u4e3a\u53c2\u8003\u3002\u6700\u597d\u7684\u65b9\u6cd5\u662f\u591a\u672c\u4e66\u5bf9\u6bd4\u9605\u8bfb\uff0c\u6d41\u7a0b\u5982\u4e0b \u5047\u8bbe\u6211\u5728\u8bfb PRML \u7b2c 10 \u7ae0\u7684\u65f6\u5019\u53d1\u73b0\u4e86\u4e00\u4e2a\u4e0d\u61c2\u7684\u8bcd\uff1aposterior inference \u4e8e\u662f\u6211\u5f80\u524d\u7ffb\uff0c\u7ffb\u5230\u4e86\u7b2c 3 \u7ae0 (Linear Model for Regression)\uff0c\u770b\u5230\u4e86\u6700\u7b80\u5355\u7684 posterior \u7136\u540e\u6211\u63a5\u7740\u7ffb AoS\uff0c\u7ffb\u5230\u4e86\u7b2c 11 \u7ae0\uff0c\u4e5f\u6709\u5bf9 posterior \u7684\u63cf\u8ff0 \u7136\u540e\u6211\u5bf9\u6bd4 PRML \u7b2c 10 \u7ae0\uff0c\u7b2c 3 \u7ae0\uff0cAoS \u7b2c 11 \u7ae0\uff0c\u4e09\u5904\u4e0d\u540c\u5730\u65b9\u5bf9 posterior \u7684\u89e3\u8bfb\uff0c\u6bd4\u8f83\u5176\u76f8\u540c\u70b9\u548c\u4e0d\u540c\u70b9\u548c\u8054\u7cfb \u8bfb\u5b8c PRML \u7b2c 10 \u548c 11 \u7ae0\u4e4b\u540e\uff0c\u63a5\u7740\u8bfb AoS \u7b2c 24 \u7ae0 (Simulation Methods)\uff0c\u7136\u540e\u628a\u5b83\u548c PRML \u7b2c 11 \u7ae0\u5bf9\u6bd4\u9605\u8bfb -- \u8fd9\u4fe9\u90fd\u662f\u8bb2 MCMC \u5982\u679c\u5230\u6b64\u5904\u53d1\u73b0\u8fd8\u6709\u57fa\u7840\u6982\u5ff5\u8bfb\u4e0d\u61c2\uff0c\u5c31\u56de\u5230 PRML \u7b2c 3 \u7ae0\uff0c\u628a\u5b83\u548c AoS \u7b2c 11 \u7ae0\u5bf9\u6bd4\u9605\u8bfb Again\uff0c\u5bf9\u6bd4\u9605\u8bfb\u975e\u5e38\u91cd\u8981\uff0c\u4e00\u5b9a\u8981\u628a\u4e0d\u540c\u672c\u4e66\u7684\u7c7b\u4f3c\u5185\u5bb9\u540c\u65f6\u6446\u5728\u9762\u524d\u76f8\u4e92\u5bf9\u6bd4\uff0c\u8fd9\u6837\u53ef\u4ee5\u663e\u8457\u589e\u5f3a\u8bb0\u5fc6 \u7136\u540e\u8bfb PRML \u7b2c 13 \u7ae0\uff08\u8df3\u8fc7\u7b2c 12 \u7ae0\uff09\uff0c\u8fd9\u4e00\u7ae0\u53ef\u4ee5\u548c MLAPP \u7684\u7b2c 17, 18 \u7ae0\u5bf9\u6bd4\u9605\u8bfb MLAPP \u7b2c 17 \u7ae0\u662f PRML \u7b2c 13.2 \u7ae0\u7684\u8be6\u7ec6\u7248\uff0c\u4e3b\u8981\u8bb2 HMM MLAPP \u7b2c 18 \u7ae0\u662f PRML \u7b2c 13.3 \u7ae0\u7684\u8be6\u7ec6\u7248\uff0c\u4e3b\u8981\u8bb2 LDS \u8bfb\u5b8c PRML \u7b2c 13 \u7ae0\u4e4b\u540e\uff0c\u518d\u53bb\u8bfb PRML \u7b2c 8 \u7ae0 (Graphical Models) -- \u6b64\u65f6\u8fd9\u90e8\u5206\u5e94\u8be5\u4f1a\u8bfb\u5f97\u5f88\u8f7b\u677e \u4ee5\u4e0a\u7684\u5185\u5bb9\u53ef\u4ee5\u8fdb\u4e00\u6b65\u5bf9\u7167 CMU 10-708 PGM \u8bfe\u7a0b\u6750\u6599 \u5230\u76ee\u524d\u4e3a\u6b62\uff0c\u5e94\u8be5\u80fd\u591f\u638c\u63e1 \u6982\u7387\u6a21\u578b\u7684\u57fa\u7840\u5b9a\u4e49 \u7cbe\u51c6\u63a8\u65ad - Sum-Product \u8fd1\u4f3c\u63a8\u65ad - MCMC \u8fd1\u4f3c\u63a8\u65ad - VI \u7136\u540e\u5c31\u53ef\u4ee5\u53bb\u505a\u66f4\u8fdb\u9636\u7684\u5185\u5bb9","title":"\u8fdb\u9636\u8def\u7ebf\u56fe"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_1","text":"\u6b64\u8def\u7ebf\u56fe\u9002\u7528\u4e8e\u5df2\u7ecf\u5b66\u8fc7\u4e86\u57fa\u7840\u673a\u5668\u5b66\u4e60 (ML, NLP, CV, RL) \u7684\u540c\u5b66 (\u9ad8\u5e74\u7ea7\u672c\u79d1\u751f\u6216\u4f4e\u5e74\u7ea7\u7814\u7a76\u751f)\uff0c\u5df2\u7ecf\u53d1\u8868\u8fc7\u81f3\u5c11\u4e00\u7bc7\u9876\u4f1a\u8bba\u6587 (NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, CVPR, ICCV) \u60f3\u8981\u8d70\u673a\u5668\u5b66\u4e60\u79d1\u7814\u8def\u7ebf\u7684\u9009\u624b\u3002 \u6b64\u8def\u7ebf\u7684\u76ee\u6807\u662f\u4e3a\u8bfb\u61c2\u4e0e\u53d1\u8868\u673a\u5668\u5b66\u4e60\u9876\u4f1a\u8bba\u6587\u6253\u4e0b\u7406\u8bba\u57fa\u7840\uff0c\u7279\u522b\u662f Probabilistic Methods \u8fd9\u4e2a track \u4e0b\u7684\u6587\u7ae0 \u673a\u5668\u5b66\u4e60\u8fdb\u9636\u53ef\u80fd\u5b58\u5728\u591a\u79cd\u4e0d\u540c\u7684\u5b66\u4e60\u8def\u7ebf\uff0c\u6b64\u8def\u7ebf\u53ea\u80fd\u4ee3\u8868\u4f5c\u8005 Yao Fu \u6240\u7406\u89e3\u7684\u6700\u4f73\u8def\u5f84\uff0c\u4fa7\u91cd\u4e8e\u8d1d\u53f6\u65af\u5b66\u6d3e\u4e0b\u7684\u6982\u7387\u5efa\u6a21\u65b9\u6cd5\uff0c\u4e5f\u4f1a\u6d89\u53ca\u5230\u5404\u9879\u76f8\u5173\u5b66\u79d1\u7684\u4ea4\u53c9\u77e5\u8bc6\u3002","title":"\u673a\u5668\u5b66\u4e60\u8fdb\u9636"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_2","text":"PRML: Pattern Recognition and Machine Learning. Christopher Bishop \u7ecf\u5178\u8d1d\u53f6\u65af\u5b66\u6d3e\u6559\u6750 AoS: All of Statistics. Larry Wasserman \u7ecf\u5178\u9891\u7387\u5b66\u6d3e\u6559\u6750 \u6240\u4ee5\u8fd9\u4e24\u672c\u4e66\u521a\u597d\u76f8\u8f85\u76f8\u6210","title":"\u5fc5\u8bfb\u6559\u6750"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_3","text":"MLAPP: Machine Learning: A Probabilistic Perspective. Kevin Murphy Convex Optimization. Stephen Boyd and Lieven Vandenberghe","title":"\u5b57\u5178"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_4","text":"W&J: Graphical Models, Exponential Families, and Variational Inference. Martin Wainwright and Michael Jordan Theory of Point Estimation. E. L. Lehmann and George Casella","title":"\u8fdb\u9636\u4e66\u7c4d"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_5","text":"","title":"\u5982\u4f55\u9605\u8bfb"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#guidelines","text":"\u5fc5\u8bfb\u6559\u6750\u5c31\u662f\u4e00\u5b9a\u8981\u8bfb\u7684\u6559\u6750 \u5b57\u5178\u7684\u610f\u601d\u662f\uff0c\u4e00\u822c\u60c5\u51b5\u4e0b\u4e0d\u7ba1\u5b83\uff0c\u4f46\u5f53\u9047\u5230\u4e86\u4e0d\u61c2\u7684\u6982\u5ff5\u7684\u65f6\u5019\uff0c\u5c31\u53bb\u5b57\u5178\u91cc\u9762\u67e5\uff08\u800c\u4e0d\u662f\u7ef4\u57fa\u767e\u79d1\uff09 \u8fdb\u9636\u4e66\u7c4d\u5148\u4e0d\u8bfb\uff0c\u5148\u8bfb\u5b8c\u5fc5\u8bfb\u4e66\u7c4d\u3002\u5fc5\u8bfb\u4e66\u7c4d\u4e00\u822c\u90fd\u662f\u8981\u524d\u524d\u540e\u540e\u53cd\u590d\u770b\u8fc7 N \u904d\u624d\u7b97\u8bfb\u5b8c \u8bfb\u7684\u8fc7\u7a0b\u4e2d\uff0c\u6700\u91cd\u8981\u7684\u8bfb\u6cd5\u5c31\u662f\u5bf9\u6bd4\u9605\u8bfb (contrastive-comparative reading)\uff1a\u540c\u65f6\u6253\u5f00\u4e24\u672c\u4e66\u8bb2\u540c\u4e00\u4e3b\u9898\u7684\u7ae0\u8282\uff0c\u7136\u540e\u5bf9\u6bd4\u76f8\u540c\u70b9\u548c\u4e0d\u540c\u70b9\u548c\u8054\u7cfb \u8bfb\u7684\u8fc7\u7a0b\u4e2d\uff0c\u5c3d\u91cf\u53bb\u56de\u60f3\u4e4b\u524d\u8bfb\u8fc7\u7684\u8bba\u6587\uff0c\u6bd4\u8f83\u8bba\u6587\u548c\u6559\u6750\u7684\u76f8\u540c\u70b9\u4e0e\u4e0d\u540c\u70b9","title":"Guidelines"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_6","text":"\u5148\u8bfb AoS \u7b2c\u516d\u7ae0: Models, Statistical Inference and Learning\uff0c\u8fd9\u4e00\u90e8\u5206\u662f\u6700\u57fa\u7840\u7684\u79d1\u666e \u7136\u540e\u8bfb PRML \u7b2c 10, 11 \u7ae0 \u7b2c 10 \u7ae0\u7684\u5185\u5bb9\u662f Variational Inference, \u7b2c 11 \u7ae0\u7684\u5185\u5bb9\u662f MCMC, \u8fd9\u4e24\u79cd\u65b9\u6cd5\u662f\u8d1d\u53f6\u65af\u63a8\u65ad\u7684\u4e24\u6761\u6700\u4e3b\u8981\u8def\u7ebf \u5982\u679c\u5728\u8bfb PRML \u7684\u8fc7\u7a0b\u4e2d\u53d1\u73b0\u6709\u4efb\u4f55\u4e0d\u61c2\u7684\u540d\u8bcd\uff0c\u5c31\u53bb\u7ffb\u524d\u9762\u7684\u7ae0\u8282\u3002\u5f88\u5927\u6982\u7387\u80fd\u591f\u5728\u7b2c 3\uff0c4 \u7ae0\u627e\u5230\u76f8\u5bf9\u5e94\u7684\u5b9a\u4e49\uff1b\u5982\u679c\u627e\u4e0d\u5230\u6216\u8005\u4e0d\u591f\u8be6\u7ec6\uff0c\u5c31\u53bb\u67e5 MLAPP AoS \u7b2c 8 \u7ae0 (Parametric Inference) \u548c\u7b2c 11 \u7ae0 (Bayesian Inference) \u4e5f\u53ef\u4ee5\u4f5c\u4e3a\u53c2\u8003\u3002\u6700\u597d\u7684\u65b9\u6cd5\u662f\u591a\u672c\u4e66\u5bf9\u6bd4\u9605\u8bfb\uff0c\u6d41\u7a0b\u5982\u4e0b \u5047\u8bbe\u6211\u5728\u8bfb PRML \u7b2c 10 \u7ae0\u7684\u65f6\u5019\u53d1\u73b0\u4e86\u4e00\u4e2a\u4e0d\u61c2\u7684\u8bcd\uff1aposterior inference \u4e8e\u662f\u6211\u5f80\u524d\u7ffb\uff0c\u7ffb\u5230\u4e86\u7b2c 3 \u7ae0 (Linear Model for Regression)\uff0c\u770b\u5230\u4e86\u6700\u7b80\u5355\u7684 posterior \u7136\u540e\u6211\u63a5\u7740\u7ffb AoS\uff0c\u7ffb\u5230\u4e86\u7b2c 11 \u7ae0\uff0c\u4e5f\u6709\u5bf9 posterior \u7684\u63cf\u8ff0 \u7136\u540e\u6211\u5bf9\u6bd4 PRML \u7b2c 10 \u7ae0\uff0c\u7b2c 3 \u7ae0\uff0cAoS \u7b2c 11 \u7ae0\uff0c\u4e09\u5904\u4e0d\u540c\u5730\u65b9\u5bf9 posterior \u7684\u89e3\u8bfb\uff0c\u6bd4\u8f83\u5176\u76f8\u540c\u70b9\u548c\u4e0d\u540c\u70b9\u548c\u8054\u7cfb \u8bfb\u5b8c PRML \u7b2c 10 \u548c 11 \u7ae0\u4e4b\u540e\uff0c\u63a5\u7740\u8bfb AoS \u7b2c 24 \u7ae0 (Simulation Methods)\uff0c\u7136\u540e\u628a\u5b83\u548c PRML \u7b2c 11 \u7ae0\u5bf9\u6bd4\u9605\u8bfb -- \u8fd9\u4fe9\u90fd\u662f\u8bb2 MCMC \u5982\u679c\u5230\u6b64\u5904\u53d1\u73b0\u8fd8\u6709\u57fa\u7840\u6982\u5ff5\u8bfb\u4e0d\u61c2\uff0c\u5c31\u56de\u5230 PRML \u7b2c 3 \u7ae0\uff0c\u628a\u5b83\u548c AoS \u7b2c 11 \u7ae0\u5bf9\u6bd4\u9605\u8bfb Again\uff0c\u5bf9\u6bd4\u9605\u8bfb\u975e\u5e38\u91cd\u8981\uff0c\u4e00\u5b9a\u8981\u628a\u4e0d\u540c\u672c\u4e66\u7684\u7c7b\u4f3c\u5185\u5bb9\u540c\u65f6\u6446\u5728\u9762\u524d\u76f8\u4e92\u5bf9\u6bd4\uff0c\u8fd9\u6837\u53ef\u4ee5\u663e\u8457\u589e\u5f3a\u8bb0\u5fc6 \u7136\u540e\u8bfb PRML \u7b2c 13 \u7ae0\uff08\u8df3\u8fc7\u7b2c 12 \u7ae0\uff09\uff0c\u8fd9\u4e00\u7ae0\u53ef\u4ee5\u548c MLAPP \u7684\u7b2c 17, 18 \u7ae0\u5bf9\u6bd4\u9605\u8bfb MLAPP \u7b2c 17 \u7ae0\u662f PRML \u7b2c 13.2 \u7ae0\u7684\u8be6\u7ec6\u7248\uff0c\u4e3b\u8981\u8bb2 HMM MLAPP \u7b2c 18 \u7ae0\u662f PRML \u7b2c 13.3 \u7ae0\u7684\u8be6\u7ec6\u7248\uff0c\u4e3b\u8981\u8bb2 LDS \u8bfb\u5b8c PRML \u7b2c 13 \u7ae0\u4e4b\u540e\uff0c\u518d\u53bb\u8bfb PRML \u7b2c 8 \u7ae0 (Graphical Models) -- \u6b64\u65f6\u8fd9\u90e8\u5206\u5e94\u8be5\u4f1a\u8bfb\u5f97\u5f88\u8f7b\u677e \u4ee5\u4e0a\u7684\u5185\u5bb9\u53ef\u4ee5\u8fdb\u4e00\u6b65\u5bf9\u7167 CMU 10-708 PGM \u8bfe\u7a0b\u6750\u6599 \u5230\u76ee\u524d\u4e3a\u6b62\uff0c\u5e94\u8be5\u80fd\u591f\u638c\u63e1 \u6982\u7387\u6a21\u578b\u7684\u57fa\u7840\u5b9a\u4e49 \u7cbe\u51c6\u63a8\u65ad - Sum-Product \u8fd1\u4f3c\u63a8\u65ad - MCMC \u8fd1\u4f3c\u63a8\u65ad - VI \u7136\u540e\u5c31\u53ef\u4ee5\u53bb\u505a\u66f4\u8fdb\u9636\u7684\u5185\u5bb9","title":"\u57fa\u7840\u8def\u5f84"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224n/","text":"CS224n: Natural Language Processing \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6df1\u5ea6\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 Stanford \u7684 NLP \u5165\u95e8\u8bfe\u7a0b\uff0c\u7531\u81ea\u7136\u8bed\u8a00\u5904\u7406\u9886\u57df\u7684\u5de8\u4f6c Chris Manning \u9886\u8854\u6559\u6388\uff08word2vec \u7b97\u6cd5\u7684\u5f00\u521b\u8005\uff09\u3002\u5185\u5bb9\u8986\u76d6\u4e86\u8bcd\u5411\u91cf\u3001RNN\u3001LSTM\u3001Seq2Seq \u6a21\u578b\u3001\u673a\u5668\u7ffb\u8bd1\u3001\u6ce8\u610f\u529b\u673a\u5236\u3001Transformer \u7b49\u7b49 NLP \u9886\u57df\u7684\u6838\u5fc3\u77e5\u8bc6\u70b9\u3002 5 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a\u96be\u5ea6\u5faa\u5e8f\u6e10\u8fdb\uff0c\u5206\u522b\u662f\u8bcd\u5411\u91cf\u3001word2vec \u7b97\u6cd5\u3001Dependency parsing\u3001\u673a\u5668\u7ffb\u8bd1\u4ee5\u53ca Transformer \u7684 fine-tune\u3002 \u6700\u7ec8\u7684\u5927\u4f5c\u4e1a\u662f\u5728 Stanford \u8457\u540d\u7684 SQuAD \u6570\u636e\u96c6\u4e0a\u8bad\u7ec3 QA \u6a21\u578b\uff0c\u6709\u5b66\u751f\u7684\u5927\u4f5c\u4e1a\u751a\u81f3\u76f4\u63a5\u53d1\u8868\u4e86\u9876\u4f1a\u8bba\u6587\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs224n/index.html \u8bfe\u7a0b\u89c6\u9891\uff1aB \u7ad9\u641c\u7d22 CS224n \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a http://web.stanford.edu/class/cs224n/index.html \uff0c5 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a + 1 \u4e2a Final Project \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS224n - GitHub \u4e2d\u3002","title":"Stanford CS224n: Natural Language Processing"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224n/#cs224n-natural-language-processing","text":"","title":"CS224n: Natural Language Processing"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224n/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6df1\u5ea6\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 Stanford \u7684 NLP \u5165\u95e8\u8bfe\u7a0b\uff0c\u7531\u81ea\u7136\u8bed\u8a00\u5904\u7406\u9886\u57df\u7684\u5de8\u4f6c Chris Manning \u9886\u8854\u6559\u6388\uff08word2vec \u7b97\u6cd5\u7684\u5f00\u521b\u8005\uff09\u3002\u5185\u5bb9\u8986\u76d6\u4e86\u8bcd\u5411\u91cf\u3001RNN\u3001LSTM\u3001Seq2Seq \u6a21\u578b\u3001\u673a\u5668\u7ffb\u8bd1\u3001\u6ce8\u610f\u529b\u673a\u5236\u3001Transformer \u7b49\u7b49 NLP \u9886\u57df\u7684\u6838\u5fc3\u77e5\u8bc6\u70b9\u3002 5 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a\u96be\u5ea6\u5faa\u5e8f\u6e10\u8fdb\uff0c\u5206\u522b\u662f\u8bcd\u5411\u91cf\u3001word2vec \u7b97\u6cd5\u3001Dependency parsing\u3001\u673a\u5668\u7ffb\u8bd1\u4ee5\u53ca Transformer \u7684 fine-tune\u3002 \u6700\u7ec8\u7684\u5927\u4f5c\u4e1a\u662f\u5728 Stanford \u8457\u540d\u7684 SQuAD \u6570\u636e\u96c6\u4e0a\u8bad\u7ec3 QA \u6a21\u578b\uff0c\u6709\u5b66\u751f\u7684\u5927\u4f5c\u4e1a\u751a\u81f3\u76f4\u63a5\u53d1\u8868\u4e86\u9876\u4f1a\u8bba\u6587\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224n/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs224n/index.html \u8bfe\u7a0b\u89c6\u9891\uff1aB \u7ad9\u641c\u7d22 CS224n \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a http://web.stanford.edu/class/cs224n/index.html \uff0c5 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a + 1 \u4e2a Final Project","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224n/#_3","text":"@PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS224n - GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224w/","text":"CS224w: Machine Learning with Graphs \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6df1\u5ea6\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython, LaTeX \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 Stanford \u7684\u56fe\u795e\u7ecf\u7f51\u7edc\u5165\u95e8\u8bfe\uff0c\u8fd9\u95e8\u8bfe\u6211\u6ca1\u6709\u4e0a\u8fc7\uff0c\u4f46\u4f17\u591a\u505a GNN \u7684\u670b\u53cb\u90fd\u5411\u6211\u529b\u8350\u8fc7\u8fd9\u95e8\u8bfe\uff0c\u60f3\u5fc5 Stanford \u7684\u8bfe\u8d28\u91cf\u8fd8\u662f\u4e00\u5982\u65e2\u5f80\u5730\u6709\u4fdd\u8bc1\u7684\u3002\u53e6\u5916\u5c31\u662f\u8fd9\u95e8\u8bfe\u7684\u6388\u8bfe\u8001\u5e08\u975e\u5e38\u5e74\u8f7b\u5e05\u6c14:) \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs224w/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.youtube.com/watch?v=JAB_plj2rbA \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a http://web.stanford.edu/class/cs224w/ \uff0c6 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a\uff0c3 \u4e2a LaTeX \u4e66\u9762\u4f5c\u4e1a","title":"Stanford CS224w: Machine Learning with Graphs"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224w/#cs224w-machine-learning-with-graphs","text":"","title":"CS224w: Machine Learning with Graphs"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224w/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6df1\u5ea6\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython, LaTeX \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 Stanford \u7684\u56fe\u795e\u7ecf\u7f51\u7edc\u5165\u95e8\u8bfe\uff0c\u8fd9\u95e8\u8bfe\u6211\u6ca1\u6709\u4e0a\u8fc7\uff0c\u4f46\u4f17\u591a\u505a GNN \u7684\u670b\u53cb\u90fd\u5411\u6211\u529b\u8350\u8fc7\u8fd9\u95e8\u8bfe\uff0c\u60f3\u5fc5 Stanford \u7684\u8bfe\u8d28\u91cf\u8fd8\u662f\u4e00\u5982\u65e2\u5f80\u5730\u6709\u4fdd\u8bc1\u7684\u3002\u53e6\u5916\u5c31\u662f\u8fd9\u95e8\u8bfe\u7684\u6388\u8bfe\u8001\u5e08\u975e\u5e38\u5e74\u8f7b\u5e05\u6c14:)","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224w/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs224w/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.youtube.com/watch?v=JAB_plj2rbA \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a http://web.stanford.edu/class/cs224w/ \uff0c6 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a\uff0c3 \u4e2a LaTeX \u4e66\u9762\u4f5c\u4e1a","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS230/","text":"Coursera: Deep Learning \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u673a\u5668\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 \u5434\u6069\u8fbe\u5728 Coursera \u5f00\u8bbe\u7684\u53e6\u4e00\u95e8\u7f51\u7ea2\u8bfe\u7a0b\uff0c\u5b66\u4e60\u8005\u65e0\u6570\uff0c\u582a\u79f0\u5723\u7ecf\u7ea7\u7684\u6df1\u5ea6\u5b66\u4e60\u5165\u95e8\u8bfe\u3002\u6df1\u5165\u6d45\u51fa\u7684\u8bb2\u89e3\uff0c\u773c\u82b1\u7f2d\u4e71\u7684 Project\u3002\u4ece\u6700\u57fa\u7840\u7684\u795e\u7ecf\u7f51\u7edc\uff0c\u5230 CNN, RNN\uff0c\u518d\u5230\u6700\u8fd1\u5927\u70ed\u7684 Transformer\u3002\u5b66\u5b8c\u8fd9\u95e8\u8bfe\uff0c\u4f60\u5c06\u521d\u6b65\u638c\u63e1\u6df1\u5ea6\u5b66\u4e60\u9886\u57df\u5fc5\u5907\u7684\u77e5\u8bc6\u548c\u6280\u80fd\uff0c\u5e76\u4e14\u53ef\u4ee5\u5728 Kaggle \u4e2d\u53c2\u52a0\u81ea\u5df1\u611f\u5174\u8da3\u7684\u6bd4\u8d5b\uff0c\u5728\u5b9e\u8df5\u4e2d\u953b\u70bc\u81ea\u5df1\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://www.coursera.org/specializations/deep-learning \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.coursera.org/specializations/deep-learning \uff0cB\u7ad9\u6709\u642c\u8fd0 \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://www.coursera.org/specializations/deep-learning","title":"Coursera: Deep Learning"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS230/#coursera-deep-learning","text":"","title":"Coursera: Deep Learning"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS230/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u673a\u5668\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 \u5434\u6069\u8fbe\u5728 Coursera 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https://www.coursera.org/specializations/deep-learning \uff0cB\u7ad9\u6709\u642c\u8fd0 \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://www.coursera.org/specializations/deep-learning","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS231/","text":"CS231n: CNN for Visual Recognition \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u673a\u5668\u5b66\u4e60\u57fa\u7840 \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 Stanford \u7684 CV \u5165\u95e8\u8bfe\uff0c\u7531\u8ba1\u7b97\u673a\u9886\u57df\u7684\u5de8\u4f6c\u674e\u98de\u98de\u9662\u58eb\u9886\u8854\u6559\u6388\uff08CV \u9886\u57df\u5212\u65f6\u4ee3\u7684\u8457\u540d\u6570\u636e\u96c6 ImageNet 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Regression\u3001Classification\u3001CNN\u3001Self-Attention\u3001Transformer\u3001GAN\u3001BERT\u3001Anomaly Detection\u3001Explainable AI\u3001Attack\u3001Adaptation\u3001 RL\u3001Compression\u3001Life-Long Learning \u4ee5\u53ca Meta Learning\u3002\u53ef\u8c13\u662f\u5305\u7f57\u4e07\u8c61\uff0c\u80fd\u8ba9\u5b66\u751f\u5bf9\u4e8e\u6df1\u5ea6\u5b66\u4e60\u7684\u7edd\u5927\u591a\u6570\u9886\u57df\u90fd\u6709\u4e00\u5b9a\u4e86\u89e3\uff0c\u4ece\u800c\u53ef\u4ee5\u8fdb\u4e00\u6b65\u9009\u62e9\u60f3\u8981\u6df1\u5165\u7684\u65b9\u5411\u8fdb\u884c\u5b66\u4e60\u3002 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\uff0c\u6bcf\u8282\u8bfe\u7684\u94fe\u63a5\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://speech.ee.ntu.edu.tw/~hylee/ml/2021-spring.html \uff0c15 \u4e2a lab\uff0c\u51e0\u4e4e\u8986\u76d6\u4e86\u4e3b\u6d41\u6df1\u5ea6\u5b66\u4e60\u7684\u6240\u6709\u9886\u57df","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/EE16/","text":"UCB EE16A&B: Designing Information Devices and Systems I&II Descriptions Offered by: UC Berkeley Prerequisites: None Programming Languages: Python Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 150 hours This introductory class for freshmen majoring in electronics at UC Berkeley teaches the fundamentals of circuitry. Through a variety of hands-on labs, students will experience collecting information from the environment through sensors and analyzing it to make predictions. Due to the COVID-19, all labs have remote online version, making them ideal for self-study. Course Resources Course Website: EE16A , EE16B Recordings: EE16A , EE16B Textbooks: EE16A , EE16B Assignments: EE16A , EE16B Personal Resources All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/EE16A - GitHub .","title":"EE16A&B: Designing Information Devices and Systems I&II"},{"location":"en/%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/EE16/#ucb-ee16ab-designing-information-devices-and-systems-iii","text":"","title":"UCB EE16A&B: Designing Information Devices and Systems I&II"},{"location":"en/%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/EE16/#descriptions","text":"Offered by: UC Berkeley Prerequisites: None Programming Languages: Python Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 150 hours This introductory class for freshmen majoring in electronics at UC Berkeley teaches the fundamentals of circuitry. Through a variety of hands-on labs, students will experience collecting information from the environment through sensors and analyzing it to make predictions. Due to the COVID-19, all labs have remote online version, making them ideal for self-study.","title":"Descriptions"},{"location":"en/%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/EE16/#course-resources","text":"Course Website: EE16A , EE16B Recordings: EE16A , EE16B Textbooks: EE16A , EE16B Assignments: EE16A , EE16B","title":"Course Resources"},{"location":"en/%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/EE16/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/EE16A - GitHub .","title":"Personal Resources"},{"location":"en/%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/Signals_and_Systems_AVO/","text":"MIT 6.007 Signals and Systems Descriptions Offered by: MIT Prerequisites: Calculus, Linear Algebra Programming Languages: Matlab Preferred Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: 50-70 hours The name of the instructor: Prof. Alan V. Oppenheim Okay, enough reason to take this class. Course Resources Course Website: https://ocw.mit.edu/resources/res-6-007-signals-and-systems-spring-2011/index.htm Recordings: https://www.bilibili.com/video/BV1CZ4y1j7hs Textbooks: Signals and Systems, 2nd Edition Assignments: https://ocw.mit.edu/resources/res-6-007-signals-and-systems-spring-2011/assignments","title":"MIT 6.007 Signals and Systems"},{"location":"en/%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/Signals_and_Systems_AVO/#mit-6007-signals-and-systems","text":"","title":"MIT 6.007 Signals and Systems"},{"location":"en/%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/Signals_and_Systems_AVO/#descriptions","text":"Offered by: MIT Prerequisites: Calculus, Linear Algebra Programming Languages: Matlab Preferred Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: 50-70 hours The name of the instructor: Prof. Alan V. Oppenheim Okay, enough reason to take this class.","title":"Descriptions"},{"location":"en/%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/Signals_and_Systems_AVO/#course-resources","text":"Course Website: https://ocw.mit.edu/resources/res-6-007-signals-and-systems-spring-2011/index.htm Recordings: https://www.bilibili.com/video/BV1CZ4y1j7hs Textbooks: Signals and Systems, 2nd Edition Assignments: https://ocw.mit.edu/resources/res-6-007-signals-and-systems-spring-2011/assignments","title":"Course Resources"},{"location":"en/%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/signal/","text":"UCB EE120: Signal and Systems Descriptions Offered by: UC Berkeley Prerequisites: CS61A, CS70, Calculus, Linear Algebra Programming Languages: Python Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 100 hours The highlight of this course is the six exciting labs that will allow you to use signals and systems theory to solve practical problems in Python. For example, in lab3 you will implement the FFT algorithm and compare the performance with Numpy's official implementation. In lab4 you will infer the heart rate by processing the video of fingers. Lab5 is the most awesome one where you will reduce the noise in the photos taken by the Hubble telescope to recover the brilliant and bright starry sky. In lab6 you will build a feedback system to stabilize the pole on the cart. Course Resources Course Website: https://inst.eecs.berkeley.edu/~ee120/fa19/ Recordings: refer to the course website Assignments: refer to the course website Personal Resources All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/UCB-EE120 - GitHub","title":"UCB EE120 : Signal and Systems"},{"location":"en/%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/signal/#ucb-ee120-signal-and-systems","text":"","title":"UCB EE120: Signal and Systems"},{"location":"en/%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/signal/#descriptions","text":"Offered by: UC Berkeley Prerequisites: CS61A, CS70, Calculus, Linear Algebra Programming Languages: Python Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 100 hours The highlight of this course is the six exciting labs that will allow you to use signals and systems theory to solve practical problems in Python. For example, in lab3 you will implement the FFT algorithm and compare the performance with Numpy's official implementation. In lab4 you will infer the heart rate by processing the video of fingers. Lab5 is the most awesome one where you will reduce the noise in the photos taken by the Hubble telescope to recover the brilliant and bright starry sky. In lab6 you will build a feedback system to stabilize the pole on the cart.","title":"Descriptions"},{"location":"en/%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/signal/#course-resources","text":"Course Website: https://inst.eecs.berkeley.edu/~ee120/fa19/ Recordings: refer to the course website Assignments: refer to the course website","title":"Course Resources"},{"location":"en/%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/signal/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/UCB-EE120 - GitHub","title":"Personal Resources"},{"location":"en/%E7%A8%8B%E5%BA%8F%E8%AF%AD%E8%A8%80%E8%AE%BE%E8%AE%A1/CS242/","text":"","title":"CS242"},{"location":"en/%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/CS161/","text":"UCB CS161: Computer Security \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS61A, CS61B, CS61C \u7f16\u7a0b\u8bed\u8a00\uff1aC, Go \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 \u4f2f\u514b\u5229\u7684\u8ba1\u7b97\u673a\u7cfb\u7edf\u5b89\u5168\u8bfe\u7a0b\uff0c\u8bfe\u7a0b\u5185\u5bb9\u5206\u4e3a5\u4e2a\u90e8\u5206\uff1a Security principles: how to design a secure system Memory safety: buffer overflow attack Cryptography: symmetric encryption, asymmetric encryption, MAC, digital signature ......... Web: SQL-injection, XSS, XSRF ....... Networking: attacks for each layer \u8fd9\u95e8\u8bfe\u8ba9\u6211\u5370\u8c61\u6700\u4e3a\u6df1\u523b\u7684\u90e8\u5206\u662f Project2\uff0c\u8ba9\u4f60\u7528 Go \u8bed\u8a00\u8bbe\u8ba1\u548c\u5b9e\u73b0\u4e00\u4e2a\u5b89\u5168\u7684\u6587\u4ef6\u5206\u4eab\u7cfb\u7edf\u3002\u6211\u82b1\u4e86\u6574\u6574\u4e09\u5929\u624d\u5b8c\u6210\u4e86\u8fd9\u4e2a\u975e\u5e38\u8650\u7684 Project\uff0c\u603b\u4ee3\u7801\u91cf\u8d85\u8fc7 3k \u884c\u3002\u5728\u8fd9\u6837\u5bc6\u96c6\u578b\u7684\u5f00\u53d1\u8fc7\u7a0b\u4e2d\uff0c\u80fd\u6781\u5927\u5730\u953b\u70bc\u4f60\u8bbe\u8ba1\u548c\u5b9e\u73b0\u4e00\u4e2a\u5b89\u5168\u7cfb\u7edf\u7684\u80fd\u529b\u3002 2020 \u5e74\u590f\u5b63\u5b66\u671f\u7684\u7248\u672c\u5f00\u6e90\u4e86\u8bfe\u7a0b\u5f55\u5f71\uff0c\u5927\u5bb6\u53ef\u4ee5\u5728\u4e0b\u9762\u7684\u8bfe\u7a0b\u7f51\u7ad9\u94fe\u63a5\u91cc\u627e\u5230\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://su20.cs161.org/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a https://textbook.cs161.org/ \u8bfe\u7a0b\u4f5c\u4e1a\uff1a7 \u4e2a\u5728\u7ebf HW + 3 \u4e2a Lab + 3 \u4e2a Project \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/UCB-CS161 - GitHub \u4e2d\u3002","title":"UCB CS161: Computer Security"},{"location":"en/%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/CS161/#ucb-cs161-computer-security","text":"","title":"UCB CS161: Computer Security"},{"location":"en/%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/CS161/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS61A, CS61B, CS61C \u7f16\u7a0b\u8bed\u8a00\uff1aC, Go \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 \u4f2f\u514b\u5229\u7684\u8ba1\u7b97\u673a\u7cfb\u7edf\u5b89\u5168\u8bfe\u7a0b\uff0c\u8bfe\u7a0b\u5185\u5bb9\u5206\u4e3a5\u4e2a\u90e8\u5206\uff1a Security principles: how to design a secure system Memory safety: buffer overflow attack Cryptography: symmetric encryption, asymmetric encryption, MAC, digital signature ......... Web: SQL-injection, XSS, XSRF ....... Networking: attacks for each layer \u8fd9\u95e8\u8bfe\u8ba9\u6211\u5370\u8c61\u6700\u4e3a\u6df1\u523b\u7684\u90e8\u5206\u662f Project2\uff0c\u8ba9\u4f60\u7528 Go \u8bed\u8a00\u8bbe\u8ba1\u548c\u5b9e\u73b0\u4e00\u4e2a\u5b89\u5168\u7684\u6587\u4ef6\u5206\u4eab\u7cfb\u7edf\u3002\u6211\u82b1\u4e86\u6574\u6574\u4e09\u5929\u624d\u5b8c\u6210\u4e86\u8fd9\u4e2a\u975e\u5e38\u8650\u7684 Project\uff0c\u603b\u4ee3\u7801\u91cf\u8d85\u8fc7 3k \u884c\u3002\u5728\u8fd9\u6837\u5bc6\u96c6\u578b\u7684\u5f00\u53d1\u8fc7\u7a0b\u4e2d\uff0c\u80fd\u6781\u5927\u5730\u953b\u70bc\u4f60\u8bbe\u8ba1\u548c\u5b9e\u73b0\u4e00\u4e2a\u5b89\u5168\u7cfb\u7edf\u7684\u80fd\u529b\u3002 2020 \u5e74\u590f\u5b63\u5b66\u671f\u7684\u7248\u672c\u5f00\u6e90\u4e86\u8bfe\u7a0b\u5f55\u5f71\uff0c\u5927\u5bb6\u53ef\u4ee5\u5728\u4e0b\u9762\u7684\u8bfe\u7a0b\u7f51\u7ad9\u94fe\u63a5\u91cc\u627e\u5230\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/CS161/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a https://su20.cs161.org/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a https://textbook.cs161.org/ \u8bfe\u7a0b\u4f5c\u4e1a\uff1a7 \u4e2a\u5728\u7ebf HW + 3 \u4e2a Lab + 3 \u4e2a Project","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/CS161/#_3","text":"@PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/UCB-CS161 - GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/MIT6.858/","text":"MIT 6.858: Computer System Security \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784\uff0c\u5bf9\u8ba1\u7b97\u673a\u7cfb\u7edf\u6709\u521d\u6b65\u4e86\u89e3 \u7f16\u7a0b\u8bed\u8a00\uff1aC, Python \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 MIT \u7684\u8ba1\u7b97\u673a\u7cfb\u7edf\u5b89\u5168\u8bfe\u7a0b\uff0c\u5b9e\u9a8c\u73af\u5883\u662f\u4e00\u4e2a Web Application Zoobar\u3002\u5b66\u751f\u5b66\u4e60\u653b\u9632\u6280\u672f\u5e76\u5e94\u7528\u4e8e\u8be5 Web Application\u3002 Lab 1: you will explore the zoobar web application, and use buffer overflow attacks to break its security properties. Lab 2: you will improve the zoobar web application by using privilege separation, so that if one component is compromised, the adversary doesn't get control over the whole web application. Lab 3: you will build a program analysis tool based on symbolic execution to find bugs in Python code such as the zoobar web application. Lab 4: you will improve the zoobar application against browser attacks. \u8fd9\u4e2a\u8bfe\u6211\u4e3b\u8981\u662f\u505a\u4e86 Lab 3\u3002Lab 3 \u662f\u901a\u8fc7\u6df7\u5408\u7b26\u53f7\u6267\u884c\u6765\u904d\u5386\u7a0b\u5e8f\u7684\u6240\u6709\u5206\u652f\uff0c\u7406\u89e3\u4e86\u7b26\u53f7\u6267\u884c\u7684\u601d\u60f3\u540e Lab \u5e76\u4e0d\u96be\u505a\u3002\u8fd9\u4e2a Lab \u76f4\u89c2\u5c55\u793a\u7b26\u53f7\u6267\u884c\u8fd9\u79cd\u6280\u672f\u7684\u4f7f\u7528\u65b9\u6cd5\u3002 \u8fd9\u4e2a\u8bfe\u7684 Final Project \u662f\u5b9e\u73b0 SecFS \uff0c\u4e00\u4e2a\u8fdc\u7aef\u6587\u4ef6\u7cfb\u7edf\uff0c\u9762\u5bf9\u5b8c\u5168\u4e0d\u53ef\u4fe1\u7684\u670d\u52a1\u5668\uff0c\u63d0\u4f9b\u673a\u5bc6\u6027\u548c\u5b8c\u6574\u6027\u3002\u53c2\u8003\u8bba\u6587\u4e3a SUNDR \u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://css.csail.mit.edu/6.858/2022/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a4 \u4e2a Lab + Final Project / Lab5","title":"MIT 6.858: Computer System Security"},{"location":"en/%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/MIT6.858/#mit-6858-computer-system-security","text":"","title":"MIT 6.858: Computer System Security"},{"location":"en/%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/MIT6.858/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784\uff0c\u5bf9\u8ba1\u7b97\u673a\u7cfb\u7edf\u6709\u521d\u6b65\u4e86\u89e3 \u7f16\u7a0b\u8bed\u8a00\uff1aC, Python \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 MIT \u7684\u8ba1\u7b97\u673a\u7cfb\u7edf\u5b89\u5168\u8bfe\u7a0b\uff0c\u5b9e\u9a8c\u73af\u5883\u662f\u4e00\u4e2a Web Application Zoobar\u3002\u5b66\u751f\u5b66\u4e60\u653b\u9632\u6280\u672f\u5e76\u5e94\u7528\u4e8e\u8be5 Web Application\u3002 Lab 1: you will explore the zoobar web application, and use buffer overflow attacks to break its security properties. Lab 2: you will improve the zoobar web application by using privilege separation, so that if one component is compromised, the adversary doesn't get control over the whole web application. Lab 3: you will build a program analysis tool based on symbolic execution to find bugs in Python code such as the zoobar web application. Lab 4: you will improve the zoobar application against browser attacks. \u8fd9\u4e2a\u8bfe\u6211\u4e3b\u8981\u662f\u505a\u4e86 Lab 3\u3002Lab 3 \u662f\u901a\u8fc7\u6df7\u5408\u7b26\u53f7\u6267\u884c\u6765\u904d\u5386\u7a0b\u5e8f\u7684\u6240\u6709\u5206\u652f\uff0c\u7406\u89e3\u4e86\u7b26\u53f7\u6267\u884c\u7684\u601d\u60f3\u540e Lab \u5e76\u4e0d\u96be\u505a\u3002\u8fd9\u4e2a Lab \u76f4\u89c2\u5c55\u793a\u7b26\u53f7\u6267\u884c\u8fd9\u79cd\u6280\u672f\u7684\u4f7f\u7528\u65b9\u6cd5\u3002 \u8fd9\u4e2a\u8bfe\u7684 Final Project \u662f\u5b9e\u73b0 SecFS \uff0c\u4e00\u4e2a\u8fdc\u7aef\u6587\u4ef6\u7cfb\u7edf\uff0c\u9762\u5bf9\u5b8c\u5168\u4e0d\u53ef\u4fe1\u7684\u670d\u52a1\u5668\uff0c\u63d0\u4f9b\u673a\u5bc6\u6027\u548c\u5b8c\u6574\u6027\u3002\u53c2\u8003\u8bba\u6587\u4e3a SUNDR \u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/MIT6.858/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://css.csail.mit.edu/6.858/2022/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a4 \u4e2a Lab + Final Project / Lab5","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106B_CS106X/","text":"Stanford CS106B/X: Programming Abstractions in C++ Descriptions Offered by: Stanford Prerequisites: CS50/CS106A/CS61A or equivalent Programming Languages: C++ Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: 50-70 hours CS106B/X are advanced programming courses at Stanford. CS106X is more difficult and in-depth than CS106B, but the main content is similar. Based on programming assignments in C++ language, students will develop the ability to solve real-world problems through programming abstraction. It also covers some simple data structures and algorithms, but is generally not as systematic as a specialized data structures course. Resources Course Website: CS106B , CS106X Textbook: https://web.stanford.edu/class/cs106x/res/reader/CS106BX-Reader.pdf Recordings: https://www.bilibili.com/video/BV1G7411k7jG","title":"Stanford CS106B/X"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106B_CS106X/#stanford-cs106bx-programming-abstractions-in-c","text":"","title":"Stanford CS106B/X: Programming Abstractions in C++"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106B_CS106X/#descriptions","text":"Offered by: Stanford Prerequisites: CS50/CS106A/CS61A or equivalent Programming Languages: C++ Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: 50-70 hours CS106B/X are advanced programming courses at Stanford. CS106X is more difficult and in-depth than CS106B, but the main content is similar. Based on programming assignments in C++ language, students will develop the ability to solve real-world problems through programming abstraction. It also covers some simple data structures and algorithms, but is generally not as systematic as a specialized data structures course.","title":"Descriptions"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106B_CS106X/#resources","text":"Course Website: CS106B , CS106X Textbook: https://web.stanford.edu/class/cs106x/res/reader/CS106BX-Reader.pdf Recordings: https://www.bilibili.com/video/BV1G7411k7jG","title":"Resources"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106L/","text":"CS106L: Stanford C++ Programming Descriptions Offered by: Stanford Prerequisites: better if you are already proficient in a programming language Programming Languages: C++ Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 20 hours I've been writing C++ code since freshman year, and it wasn't until I finished this class that I realized the C++ code I was writing was probably just C + cin / cout . This class will dive into a lot of standard C++ features and syntax that will allow you to write quality C++ code. Techniques such as auto binding, uniform initialization, lambda function, move semantics, RAII, etc. have been used repeatedly in my coding career since then and are very useful. It is worth mentioning that in this class, you will implement a HashMap (similar to unordered_map in STL), which almost ties the whole course together and is a great test of coding skills. Especially after the implementation of iterator , I started to understand why Linus is so sarcastic about C/C++, because it is really hard to write correctly. In short, the course is not difficult but very informative which requires you to consolidate repeatedly in later practice. The reason why Stanford offers a single C++ programming class is that many of the subsequent CS courses' projects are based on C++. For example, CS144 Computer Networks and CS143 Compilers. Both of these courses are included in this book. Course Resources Course Website: http://web.stanford.edu/class/cs106l/ Recordings: https://www.youtube.com/channel/UCSqr6y-eaQT_qZJVUm_4QxQ/playlists Textbook: http://web.stanford.edu/class/cs106l/full_course_reader.pdf Assignment1 Download: https://github.com/snme/cs106L-assignment1 Assignment2 Download: https://github.com/snme/cs106L-assignment2 Assignments: refer to the course website Personal Resources All the resources and assignments used by @PKUFlyingPig are maintained in PKUFlyingPig/CS106L - GitHub .","title":"Stanford CS106L: Standard C++ Programming"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106L/#cs106l-stanford-c-programming","text":"","title":"CS106L: Stanford C++ Programming"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106L/#descriptions","text":"Offered by: Stanford Prerequisites: better if you are already proficient in a programming language Programming Languages: C++ Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 20 hours I've been writing C++ code since freshman year, and it wasn't until I finished this class that I realized the C++ code I was writing was probably just C + cin / cout . This class will dive into a lot of standard C++ features and syntax that will allow you to write quality C++ code. Techniques such as auto binding, uniform initialization, lambda function, move semantics, RAII, etc. have been used repeatedly in my coding career since then and are very useful. It is worth mentioning that in this class, you will implement a HashMap (similar to unordered_map in STL), which almost ties the whole course together and is a great test of coding skills. Especially after the implementation of iterator , I started to understand why Linus is so sarcastic about C/C++, because it is really hard to write correctly. In short, the course is not difficult but very informative which requires you to consolidate repeatedly in later practice. The reason why Stanford offers a single C++ programming class is that many of the subsequent CS courses' projects are based on C++. For example, CS144 Computer Networks and CS143 Compilers. Both of these courses are included in this book.","title":"Descriptions"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106L/#course-resources","text":"Course Website: http://web.stanford.edu/class/cs106l/ Recordings: https://www.youtube.com/channel/UCSqr6y-eaQT_qZJVUm_4QxQ/playlists Textbook: http://web.stanford.edu/class/cs106l/full_course_reader.pdf Assignment1 Download: https://github.com/snme/cs106L-assignment1 Assignment2 Download: https://github.com/snme/cs106L-assignment2 Assignments: refer to the course website","title":"Course Resources"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106L/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig are maintained in PKUFlyingPig/CS106L - GitHub .","title":"Personal Resources"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS110L/","text":"CS110L: Safety in Systems Programming Descriptions Offered by: Stanford Prerequisites: basic knowledge about programming and computer system Programming Languages: Rust Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 30 hours In this course, you will learn a fantastic language, Rust. If you have studied C and have some knowledge of systems programming, you should have heard about memory leaks and the danger of pointers, but C's high efficiency makes it impossible to be replaced by other higher-level languages with garbage collection such as Java in system-level programming. Whereas Rust aims to make up for C's lack of security while having competitive efficiency. Therefore, Rust was designed from a system programmer's point of view. By learning Rust, you will learn the principles to write safer and more elegant system code (e.g., operating systems, etc.). The latter part of this course focuses on the topic of concurrency, where you will systematically learn multi-processing, multi-threading, event-driven programming, and several other techniques. In the second project, you will compare the pros and cons of each method. Personally, I find the concept of \"futures\" in Rust fascinating and elegant, and mastering this idea will help you in your following systems-related courses. In addition, Tsinghua University's operating system lab, rCore is based on Rust. You can see the documentation for more details. Course Resources Course Website: https://reberhardt.com/cs110l/spring-2020/ Recordings: https://youtu.be/j7AQrtLevUE Textbook: None Assignments\uff1a6 Labs, 2 Projects, the course website has specific requirements. The projects are quite interesting where you will Implement a GDB-like debugger and a load balancer in Rust. Personal Resources All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/CS110L - GitHub","title":"Stanford CS110L: Safety in Systems Programming"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS110L/#cs110l-safety-in-systems-programming","text":"","title":"CS110L: Safety in Systems Programming"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS110L/#descriptions","text":"Offered by: Stanford Prerequisites: basic knowledge about programming and computer system Programming Languages: Rust Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 30 hours In this course, you will learn a fantastic language, Rust. If you have studied C and have some knowledge of systems programming, you should have heard about memory leaks and the danger of pointers, but C's high efficiency makes it impossible to be replaced by other higher-level languages with garbage collection such as Java in system-level programming. Whereas Rust aims to make up for C's lack of security while having competitive efficiency. Therefore, Rust was designed from a system programmer's point of view. By learning Rust, you will learn the principles to write safer and more elegant system code (e.g., operating systems, etc.). The latter part of this course focuses on the topic of concurrency, where you will systematically learn multi-processing, multi-threading, event-driven programming, and several other techniques. In the second project, you will compare the pros and cons of each method. Personally, I find the concept of \"futures\" in Rust fascinating and elegant, and mastering this idea will help you in your following systems-related courses. In addition, Tsinghua University's operating system lab, rCore is based on Rust. You can see the documentation for more details.","title":"Descriptions"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS110L/#course-resources","text":"Course Website: https://reberhardt.com/cs110l/spring-2020/ Recordings: https://youtu.be/j7AQrtLevUE Textbook: None Assignments\uff1a6 Labs, 2 Projects, the course website has specific requirements. The projects are quite interesting where you will Implement a GDB-like debugger and a load balancer in Rust.","title":"Course Resources"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS110L/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/CS110L - GitHub","title":"Personal Resources"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS50/","text":"CS50: This is CS50x Descriptions Offered by: Harvard Prerequisites: None Programming Languages: C, Python, SQL, HTML, CSS, JavaScript Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: 20 hours This course has been voted the most popular public course by Harvard students for many years. Professor Malan is very passionate in class. I still remember the scene where he tears up the Yellow pages to explain the dichotomy method. Since this is a university-wide public course, the contents are pretty friendly to beginners and even if you already have some programming experience, all the programming assignments are quite exciting and worth a try. Course Resources Course Website: https://cs50.harvard.edu/x/2022/ Recordings: https://cs50.harvard.edu/x/2022/ Assignments: https://cs50.harvard.edu/x/2022/","title":"Harvard CS50: This is CS50x"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS50/#cs50-this-is-cs50x","text":"","title":"CS50: This is CS50x"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS50/#descriptions","text":"Offered by: Harvard Prerequisites: None Programming Languages: C, Python, SQL, HTML, CSS, JavaScript Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: 20 hours This course has been voted the most popular public course by Harvard students for many years. Professor Malan is very passionate in class. I still remember the scene where he tears up the Yellow pages to explain the dichotomy method. Since this is a university-wide public course, the contents are pretty friendly to beginners and even if you already have some programming experience, all the programming assignments are quite exciting and worth a try.","title":"Descriptions"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS50/#course-resources","text":"Course Website: https://cs50.harvard.edu/x/2022/ Recordings: https://cs50.harvard.edu/x/2022/ Assignments: https://cs50.harvard.edu/x/2022/","title":"Course Resources"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS61A/","text":"CS61A: Structure and Interpretation of Computer Programs Descriptions Offered by: UC Berkeley Prerequisites: None Programming Languages: Python, Scheme, SQL Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: 50 hours This is the first course in the Berkeley CS61 series, and my introductory course to Python. The CS61 series is composed of introductory courses to the CS major at Berkeley, where CS61A: Emphasizes abstraction and equips students to use programs to solve real-world problems without focusing on the underlying hardware details. CS61B: Focuses on algorithms and data structures and the construction of large-scale programs, where students combine knowledge of algorithms and data structures with the Java language to build large-scale projects at the thousand-line code level (such as a simple Google Maps, a two-dimensional version of Minecraft). CS61C: Focusing on computer architecture, students will understand how high-level languages (e.g. C) are converted step-by-step into machine-understandable bit strings and executed on CPUs. Students will learn about the RISC-V architecture and implement a CPU on their own by using Logism. CS61B and CS61C are both included in this guidebook. Going back to CS61A, you will note that this is not just a programming language class, but goes deeper into the principles of program construction and operation. Finally you will implement an interpreter for Scheme in Python in Project 4. In addition, abstraction will be a major theme in this class, as you will learn about functional programming, data abstraction, object orientation, etc. to make your code more readable and modular. Of course, learning a programming language is also a big part of this course. You will master three programming languages, Python, Scheme, and SQL, and in learning and comparing them, you will be equiped with the ability to quickly master a new programming language. Note: If you have no prior programming experience at all, getting started with CS61A requires a relatively high level of learning ability and self-discipline. To avoid the frustration of a struggling experience, you may choose a more friendly introductory programming course at first. For example, CS10 at Berkeley or CS50 at Harvard are both good choices. Course Resources Course Website: https://inst.eecs.berkeley.edu/~cs61a/su20/ Recordings: refer to the course website Textbook: http://composingprograms.com/ Assignments: refer to the course website Personal Resources All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/CS61A - GitHub","title":"UCB CS61A: Structure and Interpretation of Computer Programs"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS61A/#cs61a-structure-and-interpretation-of-computer-programs","text":"","title":"CS61A: Structure and Interpretation of Computer Programs"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS61A/#descriptions","text":"Offered by: UC Berkeley Prerequisites: None Programming Languages: Python, Scheme, SQL Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: 50 hours This is the first course in the Berkeley CS61 series, and my introductory course to Python. The CS61 series is composed of introductory courses to the CS major at Berkeley, where CS61A: Emphasizes abstraction and equips students to use programs to solve real-world problems without focusing on the underlying hardware details. CS61B: Focuses on algorithms and data structures and the construction of large-scale programs, where students combine knowledge of algorithms and data structures with the Java language to build large-scale projects at the thousand-line code level (such as a simple Google Maps, a two-dimensional version of Minecraft). CS61C: Focusing on computer architecture, students will understand how high-level languages (e.g. C) are converted step-by-step into machine-understandable bit strings and executed on CPUs. Students will learn about the RISC-V architecture and implement a CPU on their own by using Logism. CS61B and CS61C are both included in this guidebook. Going back to CS61A, you will note that this is not just a programming language class, but goes deeper into the principles of program construction and operation. Finally you will implement an interpreter for Scheme in Python in Project 4. In addition, abstraction will be a major theme in this class, as you will learn about functional programming, data abstraction, object orientation, etc. to make your code more readable and modular. Of course, learning a programming language is also a big part of this course. You will master three programming languages, Python, Scheme, and SQL, and in learning and comparing them, you will be equiped with the ability to quickly master a new programming language. Note: If you have no prior programming experience at all, getting started with CS61A requires a relatively high level of learning ability and self-discipline. To avoid the frustration of a struggling experience, you may choose a more friendly introductory programming course at first. For example, CS10 at Berkeley or CS50 at Harvard are both good choices.","title":"Descriptions"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS61A/#course-resources","text":"Course Website: https://inst.eecs.berkeley.edu/~cs61a/su20/ Recordings: refer to the course website Textbook: http://composingprograms.com/ Assignments: refer to the course website","title":"Course Resources"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS61A/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/CS61A - GitHub","title":"Personal Resources"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/Duke-Coursera-Intro-C/","text":"Introductory C Programming Specialization Descriptions Offered by: Duke Prerequisites: None Programming Languages: C Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 110 hours This is an excellent course which I benefited a lot from. The course teaches fundamental concepts such as frame, stack memory, heap memory, etc. There are great programming assignments to deepen and reinforce your understanding of the hardest part in C, like pointers. The course provides excellent practice in GDB, Valgrind, and the assignments will cover some basic Git exercises. The course instructor recommends using Emacs for homework, so it's a good opportunity to learn Emacs. If you already know how to use Vim, I suggest you use Evil. This way you don't lose the editing capabilities of Vim, and you get to experience the power of Emacs. Having both Emacs and Vim in your kit will increase your efficiency considerably. Emacs' org-mode, smooth integration of GDB, etc., are convenient for developers. It may require payment, but I think it's worth it. Although this is an introductory course, it has both breadth and depth. Course Resources Course Website: https://www.coursera.org/specializations/c-programming Recordings: refer to the course website Textbook: refer to the course website Assignments: refer to the course website Personal Resources All the resources and assignments used by in this course are maintained in Duke Coursera Intro C . Several assignments have not been completed so far for time reasons.","title":"Duke University: Introductory C Programming Specialization"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/Duke-Coursera-Intro-C/#introductory-c-programming-specialization","text":"","title":"Introductory C Programming Specialization"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/Duke-Coursera-Intro-C/#descriptions","text":"Offered by: Duke Prerequisites: None Programming Languages: C Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 110 hours This is an excellent course which I benefited a lot from. The course teaches fundamental concepts such as frame, stack memory, heap memory, etc. There are great programming assignments to deepen and reinforce your understanding of the hardest part in C, like pointers. The course provides excellent practice in GDB, Valgrind, and the assignments will cover some basic Git exercises. The course instructor recommends using Emacs for homework, so it's a good opportunity to learn Emacs. If you already know how to use Vim, I suggest you use Evil. This way you don't lose the editing capabilities of Vim, and you get to experience the power of Emacs. Having both Emacs and Vim in your kit will increase your efficiency considerably. Emacs' org-mode, smooth integration of GDB, etc., are convenient for developers. It may require payment, but I think it's worth it. Although this is an introductory course, it has both breadth and depth.","title":"Descriptions"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/Duke-Coursera-Intro-C/#course-resources","text":"Course Website: https://www.coursera.org/specializations/c-programming Recordings: refer to the course website Textbook: refer to the course website Assignments: refer to the course website","title":"Course Resources"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/Duke-Coursera-Intro-C/#personal-resources","text":"All the resources and assignments used by in this course are maintained in Duke Coursera Intro C . Several assignments have not been completed so far for time reasons.","title":"Personal Resources"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/MIT-Missing-Semester/","text":"MIT: The Missing Semester of Your CS Education Descriptions Offered by: MIT Prerequisites: None Programming Languages: Shell Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: 10 hours Just as the course name indicated, this course will teach the missing things in the university courses. It will cover shell programming, git, vim editor, tmux, ssh, sed, awk and even how to beautify your terminal. Trust me, this will be your first step to become a hacker! Resources Homepage: https://missing.csail.mit.edu/ Records: https://www.youtube.com/playlist?list=PLyzOVJj3bHQuloKGG59rS43e29ro7I57J Assignments: Some exercises after each lecture.","title":"MIT-Missing-Semester"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/MIT-Missing-Semester/#mit-the-missing-semester-of-your-cs-education","text":"","title":"MIT: The Missing Semester of Your CS Education"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/MIT-Missing-Semester/#descriptions","text":"Offered by: MIT Prerequisites: None Programming Languages: Shell Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: 10 hours Just as the course name indicated, this course will teach the missing things in the university courses. It will cover shell programming, git, vim editor, tmux, ssh, sed, awk and even how to beautify your terminal. Trust me, this will be your first step to become a hacker!","title":"Descriptions"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/MIT-Missing-Semester/#resources","text":"Homepage: https://missing.csail.mit.edu/ Records: https://www.youtube.com/playlist?list=PLyzOVJj3bHQuloKGG59rS43e29ro7I57J Assignments: Some exercises after each lecture.","title":"Resources"},{"location":"en/%E7%BC%96%E8%AF%91%E5%8E%9F%E7%90%86/6035/","text":"","title":"6035"},{"location":"en/%E7%BC%96%E8%AF%91%E5%8E%9F%E7%90%86/CS143/","text":"Stanford CS143: Compilers \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784 \u7f16\u7a0b\u8bed\u8a00\uff1aJava \u6216 C++ \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 \u65af\u5766\u798f\u7684\u7f16\u8bd1\u539f\u7406\u8bfe\u7a0b\uff0c\u8bbe\u8ba1\u8005\u5f00\u53d1\u4e86\u4e00\u4e2a 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\u4e2a\u7f16\u7a0b\u4f5c\u4e1a\u5e26\u4f60\u5b9e\u73b0\u4e00\u4e2a\u7f16\u8bd1\u5668 \u8d44\u6e90\u6c47\u603b @skyzluo \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 skyzluo/CS143-Compilers-Stanford - GitHub \u4e2d\u3002","title":"Stanford CS143: Compilers"},{"location":"en/%E7%BC%96%E8%AF%91%E5%8E%9F%E7%90%86/CS143/#stanford-cs143-compilers","text":"","title":"Stanford CS143: Compilers"},{"location":"en/%E7%BC%96%E8%AF%91%E5%8E%9F%E7%90%86/CS143/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784 \u7f16\u7a0b\u8bed\u8a00\uff1aJava \u6216 C++ \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 \u65af\u5766\u798f\u7684\u7f16\u8bd1\u539f\u7406\u8bfe\u7a0b\uff0c\u8bbe\u8ba1\u8005\u5f00\u53d1\u4e86\u4e00\u4e2a 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\u4e2d\u3002\u5bf9\u4e8e\u4f5c\u4e1a\u7684\u5177\u4f53\u5b9e\u73b0\uff0c\u5728\u77e5\u4e4e\u4e0a\u6709\u5f88\u591a\u76f8\u5173\u6587\u7ae0\u8fdb\u884c\u4e86\u7ec6\u81f4\u8bb2\u89e3\u53ef\u4ee5\u53c2\u8003\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E5%9B%BE%E5%BD%A2%E5%AD%A6/GAMES202/","text":"GAMES202 \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUCSB \u5148\u4fee\u8981\u6c42\uff1a\u7ebf\u6027\u4ee3\u6570\uff0c\u9ad8\u7b49\u6570\u5b66\uff0cC++\uff0cGAMES101 \u7f16\u7a0b\u8bed\u8a00\uff1aC++ \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a60 \u5c0f\u65f6 \u5b98\u65b9\u4ecb\u7ecd: \u672c\u8bfe\u7a0b\u5c06\u5168\u9762\u5730\u4ecb\u7ecd\u73b0\u4ee3\u5b9e\u65f6\u6e32\u67d3\u4e2d\u7684\u5173\u952e\u95ee\u9898\u4e0e\u89e3\u51b3\u65b9\u6cd5\u3002\u7531\u4e8e\u5b9e\u65f6\u6e32\u67d3 (>30 FPS) 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\u9664\u4e86\u6700\u65b0\u6700\u5168\u7684\u5185\u5bb9\u4e4b\u5916\uff0c\u672c\u8bfe\u7a0b\u4e0e\u5176\u5b83\u4efb\u4f55\u5b9e\u65f6\u6e32\u67d3\u7684\u6559\u7a0b\u8fd8\u6709\u4e00\u4e2a\u91cd\u8981\u7684\u533a\u522b\uff0c\u90a3\u5c31\u662f\u672c\u8bfe\u7a0b\u4e0d\u4f1a\u8bb2\u6388\u4efb\u4f55\u4e0e\u6e38\u620f\u5f15\u64ce\u7684\u4f7f\u7528\u76f8\u5173\u7684\u5185\u5bb9\uff0c\u5e76\u4e14\u4e0d\u4f1a\u7279\u522b\u5f3a\u8c03\u5177\u4f53\u7684\u7740\u8272\u5668\u5b9e\u73b0\u6280\u672f\uff0c\u800c\u4e3b\u8981\u8bb2\u6388\u5b9e\u65f6\u6e32\u67d3\u80cc\u540e\u7684\u79d1\u5b66\u4e0e\u77e5\u8bc6\u3002\u672c\u8bfe\u7a0b\u7684\u76ee\u6807\u662f\u5728\u4f60\u5b66\u4e60\u5b8c\u8fd9\u95e8\u8bfe\u7684\u65f6\u5019\uff0c\u4f60\u5c06\u6709\u6df1\u539a\u7684\u529f\u5e95\u53bb\u5f00\u53d1\u4e00\u4e2a\u5c5e\u4e8e\u4f60\u81ea\u5df1\u7684\u5b9e\u65f6\u6e32\u67d3\u5f15\u64ce\u3002 \u4f5c\u4e3a GAMES101 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\u7f16\u7a0b\u8bed\u8a00\uff1aC++ \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a60 \u5c0f\u65f6 \u5b98\u65b9\u4ecb\u7ecd: \u672c\u8bfe\u7a0b\u5c06\u5168\u9762\u5730\u4ecb\u7ecd\u73b0\u4ee3\u5b9e\u65f6\u6e32\u67d3\u4e2d\u7684\u5173\u952e\u95ee\u9898\u4e0e\u89e3\u51b3\u65b9\u6cd5\u3002\u7531\u4e8e\u5b9e\u65f6\u6e32\u67d3 (>30 FPS) \u5bf9\u901f\u5ea6\u8981\u6c42\u6781\u9ad8\uff0c\u56e0\u6b64\u672c\u8bfe\u7a0b\u7684\u5173\u6ce8\u70b9\u5c06\u662f\u5728\u82db\u523b\u7684\u65f6\u95f4\u9650\u5236\u4e0b\uff0c\u4eba\u4eec\u5982\u4f55\u6253\u7834\u901f\u5ea6\u4e0e\u8d28\u91cf\u4e4b\u95f4\u7684\u6743\u8861\uff0c\u540c\u65f6\u4fdd\u8bc1\u5b9e\u65f6\u7684\u9ad8\u901f\u5ea6\u4e0e\u7167\u7247\u7ea7\u7684\u771f\u5b9e\u611f\u3002 \u672c\u8bfe\u7a0b\u5c06\u4ee5\u4e13\u9898\u7684\u5f62\u5f0f\u5448\u73b0\uff0c\u8bfe\u7a0b\u5185\u5bb9\u4f1a\u8986\u76d6\u5b66\u672f\u754c\u4e0e\u5de5\u4e1a\u754c\u7684\u524d\u6cbf\u5185\u5bb9\uff0c\u5305\u62ec\uff1a\uff081\uff09\u5b9e\u65f6\u8f6f\u9634\u5f71\u7684\u6e32\u67d3\uff1b\uff082\uff09\u73af\u5883\u5149\u7167\uff1b\uff083\uff09\u57fa\u4e8e\u9884\u8ba1\u7b97\u6216\u65e0\u9884\u8ba1\u7b97\u7684\u5168\u5c40\u5149\u7167\uff1b\uff084\uff09\u57fa\u4e8e\u7269\u7406\u7684\u7740\u8272\u6a21\u578b\u4e0e\u65b9\u6cd5\uff1b\uff085\uff09\u5b9e\u65f6\u5149\u7ebf\u8ffd\u8e2a\uff1b\uff086\uff09\u6297\u952f\u9f7f\u4e0e\u8d85\u91c7\u6837\uff1b\u4ee5\u53ca\u4e00\u4e9b\u5e38\u89c1\u7684\u52a0\u901f\u65b9\u5f0f\u7b49\u7b49\u3002 \u9664\u4e86\u6700\u65b0\u6700\u5168\u7684\u5185\u5bb9\u4e4b\u5916\uff0c\u672c\u8bfe\u7a0b\u4e0e\u5176\u5b83\u4efb\u4f55\u5b9e\u65f6\u6e32\u67d3\u7684\u6559\u7a0b\u8fd8\u6709\u4e00\u4e2a\u91cd\u8981\u7684\u533a\u522b\uff0c\u90a3\u5c31\u662f\u672c\u8bfe\u7a0b\u4e0d\u4f1a\u8bb2\u6388\u4efb\u4f55\u4e0e\u6e38\u620f\u5f15\u64ce\u7684\u4f7f\u7528\u76f8\u5173\u7684\u5185\u5bb9\uff0c\u5e76\u4e14\u4e0d\u4f1a\u7279\u522b\u5f3a\u8c03\u5177\u4f53\u7684\u7740\u8272\u5668\u5b9e\u73b0\u6280\u672f\uff0c\u800c\u4e3b\u8981\u8bb2\u6388\u5b9e\u65f6\u6e32\u67d3\u80cc\u540e\u7684\u79d1\u5b66\u4e0e\u77e5\u8bc6\u3002\u672c\u8bfe\u7a0b\u7684\u76ee\u6807\u662f\u5728\u4f60\u5b66\u4e60\u5b8c\u8fd9\u95e8\u8bfe\u7684\u65f6\u5019\uff0c\u4f60\u5c06\u6709\u6df1\u539a\u7684\u529f\u5e95\u53bb\u5f00\u53d1\u4e00\u4e2a\u5c5e\u4e8e\u4f60\u81ea\u5df1\u7684\u5b9e\u65f6\u6e32\u67d3\u5f15\u64ce\u3002 \u4f5c\u4e3a GAMES101 \u7684\u8fdb\u9636\u8bfe\u7a0b\uff0c\u96be\u5ea6\u6709\u4e00\u5b9a\u7684\u63d0\u5347\uff0c\u4f46\u4e0d\u4f1a\u5f88\u5927\uff0c\u76f8\u4fe1\u5b8c\u6210\u4e86 GAMES101 \u7684\u540c\u5b66\u90fd\u6709\u80fd\u529b\u5b8c\u6210\u8fd9\u95e8\u8bfe\u7a0b\u3002\u6bcf\u4e2a project \u4ee3\u7801\u91cf\u90fd\u4e0d\u4f1a\u5f88\u591a\uff0c\u4f46\u662f\u90fd\u9700\u8981\u4e00\u5b9a\u7684\u601d\u8003\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E5%9B%BE%E5%BD%A2%E5%AD%A6/GAMES202/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a GAMES202 \u8bfe\u7a0b\u89c6\u9891\uff1a bilibili \u8bfe\u7a0b\u6559\u6750\uff1aReal-Time Rendering, 4th edition. \u8bfe\u7a0b\u4f5c\u4e1a\uff1a 5\u4e2aproject","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/CS144/","text":"CS144: Computer Network Introduction Offered by: Stanford Prerequisites: Computer System Fundamentals, CS106L Programming Language: C++ Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 100 hours One of the lecturers of this course is Professor Nick McKeown , a giant in the field of Networking. At the end of each chapter of MOOC, he will interview an executive in the industry or an expert in the academia, which can certainly broaden your horizons. In the projects, you will use C++ to build the entire TCP/IP protocol stack, the IP router, and the ARP protocol step by step from scratch. Finally, you will replace Linux Kernel's protocol stack with your own and use socket programming to communicate with your classmates, which is really amazing and exciting. Resources Course Website: https://cs144.github.io/ Video: https://www.youtube.com/watch?v=r2WZNaFyrbQ&list=PL6RdenZrxrw9inR-IJv-erlOKRHjymxMN Textbook: None Assignments: refer to the course website Reference PKUFlyingPig Lexssama's Blogs huangrt01 kiprey \u5eb7\u5b87PL's Blog doraemonzzz ViXbob's libsponge \u5403\u7740\u571f\u8c46\u5750\u5730\u94c1\u7684\u535a\u5ba2 Smith \u661f\u9065\u89c1 EIMadrigal Joey","title":"Stanford CS144: Computer Network"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/CS144/#cs144-computer-network","text":"","title":"CS144: Computer Network"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/CS144/#introduction","text":"Offered by: Stanford Prerequisites: Computer System Fundamentals, CS106L Programming Language: C++ Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 100 hours One of the lecturers of this course is Professor Nick McKeown , a giant in the field of Networking. At the end of each chapter of MOOC, he will interview an executive in the industry or an expert in the academia, which can certainly broaden your horizons. In the projects, you will use C++ to build the entire TCP/IP protocol stack, the IP router, and the ARP protocol step by step from scratch. Finally, you will replace Linux Kernel's protocol stack with your own and use socket programming to communicate with your classmates, which is really amazing and exciting.","title":"Introduction"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/CS144/#resources","text":"Course Website: https://cs144.github.io/ Video: https://www.youtube.com/watch?v=r2WZNaFyrbQ&list=PL6RdenZrxrw9inR-IJv-erlOKRHjymxMN Textbook: None Assignments: refer to the course website","title":"Resources"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/CS144/#reference","text":"PKUFlyingPig Lexssama's Blogs huangrt01 kiprey \u5eb7\u5b87PL's Blog doraemonzzz ViXbob's libsponge \u5403\u7740\u571f\u8c46\u5750\u5730\u94c1\u7684\u535a\u5ba2 Smith \u661f\u9065\u89c1 EIMadrigal Joey","title":"Reference"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/topdown/","text":"Computer Networking: A Top-Down Approach Descriptions Offered by: UMass Prerequisites: basic knowledge about computer system Programming Languages: None Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 40 hours Computer Networking: A Top-Down Approach is a classic textbook in the field of computer networking. The two authors, Jim Kurose and Keith Ross, have carefully crafted a course website to support the textbook, with lecture recordings, interactive online questions, and WireShark labs for network packet analysis. The only pity is that this course doesn't have hardcore programming assignments, and Stanford's CS144 makes up for that. 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The two authors, Jim Kurose and Keith Ross, have carefully crafted a course website to support the textbook, with lecture recordings, interactive online questions, and WireShark labs for network packet analysis. 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In the 2016 spring, the course open-sourced all of its programming assignments, and the textbook can be found on the latest website (see links below).","title":"Descriptions"},{"location":"en/%E8%BD%AF%E4%BB%B6%E5%B7%A5%E7%A8%8B/6031/#course-resources","text":"Course Website: 2021spring , 2016spring Recordings: None Assignments\uff1a4 Problem Sets + 1 Project","title":"Course Resources"},{"location":"en/%E8%BD%AF%E4%BB%B6%E5%B7%A5%E7%A8%8B/6031/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/MIT6.031 --- GitHub @pengzhangzhi completed the assignments and took some notes, maintained at pengzhangzhi/self-taught-CS/Software Construction - Github \u3002","title":"Personal Resources"},{"location":"en/%E8%BD%AF%E4%BB%B6%E5%B7%A5%E7%A8%8B/CS169/","text":"UCB CS169: software engineering Descriptions Offered by: UC Berkeley Prerequisites: None Programming Languages: Ruby/JavaScript Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 100 hours This is Berkeley's software engineering course. 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一个仅供参考的 CS 学习规划

计算机领域方向庞杂，知识浩如烟海，每个细分领域如果深究下去都可以说学无止境。因此，一个清晰明确的学习规划是非常重要的。这一节的内容是对后续整本书的内容的一个概览，你可以将其看作是这本书的目录，按需选择自己感兴趣的内容进行学习。

不过，在开始学习之前，先向小白们强烈推荐一个科普向系列视频 Crash Course: Computer Science，在短短 8 个小时里非常生动且全面地科普了关于计算机科学的方方面面：计算机的历史、计算机是如何运作的、组成计算机的各个重要模块、计算机科学中的重要思想等等等等。正如它的口号所说的 Computers are not magic!，希望看完这个视频之后，大家能对计算机科学有个全貌性地感知，从而怀着兴趣去面对下面浩如烟海的更为细致且深入的学习内容。

必学工具

俗话说：磨刀不误砍柴工。如果你是一个刚刚接触计算机的24k纯小白，学会一些工具将会让你事半功倍。

学会提问：也许你会惊讶，提问也算计算机必备技能吗，还放在第一条？我觉得在开源社区中，学会提问是一项非常重要的能力，它包含两方面的事情。其一是会变相地培养你自主解决问题的能力，因为从形成问题、描述问题并发布、他人回答、最后再到理解回答这个周期是非常长的，如果遇到什么鸡毛蒜皮的事情都希望别人最好远程桌面手把手帮你完成，那计算机的世界基本与你无缘了。其二，如果真的经过尝试还无法解决，可以借助开源社区的帮助，但这时候如何通过简洁的文字让别人瞬间理解你的处境以及目的，就显得尤为重要。推荐阅读提问的智慧这篇文章，这不仅能提高你解决问题的概率和效率，也能让开源社区里无偿提供解答的人们拥有一个好心情。

MIT-Missing-Semester 这门课覆盖了这些工具中绝大部分，而且有相当详细的使用指导，强烈建议小白学习。

翻墙：由于一些众所周知的原因，谷歌、GitHub 等网站在大陆无法访问。然而很多时候，谷歌和 StackOverflow 可以解决你在开发过程中遇到的 99% 的问题。因此，学会翻墙几乎是一个内地 CSer 的必备技能。（考虑到法律问题，这个文档提供的翻墙方式仅对拥有北大邮箱的用户适用）。

命令行：熟练使用命令行是一种常常被忽视，或被认为难以掌握的技能，但实际上，它会极大地提高你作为工程师的灵活性以及生产力。命令行的艺术是一份非常经典的教程，它源于 Quora 的一个提问，但在各路大神的贡献努力下已经成为了一个 GitHub 十万 stars 的顶流项目，被翻译成了十几种语言。教程不长，非常建议大家反复通读，在实践中内化吸收。同时，掌握 Shell 脚本编程也是一项不容忽视的技术，可以参考这个教程。

IDE (Integrated Development Environment)：集成开发环境，说白了就是你写代码的地方。作为一个码农，IDE 的重要性不言而喻，但由于很多 IDE 是为大型工程项目设计的，体量较大，功能也过于丰富。其实如今一些轻便的文本编辑器配合丰富的插件生态基本可以满足日常的轻量编程需求。个人常用的编辑器是 VS Code 和 Sublime（前者的插件配置非常简单，后者略显复杂但颜值很高）。当然对于大型项目我还是会采用略重型的 IDE，例如 Pycharm (Python)，IDEA (Java) 等等（免责申明：所有的 IDE 都是世界上最好的 IDE）。

Vim：一款命令行编辑工具。这是一个学习曲线有些陡峭的编辑器，不过学会它我觉得是非常有必要的，因为它将极大地提高你的开发效率。现在绝大多数 IDE 也都支持 Vim 插件，让你在享受现代开发环境的同时保留极客的炫酷（yue）。

Git：一款代码版本控制工具。Git的学习曲线可能更为陡峭，但出自 Linux 之父 Linus 之手的 Git 绝对是每个学 CS 的童鞋必须掌握的神器之一。

GitHub：基于 Git 的代码托管平台。全世界最大的代码开源社区，大佬集聚地。

GNU Make：一款工程构建工具。善用 GNU Make 会让你养成代码模块化的习惯，同时也能让你熟悉一些大型工程的编译链接流程。

CMake：一款功能比 GNU Make 更为强大的构建工具，建议掌握 GNU Make 之后再加以学习。

LaTex：逼格提升 论文排版工具。

Docker：一款相较于虚拟机更轻量级的软件打包与环境部署工具。

实用工具箱：除了上面提到的这些在开发中使用频率极高的工具之外，我还收集了很多实用有趣的免费工具，例如一些下载工具、设计工具、学习网站等等。

Thesis：毕业论文 Word 写作教程。

好书推荐

私以为一本好的教材应当是以人为本的，而不是炫技式的理论堆砌。告诉读者“是什么”固然重要，但更好的应当是教材作者将其在这个领域深耕几十年的经验融汇进书中，向读者娓娓道来“为什么”以及未来应该“怎么做”。

链接戳这里

环境配置

你以为的开发 —— 在 IDE 里疯狂码代码数小时。

实际上的开发 —— 配环境配几天还没开始写代码。

PC 端环境配置

如果你是 Mac 用户，那么你很幸运，这份指南 将会手把手地带你搭建起整套开发环境。如果你是 Windows 用户，可以参考这个相对简略的教程。

另外大家可以参考一份灵感来自 6.NULL MIT-Missing-Semester 的 环境配置指南，重点在于终端的美化配置。此外还包括常用软件源（如 GitHub, Anaconda, PyPI 等）的加速与替换以及一些 IDE 的配置与激活教程。

服务器端环境配置

推荐一个非常不错的 GitHub 项目 DevOps-Guide，其中涵盖了非常多的运维方面的基础知识和教程，例如 Docker, Kubernetes, Linux, CI-CD, GitHub Actions 等等。

课程地图

正如这章开头提到的，这份课程地图仅仅是一个仅供参考的课程规划，我作为一个临近毕业的本科生。深感自己没有权利也没有能力向别人宣扬“应该怎么学”。因此如果你觉得以下的课程分类与选择有不合理之处，我全盘接受，并深感抱歉。你可以在下一节定制属于你的课程地图

以下课程类别中除了含有 基础 和 入门 字眼的以外，并无明确的先后次序，大家只要满足某个课程的先修要求，完全可以根据自己的需要和喜好选择想要学习的课程。

另外由于贡献者的不断增加，这份课程地图已经从最初我的学习经历，发展成为很多 CS 自学者的资源合集，其中难免有内容交叉甚至重复的。之所以都列出来，还是希望集百家之长，给大家尽可能多的选择与参考。

数学基础

微积分与线性代数

作为大一新生，学好微积分线代是和写代码至少同等重要的事情，相信已经有无数的前人经验提到过这一点，但我还是要不厌其烦地再强调一遍：学好微积分线代真的很重要！你也许会吐槽这些东西岂不是考完就忘，那我觉得你是并没有把握住它们本质，对它们的理解还没有达到刻骨铭心的程度。如果觉得老师课上讲的内容晦涩难懂，不妨参考 MIT 的 Calculus Course 和 18.06: Linear Algebra 的课程 notes，至少于我而言，它帮助我深刻理解了微积分和线性代数的许多本质。顺道再安利一个油管数学网红 3Blue1Brown，他的频道有很多用生动形象的动画阐释数学本质内核的视频，兼具深度和广度，质量非常高。

信息论入门

作为计算机系的学生，及早了解一些信息论的基础知识，我觉得是大有裨益的。但大多信息论课程都面向高年级本科生甚至研究生，对新手极不友好。而 MIT 的 6.050J: Information theory and Entropy 这门课正是为大一新生量身定制的，几乎没有先修要求，涵盖了编码、压缩、通信、信息熵等等内容，非常有趣。

数学进阶

离散数学与概率论

集合论、图论、概率论等等是算法推导与证明的重要工具，也是后续高阶数学课程的基础。但我觉得这类课程的讲授很容易落入理论化与形式化的窠臼，让课堂成为定理结论的堆砌，而无法使学生深刻把握理论的本质，进而造成学了就背，考了就忘的怪圈。如果能在理论教学中穿插算法运用实例，学生在拓展算法知识的同时也能窥见理论的力量和魅力。

UCB CS70 : discrete Math and probability theory 和 UCB CS126 : Probability theory 是 UC Berkeley 的概率论课程，前者覆盖了离散数学和概率论基础，后者则涉及随机过程以及深入的理论内容。两者都非常注重理论和实践的结合，有丰富的算法实际运用实例，后者还有大量的 Python 编程作业来让学生运用概率论的知识解决实际问题。

数值分析

作为计算机系的学生，培养计算思维是很重要的，实际问题的建模、离散化，计算机的模拟、分析，是一项很重要的能力。而这两年开始风靡的，由 MIT 打造的 Julia 编程语言以其 C 一样的速度和 Python 一样友好的语法在数值计算领域有一统天下之势，MIT 的许多数学课程也开始用 Julia 作为教学工具，把艰深的数学理论用直观清晰的代码展示出来。

ComputationalThinking 是 MIT 开设的一门计算思维入门课，所有课程内容全部开源，可以在课程网站直接访问。这门课利用 Julia 编程语言，在图像处理、社会科学与数据科学、气候学建模三个 topic 下带领学生理解算法、数学建模、数据分析、交互设计、图例展示，让学生体验计算与科学的美妙结合。内容虽然不难，但给我最深刻的感受就是，科学的魅力并不是故弄玄虚的艰深理论，不是诘屈聱牙的术语行话，而是用直观生动的案例，用简练深刻的语言，让每个普通人都能理解。

上完上面的体验课之后，如果意犹未尽的话，不妨试试 MIT 的 18.330 : Introduction to numerical analysis，这门课的编程作业同样会用 Julia 编程语言，不过难度和深度上都上了一个台阶。内容涉及了浮点编码、Root finding、线性系统、微分方程等等方面，整门课的主旨就是让你利用离散化的计算机表示去估计和逼近一个数学上连续的概念。这门课的教授还专门撰写了一本配套的开源教材 Fundamentals of Numerical Computation，里面附有丰富的 Julia 代码实例和严谨的公式推导。

如果你还意犹未尽的话，还有 MIT 的数值分析研究生课程 18.335: Introduction to numerical method 供你参考。

微分方程

如果世间万物的运动发展都能用方程来刻画和描述，这是一件多么酷的事情呀！虽然几乎任何一所学校的 CS 培养方案中都没有微分方程相关的必修课程，但我还是觉得掌握它会赋予你一个新的视角来审视这个世界。

由于微分方程中往往会用到很多复变函数的知识，所以大家可以参考 MIT18.04: Complex variables functions 的课程 notes 来补齐先修知识。

MIT18.03: differential equations) 主要覆盖了常微分方程的求解，在此基础之上 MIT18.152: Partial differential equations) 则会深入偏微分方程的建模与求解。掌握了微分方程这一有利工具，相信对于你的实际问题的建模能力以及从众多噪声变量中把握本质的直觉都会有很大帮助。

数学高阶

作为计算机系的学生，我经常听到数学无用论的论断，对此我不敢苟同但也无权反对，但若凡事都硬要争出个有用和无用的区别来，倒也着实无趣，因此下面这些面向高年级甚至研究生的数学课程，大家按兴趣自取所需。

凸优化

Standford EE364A: Convex Optimization

信息论

MIT6.441: Information Theory

应用统计学

MIT18.650: Statistics for Applications

初等数论

MIT18.781: Theory of Numbers

密码学

Standford CS255: Cryptography

编程入门

Languages are tools, you choose the right tool to do the right thing. Since there's no universally perfect tool, there's no universally perfect language.

Shell

Python

C++

Rust

OCaml

电子基础

电路基础

作为计算机系的学生，了解一些基础的电路知识，感受从传感器收集数据到数据分析再到算法预测整条流水线，对于后续知识的学习以及计算思维的培养还是很有帮助的。EE16A&B: Designing Information Devices and Systems I&II 是伯克利 EE 学生的大一入门课，其中 EE16A 注重通过电路从实际环境中收集和分析数据，而 EE16B 则侧重从这些收集到的数据进行分析并做出预测行为。

信号与系统

信号与系统是一门我觉得非常值得一上的课，最初学它只是为了满足我对傅里叶变换的好奇，但学完之后我才不禁感叹，傅立叶变换给我提供了一个全新的视角去看待这个世界，就如同微分方程一样，让你沉浸在用数学去精确描绘和刻画这个世界的优雅与神奇之中。

MIT 6.003: signal and systems 提供了全部的课程录影、书面作业以及答案。也可以去看这门课的远古版本

而 UCB EE120: Signal and Systems 关于傅立叶变换的 notes 写得非常好，并且提供了6 个非常有趣的 Python 编程作业，让你实践中运用信号与系统的理论与算法。

数据结构与算法

数据结构与算法

算法设计与分析

软件工程

入门课

一份“能跑”的代码，和一份高质量的工业级代码是有本质区别的。因此我非常推荐低年级的同学学习一下 MIT 6.031: Software Construction 这门课，它会以 Java 语言为基础，以丰富细致的阅读材料和精心设计的编程练习传授如何编写不易出 bug、简明易懂、易于维护修改的高质量代码。大到宏观数据结构设计，小到如何写注释，遵循这些前人总结的细节和经验，对于你此后的编程生涯大有裨益。

专业课

当然，如果你想系统性地上一门软件工程的课程，那我推荐的是伯克利的 UCB CS169: software engineering。但需要提醒的是，和大多学校（包括贵校）的软件工程课程不同，这门课不会涉及传统的 design and document 模式，即强调各种类图、流程图及文档设计，而是采用近些年流行起来的小团队快速迭代 Agile Develepment 开发模式以及利用云平台的 Software as a service 服务模式。

体系结构

入门课

从小我就一直听说，计算机的世界是由 01 构成的，我不理解但大受震撼。如果你的内心也怀有这份好奇，不妨花一到两个月的时间学习 Coursera: Nand2Tetris 这门无门槛的计算机课程。这门麻雀虽小五脏俱全的课程会从 01 开始让你亲手造出一台计算机，并在上面运行俄罗斯方块小游戏。一门课里涵盖了编译、虚拟机、汇编、体系结构、数字电路、逻辑门等等从上至下、从软至硬的各类知识，非常全面。难度上也是通过精心的设计，略去了众多现代计算机复杂的细节，提取出了最核心本质的东西，力图让每个人都能理解。在低年级，如果就能从宏观上建立对整个计算机体系的鸟瞰图，是大有裨益的。

专业课

当然，如果想深入现代计算机体系结构的复杂细节，还得上一门大学本科难度的课程 UCB CS61C: Great Ideas in Computer Architecture。UC Berkeley 作为 RISC-V 架构的发源地，在体系结构领域算得上首屈一指。其课程非常注重实践，你会在 Project 中手写汇编构造神经网络，从零开始搭建一个 CPU，这些实践都会让你对计算机体系结构有更为深入的理解，而不是仅停留于“取指译码执行访存写回”的单调背诵里。

系统入门

计算机系统是一个庞杂而深刻的主题，在深入学习某个细分领域之前，对各个领域有一个宏观概念性的理解，对一些通用性的设计原则有所知晓，会让你在之后的深入学习中不断强化一些最为核心乃至哲学的概念，而不会桎梏于复杂的内部细节和各种 trick。因为在我看来，学习系统最关键的还是想让你领悟到这些最核心的东西，从而能够设计和实现出属于自己的系统。

MIT6.033: System Engineering 是 MIT 的系统入门课，主题涉及了操作系统、网络、分布式和系统安全，除了知识点的传授外，这门课还会讲授一些写作和表达上的技巧，让你学会如何设计并向别人介绍和分析自己的系统。这本书配套的教材 Principles of Computer System Design: An Introduction 也写得非常好，推荐大家阅读。

CMU 15-213: Introduction to Computer System 是 CMU 的系统入门课，内容覆盖了体系结构、操作系统、链接、并行、网络等等，兼具广度和深度，配套的教材 Computer Systems: A Programmer's Perspective 也是质量极高，强烈建议阅读。

操作系统

操作系统作为各类纷繁复杂的底层硬件虚拟化出一套规范优雅的抽象，给所有应用软件提供丰富的功能支持。了解操作系统的设计原则和内部原理对于一个不满足于当调包侠的程序员来说是大有裨益的。出于对操作系统的热爱，我上过国内外很多操作系统课程，它们各有侧重和优劣，大家可以根据兴趣各取所需。

MIT 6.S081: Operating System Engineering，MIT 著名 PDOS 实验室出品，11 个 Project 让你在一个实现非常优雅的类Unix操作系统xv6上增加各类功能模块。这门课也让我深刻认识到，做系统不是靠 PPT 念出来的，是得几万行代码一点点累起来的。

UCB CS162: Operating System，伯克利的操作系统课，采用和 Stanford 同样的 Project —— 一个教学用操作系统 Pintos。我作为北京大学2022年春季学期操作系统实验班的助教，引入并改善了这个 Project，课程资源也会全部开源，具体参见课程网站。

NJU: Operating System Design and Implementation，南京大学的蒋炎岩老师开设的操作系统课程。蒋老师以其独到的系统视角结合丰富的代码示例将众多操作系统的概念讲得深入浅出，此外这门课的全部课程内容都是中文的，非常方便大家学习。

并行与分布式系统

想必这两年各类 CS 讲座里最常听到的话就是“摩尔定律正在走向终结”，此话不假，当单核能力达到上限时，多核乃至众核架构如日中天。硬件的变化带来的是上层编程逻辑的适应与改变，要想充分利用硬件性能，编写并行程序几乎成了程序员的必备技能。与此同时，深度学习的兴起对计算机算力与存储的要求都达到了前所未有的高度，大规模集群的部署和优化也成为热门技术话题。

并行计算

CMU 15-418/Stanford CS149: Parallel Computing

分布式系统

MIT 6.824: Distributed System

系统安全

不知道你当年选择计算机是不是因为怀着一个中二的黑客梦想，但现实却是成为黑客道阻且长。

理论

UCB CS161: Computer Security 是伯克利的系统安全课程，会涵盖栈攻击、密码学、网站安全、网络安全等等内容。

实践

掌握这些理论知识之后，还需要在实践中培养和锻炼这些“黑客素养”。CTF 夺旗赛是一项比较热门的系统安全比赛，赛题中会融会贯通地考察你对计算机各个领域知识的理解和运用。北大今年也成功举办了第 0 届和第 1 届，鼓励大家后期踊跃参与，在实践中提高自己。下面列举一些我平时学习（摸鱼）用到的资源：

计算机网络

计网著名教材《自顶向下方法》的配套学习资源 Computer Networking: A Top-Down Approach。

没有什么能比自己写个 TCP/IP 协议栈更能加深对计算机网络的理解了，所以不妨试试 Stanford CS144: Computer Network，8 个 Project 带你实现整个协议栈。

数据库系统

没有什么能比自己写个关系型数据库更能加深对数据库系统的理解了。

C++版

CMU 15-445: Introduction to Database System

Java版

UCB CS186: Introduction to Database System

编译原理

没有什么能比自己写个编译器更能加深对编译器的理解了。

Stanford CS143: Compilers

计算机图形学

Stanford CS148 Games101 Games103 Games202

Web开发

网站的开发很少在计算机的培养方案里被重视，但其实掌握这项技能还是好处多多的，例如搭建自己的个人主页，抑或是给自己的课程项目做一个精彩的展示网页。

两周速成版

MIT web development course

系统学习版

Stanford CS142: Web Applications

数据科学

UCB Data100: Principles and Techniques of Data Science

人工智能

入门课

Harvard CS50’s Introduction to AI with Python

专业课

UCB CS188: Introduction to Artificial Intelligence

机器学习

入门课

Coursera: Machine Learning

专业课

深度学习

入门课

计算机视觉

Stanford CS231n: CNN for Visual Recognition

自然语言处理

Stanford CS224n: Natural Language Processing

图神经网络

Stanford CS224w: Machine Learning with Graphs

强化学习

UCB CS285: Deep Reinforcement Learning

定制属于你的课程地图

授人以鱼不如授人以渔。

以上的课程规划难免带有强烈的个人偏好，不一定适合所有人，更多是起到抛砖引玉的作用。如果你想挑选自己感兴趣的方向和内容加以学习，可以参考我在下面列出来的资源。

最后更新: 2022年9月10日

一个仅供参考的 CS 学习规划

计算机领域方向庞杂，知识浩如烟海，每个细分领域如果深究下去都可以说学无止境。因此，一个清晰明确的学习规划是非常重要的。这一节的内容是对后续整本书的内容的一个概览，你可以将其看作是这本书的目录，按需选择自己感兴趣的内容进行学习。

不过，在开始学习之前，先向小白们强烈推荐一个科普向系列视频 Crash Course: Computer Science，在短短 8 个小时里非常生动且全面地科普了关于计算机科学的方方面面：计算机的历史、计算机是如何运作的、组成计算机的各个重要模块、计算机科学中的重要思想等等等等。正如它的口号所说的 Computers are not magic!，希望看完这个视频之后，大家能对计算机科学有个全貌性地感知，从而怀着兴趣去面对下面浩如烟海的更为细致且深入的学习内容。

必学工具

俗话说：磨刀不误砍柴工。如果你是一个刚刚接触计算机的24k纯小白，学会一些工具将会让你事半功倍。

学会提问：也许你会惊讶，提问也算计算机必备技能吗，还放在第一条？我觉得在开源社区中，学会提问是一项非常重要的能力，它包含两方面的事情。其一是会变相地培养你自主解决问题的能力，因为从形成问题、描述问题并发布、他人回答、最后再到理解回答这个周期是非常长的，如果遇到什么鸡毛蒜皮的事情都希望别人最好远程桌面手把手帮你完成，那计算机的世界基本与你无缘了。其二，如果真的经过尝试还无法解决，可以借助开源社区的帮助，但这时候如何通过简洁的文字让别人瞬间理解你的处境以及目的，就显得尤为重要。推荐阅读提问的智慧这篇文章，这不仅能提高你解决问题的概率和效率，也能让开源社区里无偿提供解答的人们拥有一个好心情。

MIT-Missing-Semester 这门课覆盖了这些工具中绝大部分，而且有相当详细的使用指导，强烈建议小白学习。

翻墙：由于一些众所周知的原因，谷歌、GitHub 等网站在大陆无法访问。然而很多时候，谷歌和 StackOverflow 可以解决你在开发过程中遇到的 99% 的问题。因此，学会翻墙几乎是一个内地 CSer 的必备技能。（考虑到法律问题，这个文档提供的翻墙方式仅对拥有北大邮箱的用户适用）。

命令行：熟练使用命令行是一种常常被忽视，或被认为难以掌握的技能，但实际上，它会极大地提高你作为工程师的灵活性以及生产力。命令行的艺术是一份非常经典的教程，它源于 Quora 的一个提问，但在各路大神的贡献努力下已经成为了一个 GitHub 十万 stars 的顶流项目，被翻译成了十几种语言。教程不长，非常建议大家反复通读，在实践中内化吸收。同时，掌握 Shell 脚本编程也是一项不容忽视的技术，可以参考这个教程。

IDE (Integrated Development Environment)：集成开发环境，说白了就是你写代码的地方。作为一个码农，IDE 的重要性不言而喻，但由于很多 IDE 是为大型工程项目设计的，体量较大，功能也过于丰富。其实如今一些轻便的文本编辑器配合丰富的插件生态基本可以满足日常的轻量编程需求。个人常用的编辑器是 VS Code 和 Sublime（前者的插件配置非常简单，后者略显复杂但颜值很高）。当然对于大型项目我还是会采用略重型的 IDE，例如 Pycharm (Python)，IDEA (Java) 等等（免责申明：所有的 IDE 都是世界上最好的 IDE）。

Vim：一款命令行编辑工具。这是一个学习曲线有些陡峭的编辑器，不过学会它我觉得是非常有必要的，因为它将极大地提高你的开发效率。现在绝大多数 IDE 也都支持 Vim 插件，让你在享受现代开发环境的同时保留极客的炫酷（yue）。

Git：一款代码版本控制工具。Git的学习曲线可能更为陡峭，但出自 Linux 之父 Linus 之手的 Git 绝对是每个学 CS 的童鞋必须掌握的神器之一。

GitHub：基于 Git 的代码托管平台。全世界最大的代码开源社区，大佬集聚地。

GNU Make：一款工程构建工具。善用 GNU Make 会让你养成代码模块化的习惯，同时也能让你熟悉一些大型工程的编译链接流程。

CMake：一款功能比 GNU Make 更为强大的构建工具，建议掌握 GNU Make 之后再加以学习。

LaTex：逼格提升 论文排版工具。

Docker：一款相较于虚拟机更轻量级的软件打包与环境部署工具。

实用工具箱：除了上面提到的这些在开发中使用频率极高的工具之外，我还收集了很多实用有趣的免费工具，例如一些下载工具、设计工具、学习网站等等。

Thesis：毕业论文 Word 写作教程。

好书推荐

私以为一本好的教材应当是以人为本的，而不是炫技式的理论堆砌。告诉读者“是什么”固然重要，但更好的应当是教材作者将其在这个领域深耕几十年的经验融汇进书中，向读者娓娓道来“为什么”以及未来应该“怎么做”。

链接戳这里

环境配置

你以为的开发 —— 在 IDE 里疯狂码代码数小时。

实际上的开发 —— 配环境配几天还没开始写代码。

PC 端环境配置

如果你是 Mac 用户，那么你很幸运，这份指南 将会手把手地带你搭建起整套开发环境。如果你是 Windows 用户，可以参考这个相对简略的教程。

另外大家可以参考一份灵感来自 6.NULL MIT-Missing-Semester 的 环境配置指南，重点在于终端的美化配置。此外还包括常用软件源（如 GitHub, Anaconda, PyPI 等）的加速与替换以及一些 IDE 的配置与激活教程。

服务器端环境配置

推荐一个非常不错的 GitHub 项目 DevOps-Guide，其中涵盖了非常多的运维方面的基础知识和教程，例如 Docker, Kubernetes, Linux, CI-CD, GitHub Actions 等等。

课程地图

正如这章开头提到的，这份课程地图仅仅是一个仅供参考的课程规划，我作为一个临近毕业的本科生。深感自己没有权利也没有能力向别人宣扬“应该怎么学”。因此如果你觉得以下的课程分类与选择有不合理之处，我全盘接受，并深感抱歉。你可以在下一节定制属于你的课程地图

以下课程类别中除了含有 基础 和 入门 字眼的以外，并无明确的先后次序，大家只要满足某个课程的先修要求，完全可以根据自己的需要和喜好选择想要学习的课程。

另外由于贡献者的不断增加，这份课程地图已经从最初我的学习经历，发展成为很多 CS 自学者的资源合集，其中难免有内容交叉甚至重复的。之所以都列出来，还是希望集百家之长，给大家尽可能多的选择与参考。

数学基础

微积分与线性代数

作为大一新生，学好微积分线代是和写代码至少同等重要的事情，相信已经有无数的前人经验提到过这一点，但我还是要不厌其烦地再强调一遍：学好微积分线代真的很重要！你也许会吐槽这些东西岂不是考完就忘，那我觉得你是并没有把握住它们本质，对它们的理解还没有达到刻骨铭心的程度。如果觉得老师课上讲的内容晦涩难懂，不妨参考 MIT 的 Calculus Course 和 18.06: Linear Algebra 的课程 notes，至少于我而言，它帮助我深刻理解了微积分和线性代数的许多本质。顺道再安利一个油管数学网红 3Blue1Brown，他的频道有很多用生动形象的动画阐释数学本质内核的视频，兼具深度和广度，质量非常高。

信息论入门

作为计算机系的学生，及早了解一些信息论的基础知识，我觉得是大有裨益的。但大多信息论课程都面向高年级本科生甚至研究生，对新手极不友好。而 MIT 的 6.050J: Information theory and Entropy 这门课正是为大一新生量身定制的，几乎没有先修要求，涵盖了编码、压缩、通信、信息熵等等内容，非常有趣。

数学进阶

离散数学与概率论

集合论、图论、概率论等等是算法推导与证明的重要工具，也是后续高阶数学课程的基础。但我觉得这类课程的讲授很容易落入理论化与形式化的窠臼，让课堂成为定理结论的堆砌，而无法使学生深刻把握理论的本质，进而造成学了就背，考了就忘的怪圈。如果能在理论教学中穿插算法运用实例，学生在拓展算法知识的同时也能窥见理论的力量和魅力。

UCB CS70 : discrete Math and probability theory 和 UCB CS126 : Probability theory 是 UC Berkeley 的概率论课程，前者覆盖了离散数学和概率论基础，后者则涉及随机过程以及深入的理论内容。两者都非常注重理论和实践的结合，有丰富的算法实际运用实例，后者还有大量的 Python 编程作业来让学生运用概率论的知识解决实际问题。

数值分析

作为计算机系的学生，培养计算思维是很重要的，实际问题的建模、离散化，计算机的模拟、分析，是一项很重要的能力。而这两年开始风靡的，由 MIT 打造的 Julia 编程语言以其 C 一样的速度和 Python 一样友好的语法在数值计算领域有一统天下之势，MIT 的许多数学课程也开始用 Julia 作为教学工具，把艰深的数学理论用直观清晰的代码展示出来。

ComputationalThinking 是 MIT 开设的一门计算思维入门课，所有课程内容全部开源，可以在课程网站直接访问。这门课利用 Julia 编程语言，在图像处理、社会科学与数据科学、气候学建模三个 topic 下带领学生理解算法、数学建模、数据分析、交互设计、图例展示，让学生体验计算与科学的美妙结合。内容虽然不难，但给我最深刻的感受就是，科学的魅力并不是故弄玄虚的艰深理论，不是诘屈聱牙的术语行话，而是用直观生动的案例，用简练深刻的语言，让每个普通人都能理解。

上完上面的体验课之后，如果意犹未尽的话，不妨试试 MIT 的 18.330 : Introduction to numerical analysis，这门课的编程作业同样会用 Julia 编程语言，不过难度和深度上都上了一个台阶。内容涉及了浮点编码、Root finding、线性系统、微分方程等等方面，整门课的主旨就是让你利用离散化的计算机表示去估计和逼近一个数学上连续的概念。这门课的教授还专门撰写了一本配套的开源教材 Fundamentals of Numerical Computation，里面附有丰富的 Julia 代码实例和严谨的公式推导。

如果你还意犹未尽的话，还有 MIT 的数值分析研究生课程 18.335: Introduction to numerical method 供你参考。

微分方程

如果世间万物的运动发展都能用方程来刻画和描述，这是一件多么酷的事情呀！虽然几乎任何一所学校的 CS 培养方案中都没有微分方程相关的必修课程，但我还是觉得掌握它会赋予你一个新的视角来审视这个世界。

由于微分方程中往往会用到很多复变函数的知识，所以大家可以参考 MIT18.04: Complex variables functions 的课程 notes 来补齐先修知识。

MIT18.03: differential equations 主要覆盖了常微分方程的求解，在此基础之上 MIT18.152: Partial differential equations 则会深入偏微分方程的建模与求解。掌握了微分方程这一有力工具，相信对于你的实际问题的建模能力以及从众多噪声变量中把握本质的直觉都会有很大帮助。

数学高阶

作为计算机系的学生，我经常听到数学无用论的论断，对此我不敢苟同但也无权反对，但若凡事都硬要争出个有用和无用的区别来，倒也着实无趣，因此下面这些面向高年级甚至研究生的数学课程，大家按兴趣自取所需。

凸优化

Standford EE364A: Convex Optimization

信息论

MIT6.441: Information Theory

应用统计学

MIT18.650: Statistics for Applications

初等数论

MIT18.781: Theory of Numbers

密码学

Standford CS255: Cryptography

编程入门

Languages are tools, you choose the right tool to do the right thing. Since there's no universally perfect tool, there's no universally perfect language.

Shell

Python

C++

Rust

OCaml

电子基础

电路基础

作为计算机系的学生，了解一些基础的电路知识，感受从传感器收集数据到数据分析再到算法预测整条流水线，对于后续知识的学习以及计算思维的培养还是很有帮助的。EE16A&B: Designing Information Devices and Systems I&II 是伯克利 EE 学生的大一入门课，其中 EE16A 注重通过电路从实际环境中收集和分析数据，而 EE16B 则侧重从这些收集到的数据进行分析并做出预测行为。

信号与系统

信号与系统是一门我觉得非常值得一上的课，最初学它只是为了满足我对傅里叶变换的好奇，但学完之后我才不禁感叹，傅立叶变换给我提供了一个全新的视角去看待这个世界，就如同微分方程一样，让你沉浸在用数学去精确描绘和刻画这个世界的优雅与神奇之中。

MIT 6.003: signal and systems 提供了全部的课程录影、书面作业以及答案。也可以去看这门课的远古版本

而 UCB EE120: Signal and Systems 关于傅立叶变换的 notes 写得非常好，并且提供了6 个非常有趣的 Python 编程作业，让你实践中运用信号与系统的理论与算法。

数据结构与算法

算法是计算机科学的核心，也是几乎一切专业课程的基础。如何将实际问题通过数学抽象转化为算法问题，并选用合适的数据结构在时间和内存大小的限制下将其解决是算法课的永恒主题。如果你受够了老师的照本宣科，那么我强烈推荐伯克利的 UCB CS61B: Data Structures and Algorithms 和普林斯顿的 Coursera: Algorithms I & II，这两门课的都讲得深入浅出并且会有丰富且有趣的编程实验将理论与知识结合起来。此外，对一些更高级的算法以及 NP 问题感兴趣的同学可以学习伯克利的算法设计与分析课程 UCB CS170: Efficient Algorithms and Intractable Problems。

软件工程

入门课

一份“能跑”的代码，和一份高质量的工业级代码是有本质区别的。因此我非常推荐低年级的同学学习一下 MIT 6.031: Software Construction 这门课，它会以 Java 语言为基础，以丰富细致的阅读材料和精心设计的编程练习传授如何编写不易出 bug、简明易懂、易于维护修改的高质量代码。大到宏观数据结构设计，小到如何写注释，遵循这些前人总结的细节和经验，对于你此后的编程生涯大有裨益。

专业课

当然，如果你想系统性地上一门软件工程的课程，那我推荐的是伯克利的 UCB CS169: software engineering。但需要提醒的是，和大多学校（包括贵校）的软件工程课程不同，这门课不会涉及传统的 design and document 模式，即强调各种类图、流程图及文档设计，而是采用近些年流行起来的小团队快速迭代 Agile Develepment 开发模式以及利用云平台的 Software as a service 服务模式。

体系结构

入门课

从小我就一直听说，计算机的世界是由 01 构成的，我不理解但大受震撼。如果你的内心也怀有这份好奇，不妨花一到两个月的时间学习 Coursera: Nand2Tetris 这门无门槛的计算机课程。这门麻雀虽小五脏俱全的课程会从 01 开始让你亲手造出一台计算机，并在上面运行俄罗斯方块小游戏。一门课里涵盖了编译、虚拟机、汇编、体系结构、数字电路、逻辑门等等从上至下、从软至硬的各类知识，非常全面。难度上也是通过精心的设计，略去了众多现代计算机复杂的细节，提取出了最核心本质的东西，力图让每个人都能理解。在低年级，如果就能从宏观上建立对整个计算机体系的鸟瞰图，是大有裨益的。

专业课

当然，如果想深入现代计算机体系结构的复杂细节，还得上一门大学本科难度的课程 UCB CS61C: Great Ideas in Computer Architecture。UC Berkeley 作为 RISC-V 架构的发源地，在体系结构领域算得上首屈一指。其课程非常注重实践，你会在 Project 中手写汇编构造神经网络，从零开始搭建一个 CPU，这些实践都会让你对计算机体系结构有更为深入的理解，而不是仅停留于“取指译码执行访存写回”的单调背诵里。

系统入门

计算机系统是一个庞杂而深刻的主题，在深入学习某个细分领域之前，对各个领域有一个宏观概念性的理解，对一些通用性的设计原则有所知晓，会让你在之后的深入学习中不断强化一些最为核心乃至哲学的概念，而不会桎梏于复杂的内部细节和各种 trick。因为在我看来，学习系统最关键的还是想让你领悟到这些最核心的东西，从而能够设计和实现出属于自己的系统。

MIT6.033: System Engineering 是 MIT 的系统入门课，主题涉及了操作系统、网络、分布式和系统安全，除了知识点的传授外，这门课还会讲授一些写作和表达上的技巧，让你学会如何设计并向别人介绍和分析自己的系统。这本书配套的教材 Principles of Computer System Design: An Introduction 也写得非常好，推荐大家阅读。

CMU 15-213: Introduction to Computer System 是 CMU 的系统入门课，内容覆盖了体系结构、操作系统、链接、并行、网络等等，兼具广度和深度，配套的教材 Computer Systems: A Programmer's Perspective 也是质量极高，强烈建议阅读。

操作系统

没有什么能比自己写个内核更能加深对操作系统的理解了。

操作系统作为各类纷繁复杂的底层硬件虚拟化出一套规范优雅的抽象，给所有应用软件提供丰富的功能支持。了解操作系统的设计原则和内部原理对于一个不满足于当调包侠的程序员来说是大有裨益的。出于对操作系统的热爱，我上过国内外很多操作系统课程，它们各有侧重和优劣，大家可以根据兴趣各取所需。

MIT 6.S081: Operating System Engineering，MIT 著名 PDOS 实验室出品，11 个 Project 让你在一个实现非常优雅的类Unix操作系统xv6上增加各类功能模块。这门课也让我深刻认识到，做系统不是靠 PPT 念出来的，是得几万行代码一点点累起来的。

UCB CS162: Operating System，伯克利的操作系统课，采用和 Stanford 同样的 Project —— 一个教学用操作系统 Pintos。我作为北京大学2022年春季学期操作系统实验班的助教，引入并改善了这个 Project，课程资源也会全部开源，具体参见课程网站。

NJU: Operating System Design and Implementation，南京大学的蒋炎岩老师开设的操作系统课程。蒋老师以其独到的系统视角结合丰富的代码示例将众多操作系统的概念讲得深入浅出，此外这门课的全部课程内容都是中文的，非常方便大家学习。

并行与分布式系统

想必这两年各类 CS 讲座里最常听到的话就是“摩尔定律正在走向终结”，此话不假，当单核能力达到上限时，多核乃至众核架构如日中天。硬件的变化带来的是上层编程逻辑的适应与改变，要想充分利用硬件性能，编写并行程序几乎成了程序员的必备技能。与此同时，深度学习的兴起对计算机算力与存储的要求都达到了前所未有的高度，大规模集群的部署和优化也成为热门技术话题。

并行计算

CMU 15-418/Stanford CS149: Parallel Computing

分布式系统

MIT 6.824: Distributed System

系统安全

不知道你当年选择计算机是不是因为怀着一个中二的黑客梦想，但现实却是成为黑客道阻且长。

理论课程

UCB CS161: Computer Security 是伯克利的系统安全课程，会涵盖栈攻击、密码学、网站安全、网络安全等等内容。

实践课程

掌握这些理论知识之后，还需要在实践中培养和锻炼这些“黑客素养”。CTF 夺旗赛是一项比较热门的系统安全比赛，赛题中会融会贯通地考察你对计算机各个领域知识的理解和运用。北大今年也成功举办了第 0 届和第 1 届，鼓励大家后期踊跃参与，在实践中提高自己。下面列举一些我平时学习（摸鱼）用到的资源：

计算机网络

没有什么能比自己写个 TCP/IP 协议栈更能加深对计算机网络的理解了。

大名鼎鼎的 Stanford CS144: Computer Network，8 个 Project 带你实现整个 TCP/IP 协议栈。

如果你只是想在理论上对计算机网络有所了解，那么推荐计网著名教材《自顶向下方法》的配套学习资源 Computer Networking: A Top-Down Approach。

数据库系统

没有什么能比自己写个关系型数据库更能加深对数据库系统的理解了。

CMU 的著名数据库神课 CMU 15-445: Introduction to Database System 会通过 4 个 Project 带你为一个用于教学的关系型数据库 bustub 添加各种功能。实验的评测框架也免费开源了，非常适合大家自学。此外课程实验会用到 C++11 的众多新特性，也是一个锻炼 C++ 代码能力的好机会。

Berkeley 作为著名开源数据库 postgres 的发源地也不遑多让，UCB CS186: Introduction to Database System 会让你用 Java 语言实现一个支持 SQL 并发查询、B+ 树索引和故障恢复的关系型数据库。

编译原理

没有什么能比自己写个编译器更能加深对编译器的理解了。

Stanford CS143: Compilers 带你手写编译器。

Web开发

前后端开发很少在计算机的培养方案里被重视，但其实掌握这项技能还是好处多多的，例如搭建自己的个人主页，抑或是给自己的课程项目做一个精彩的展示网页。

两周速成版

MIT web development course

系统学习版

Stanford CS142: Web Applications

计算机图形学

数据科学

UCB Data100: Principles and Techniques of Data Science

人工智能

入门课

Harvard CS50’s Introduction to AI with Python

专业课

UCB CS188: Introduction to Artificial Intelligence

机器学习

入门课

Coursera: Machine Learning

专业课

深度学习

入门课

计算机视觉

Stanford CS231n: CNN for Visual Recognition

自然语言处理

Stanford CS224n: Natural Language Processing

图神经网络

Stanford CS224w: Machine Learning with Graphs

强化学习

UCB CS285: Deep Reinforcement Learning

定制属于你的课程地图

授人以鱼不如授人以渔。

以上的课程规划难免带有强烈的个人偏好，不一定适合所有人，更多是起到抛砖引玉的作用。如果你想挑选自己感兴趣的方向和内容加以学习，可以参考我在下面列出来的资源。

最后更新: 2022年10月8日
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前言

最近更新：英文版正在建设中，增加陈天奇机器学习编译，增加 CMU 机器学习系统， ～

这是一本计算机的自学指南，也是对自己大学三年自学生涯的一个纪念。

这同时也是一份献给北大信科学弟学妹们的礼物。如果这本书能对你们的信科生涯有哪怕一丝一毫的帮助，都是对我极大的鼓励和慰藉。

本书目前包括了以下部分(如果你有其他好的建议，或者想加入贡献者的行列，欢迎邮件 zhongyinmin@pku.edu.cn 或者在 issue 里提问)：

  • 必学工具：IDE, 翻墙, StackOverflow, Git, GitHub, Vim, LaTeX, GNU Make, 实用工具 ...
  • 环境配置：PC端以及服务器端开发环境配置、各类运维相关教材及资料 ...
  • 经典书籍推荐：看过 CSAPP 这本书的同学一定感叹好书的重要，我将列举推荐自己看过的计算机领域的必看好书与资源链接。
  • 国外高质量 CS 课程汇总：我将把我上过的所有高质量的国外 CS 课程分门别类进行汇总，并给出相关的自学建议，大部分课程都会有一个独立的仓库维护相关的资源以及我的作业实现。

梦开始的地方 —— CS61A

大一入学时我是一个对计算机一无所知的小白，装了几十个 G 的 Visual Studio 天天和 OJ 你死我活。凭着高中的数学底子我数学课学得还不错，但在专业课上对竞赛大佬只有仰望。提到编程我只会打开那笨重的 IDE，新建一个我也不知道具体是干啥的命令行项目，然后就是 cin, cout, for 循环，然后 CE, RE, WA 循环。当时的我就处在一种拼命想学好但不知道怎么学，课上认真听讲但题还不会做，课后做作业完全是用时间和它硬耗的痛苦状态。我至今电脑里还存着自己大一上学期计算概论大作业的源代码 —— 一个 1200 行的 C++ 文件，没有头文件、没有类、没有封装、没有 unit test、没有 Makefile、没有 Git，唯一的优点是它确实能跑，缺点是“能跑”的补集。我一度怀疑我是不是不适合学计算机，因为童年对于极客的所有想象，已经被我第一个学期的体验彻底粉碎了。

这一切的转机发生在我大一的寒假，我心血来潮想学习 Python。无意间看到知乎有人推荐了 CS61A 这门课，说是 UC Berkeley 的大一入门课程，讲的就是 Python。我永远不会忘记那一天，打开 CS61A 课程网站的那个瞬间，就像哥伦布发现了新大陆一样，我开启了新世界的大门。

我一口气 3 个星期上完了这门课，它让我第一次感觉到原来 CS 可以学得如此充实而有趣，原来这世上竟有如此精华的课程。

为避免有崇洋媚外之嫌，我单纯从一个学生的视角来讲讲自学 CS61A 的体验：

  • 独立搭建的课程网站: 一个网站将所有课程资源整合一体，条理分明的课程 schedule、所有 slides, hw, discussion 的文件链接、详细明确的课程给分说明、历年的考试题与答案。这样一个网站抛开美观程度不谈，既方便学生，也让资源公正透明。

  • 课程教授亲自编写的教材：CS61A 这门课的开课老师将MIT的经典教材 Structure and Interpretation of Computer Programs (SICP) 用Python这门语言进行改编（原教材基于 Scheme 语言），保证了课堂内容与教材内容的一致性，同时补充了更多细节，可以说诚意满满。而且全书开源，可以直接线上阅读。

  • 丰富到让人眼花缭乱的课程作业：14 个 lab 巩固随堂知识点，10 个 homework，还有 4 个代码量均上千行的 project。与大家熟悉的 OJ 和 Word 文档式的作业不同，所有作业均有完善的代码框架，保姆级的作业说明。每个 Project 都有详尽的 handout 文档、全自动的评分脚本。CS61A 甚至专门开发了一个自动化的作业提交评分系统（据说还发了论文）。当然，有人会说“一个 project 几千行代码大部分都是助教帮你写好的，你还能学到啥？”。此言差矣，作为一个刚刚接触计算机，连安装 Python 都磕磕绊绊的小白来说，这样完善的代码框架既可以让你专注于巩固课堂上学习到的核心知识点，又能有“我才学了一个月就能做一个小游戏了！”的成就感，还能有机会阅读学习别人高质量的代码，从而为自己所用。我觉得在低年级，这种代码框架可以说百利而无一害。唯一的害也许是苦了老师和助教，因为开发这样的作业可想而知需要相当的时间投入。

  • 每周 Discussion 讨论课，助教会讲解知识难点和考试例题：类似于北京大学 ICS 的小班研讨，但习题全部用 LaTeX 撰写，相当规范且会明确给出 solution。

这样的课程，你完全不需要任何计算机的基础，你只需要努力、认真、花时间就够了。此前那种有劲没处使的感觉，那种付出再多时间却得不到回报的感觉，从此烟消云散。这太适合我了，我从此爱上了自学。

试想如果有人能把艰深的知识点嚼碎嚼烂，用生动直白的方式呈现给你，还有那么多听起来就很 fancy，种类繁多的 project 来巩固你的理论知识，你会觉得他们真的是在倾尽全力想方设法地让你完全掌握这门课，你会觉得不学好它简直是对这些课程建设者的侮辱。

如果你觉得我在夸大其词，那么不妨从 CS61A 开始，因为它是我的梦开始的地方。

为什么写这本书

在我2020年秋季学期担任《深入理解计算机系统》（CSAPP）这门课的助教时，我已经自学一年多了。这一年多来我无比享受这种自学模式，为了分享这种快乐，我为自己的小班同学做过一个 CS自学资料整理仓库。当时纯粹是心血来潮，因为我也不敢公然鼓励大家翘课自学。

但随着又一年时间的维护，这个仓库的内容已经相当丰富，基本覆盖了计科、智能系、软工系的绝大多数课程，我也为每个课程都建了各自的 GitHub 仓库，汇总我用到的自学资料以及作业实现。

直到大四开始凑学分毕业的时候，我打开自己的培养方案，我发现它已经是我这个自学仓库的子集了，而这距离我开始自学也才两年半而已。于是，一个大胆的想法在我脑海中浮现：也许，我可以打造一个自学式的培养方案，把我这三年自学经历中遇到的坑、走过的路记录下来，以期能为后来的学弟学妹们贡献自己的一份微薄之力。

如果大家可以在三年不到的时间里就能建立起整座CS的基础大厦，能有相对扎实的数学功底和代码能力，经历过数十个千行代码量的 Project 的洗礼，掌握至少 C/C++/Java/JS/Python/Go/Rust 等主流语言，对算法、电路、体系、网络、操统、编译、人工智能、机器学习、计算机视觉、自然语言处理、强化学习、密码学、信息论、博弈论、数值分析、统计学、分布式、数据库、图形学、Web开发、云服务、超算等等方面均有涉猎。我想，你将有足够的底气和自信选择自己感兴趣的方向，无论是就业还是科研，你都将有相当的竞争力。

因为我坚信，既然你能坚持听我 BB 到这里，你一定不缺学好 CS 的能力，你只是没有一个好的老师，给你讲一门好的课程。而我，将力图根据我三年的体验，为你挑选这样的课程。

自学的好处

对我来说，自学最大的好处就在于可以完全根据自己的进度来调整学习速度。对于一些疑难知识点，我可以反复回看视频，在网上谷歌相关的内容，上 StackOverflow 提问题，直到完全将它弄明白。而对于自己掌握得相对较快的内容，则可以两倍速甚至三倍速略过。

自学的另一大好处就是博采众长。计算机系的几大核心课程：体系、网络、操统、编译，每一门我基本都上过不同大学的课程，不同的教材、不同的知识点侧重、不同的 project 将会极大丰富你的视野，也会让你理解错误的一些内容得到及时纠正。

自学的第三个好处是时间自由，具体原因省略。

自学的坏处

当然，作为 CS 自学主义的忠实拥趸，我不得不承认自学也有它的坏处。

第一就是交流沟通的不便。我其实是一个很热衷于提问的人，对于所有没有弄明白的点，我都喜欢穷追到底。但当你面对着屏幕听到老师讲了一个你没明白的知识点的时候，你无法顺着网线到另一端向老师问个明白。我努力通过独立思考和善用 Google 来缓解这一点，但是，如果能有几个志同道合的伙伴结伴自学，那将是极好的。关于交流群的建立，大家可以参考仓库 README 中的教程。

第二就是这些自学的课程基本都是英文的。从视频到slides到作业全是英文，所以有一定的门槛。不过我觉得这个挑战如果你克服了的话对你是极为有利的。因为在当下，虽然我很不情愿，但也不得不承认，在计算机领域，很多优质的文档、论坛、网站都是全英文的。养成英文阅读的习惯，在赤旗插遍世界之前，还是有一定好处的（狗头保命）。

第三，也是我觉得最困难的一点，就是自律。因为没有 DDL 有时候真的是一件可怕的事情，特别是随着学习的深入，国外的很多课程是相当虐的。你得有足够的驱动力强迫自己静下心来，阅读几十页的 Project Handout，理解上千行的代码框架，忍受数个小时的 debug 时光。而这一切，没有学分，没有绩点，没有老师，没有同学，只有一个信念 —— 你在变强。

这本书适合谁

正如我在前言里说的，任何有志于自学计算机的朋友都可以参考这本书。如果你已经有了一定的计算机基础，只是对某个特定的领域感兴趣，可以选择性地挑选你感兴趣的内容进行学习。当然，如果你是一个像我当年一样对计算机一无所知的小白，初入大学的校门，我希望这本书能成为你的攻略，让你花最少的时间掌握你所需要的知识和能力。某种程度上，这本书更像是一个根据我的体验来排序的课程搜索引擎，帮助大家足不出户，体验世界顶级名校的计算机优质课程。

当然，作为一个还未毕业的本科生，我深感自己没有能力也没有权利去宣扬一种学习方式，我只是希望这份资料能让那些同样有自学之心和毅力朋友可以少走些弯路，收获更丰富、更多样、更满足的学习体验。

特别鸣谢

在这里，我怀着崇敬之心真诚地感谢所有将课程资源无偿开源的各位教授们。这些课程倾注了他们数十年教学生涯的积淀和心血，他们却选择无私地让所有人享受到如此高质量的CS教育。没有他们，我的大学生活不会这样充实而快乐。很多教授在我给他们发了感谢邮件之后，甚至会回复上百字的长文，真的让我无比感动。他们也时刻激励着我，做一件事，就得用心做好，无论是科研，还是为人。

你也想加入到贡献者的行列

一个人的力量终究是有限的，这本书也是我在繁重的科研之余熬夜抽空写出来的，难免有不够完善之处。另外，由于个人做的是系统方向，很多课程侧重系统领域，对于数学、理论计算机、高级算法相关的内容则相对少些。如果有大佬想在其他领域分享自己的自学经历与资源，可以直接在项目中发起 Pull Request，也欢迎和我邮件联系（zhongyinmin@pku.edu.cn）。

关于交流群的建立

方法参见仓库的 README.md。

最后更新: 2022年9月15日

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前言

最近更新：英文版正在建设中，增加陈天奇机器学习编译，增加 CMU 机器学习系统

这是一本计算机的自学指南，也是对自己大学三年自学生涯的一个纪念。

这同时也是一份献给北大信科学弟学妹们的礼物。如果这本书能对你们的信科生涯有哪怕一丝一毫的帮助，都是对我极大的鼓励和慰藉。

本书目前包括了以下部分(如果你有其他好的建议，或者想加入贡献者的行列，欢迎邮件 zhongyinmin@pku.edu.cn 或者在 issue 里提问)：

  • 必学工具：IDE, 翻墙, StackOverflow, Git, GitHub, Vim, LaTeX, GNU Make, 实用工具 ...
  • 环境配置：PC端以及服务器端开发环境配置、各类运维相关教材及资料 ...
  • 经典书籍推荐：看过 CSAPP 这本书的同学一定感叹好书的重要，我将列举推荐自己看过的计算机领域的必看好书与资源链接。
  • 国外高质量 CS 课程汇总：我将把我上过的所有高质量的国外 CS 课程分门别类进行汇总，并给出相关的自学建议，大部分课程都会有一个独立的仓库维护相关的资源以及我的作业实现。

梦开始的地方 —— CS61A

大一入学时我是一个对计算机一无所知的小白，装了几十个 G 的 Visual Studio 天天和 OJ 你死我活。凭着高中的数学底子我数学课学得还不错，但在专业课上对竞赛大佬只有仰望。提到编程我只会打开那笨重的 IDE，新建一个我也不知道具体是干啥的命令行项目，然后就是 cin, cout, for 循环，然后 CE, RE, WA 循环。当时的我就处在一种拼命想学好但不知道怎么学，课上认真听讲但题还不会做，课后做作业完全是用时间和它硬耗的痛苦状态。我至今电脑里还存着自己大一上学期计算概论大作业的源代码 —— 一个 1200 行的 C++ 文件，没有头文件、没有类、没有封装、没有 unit test、没有 Makefile、没有 Git，唯一的优点是它确实能跑，缺点是“能跑”的补集。我一度怀疑我是不是不适合学计算机，因为童年对于极客的所有想象，已经被我第一个学期的体验彻底粉碎了。

这一切的转机发生在我大一的寒假，我心血来潮想学习 Python。无意间看到知乎有人推荐了 CS61A 这门课，说是 UC Berkeley 的大一入门课程，讲的就是 Python。我永远不会忘记那一天，打开 CS61A 课程网站的那个瞬间，就像哥伦布发现了新大陆一样，我开启了新世界的大门。

我一口气 3 个星期上完了这门课，它让我第一次感觉到原来 CS 可以学得如此充实而有趣，原来这世上竟有如此精华的课程。

为避免有崇洋媚外之嫌，我单纯从一个学生的视角来讲讲自学 CS61A 的体验：

  • 独立搭建的课程网站: 一个网站将所有课程资源整合一体，条理分明的课程 schedule、所有 slides, hw, discussion 的文件链接、详细明确的课程给分说明、历年的考试题与答案。这样一个网站抛开美观程度不谈，既方便学生，也让资源公正透明。

  • 课程教授亲自编写的教材：CS61A 这门课的开课老师将MIT的经典教材 Structure and Interpretation of Computer Programs (SICP) 用Python这门语言进行改编（原教材基于 Scheme 语言），保证了课堂内容与教材内容的一致性，同时补充了更多细节，可以说诚意满满。而且全书开源，可以直接线上阅读。

  • 丰富到让人眼花缭乱的课程作业：14 个 lab 巩固随堂知识点，10 个 homework，还有 4 个代码量均上千行的 project。与大家熟悉的 OJ 和 Word 文档式的作业不同，所有作业均有完善的代码框架，保姆级的作业说明。每个 Project 都有详尽的 handout 文档、全自动的评分脚本。CS61A 甚至专门开发了一个自动化的作业提交评分系统（据说还发了论文）。当然，有人会说“一个 project 几千行代码大部分都是助教帮你写好的，你还能学到啥？”。此言差矣，作为一个刚刚接触计算机，连安装 Python 都磕磕绊绊的小白来说，这样完善的代码框架既可以让你专注于巩固课堂上学习到的核心知识点，又能有“我才学了一个月就能做一个小游戏了！”的成就感，还能有机会阅读学习别人高质量的代码，从而为自己所用。我觉得在低年级，这种代码框架可以说百利而无一害。唯一的害也许是苦了老师和助教，因为开发这样的作业可想而知需要相当的时间投入。

  • 每周 Discussion 讨论课，助教会讲解知识难点和考试例题：类似于北京大学 ICS 的小班研讨，但习题全部用 LaTeX 撰写，相当规范且会明确给出 solution。

这样的课程，你完全不需要任何计算机的基础，你只需要努力、认真、花时间就够了。此前那种有劲没处使的感觉，那种付出再多时间却得不到回报的感觉，从此烟消云散。这太适合我了，我从此爱上了自学。

试想如果有人能把艰深的知识点嚼碎嚼烂，用生动直白的方式呈现给你，还有那么多听起来就很 fancy，种类繁多的 project 来巩固你的理论知识，你会觉得他们真的是在倾尽全力想方设法地让你完全掌握这门课，你会觉得不学好它简直是对这些课程建设者的侮辱。

如果你觉得我在夸大其词，那么不妨从 CS61A 开始，因为它是我的梦开始的地方。

为什么写这本书

在我2020年秋季学期担任《深入理解计算机系统》（CSAPP）这门课的助教时，我已经自学一年多了。这一年多来我无比享受这种自学模式，为了分享这种快乐，我为自己的小班同学做过一个 CS自学资料整理仓库。当时纯粹是心血来潮，因为我也不敢公然鼓励大家翘课自学。

但随着又一年时间的维护，这个仓库的内容已经相当丰富，基本覆盖了计科、智能系、软工系的绝大多数课程，我也为每个课程都建了各自的 GitHub 仓库，汇总我用到的自学资料以及作业实现。

直到大四开始凑学分毕业的时候，我打开自己的培养方案，我发现它已经是我这个自学仓库的子集了，而这距离我开始自学也才两年半而已。于是，一个大胆的想法在我脑海中浮现：也许，我可以打造一个自学式的培养方案，把我这三年自学经历中遇到的坑、走过的路记录下来，以期能为后来的学弟学妹们贡献自己的一份微薄之力。

如果大家可以在三年不到的时间里就能建立起整座CS的基础大厦，能有相对扎实的数学功底和代码能力，经历过数十个千行代码量的 Project 的洗礼，掌握至少 C/C++/Java/JS/Python/Go/Rust 等主流语言，对算法、电路、体系、网络、操统、编译、人工智能、机器学习、计算机视觉、自然语言处理、强化学习、密码学、信息论、博弈论、数值分析、统计学、分布式、数据库、图形学、Web开发、云服务、超算等等方面均有涉猎。我想，你将有足够的底气和自信选择自己感兴趣的方向，无论是就业还是科研，你都将有相当的竞争力。

因为我坚信，既然你能坚持听我 BB 到这里，你一定不缺学好 CS 的能力，你只是没有一个好的老师，给你讲一门好的课程。而我，将力图根据我三年的体验，为你挑选这样的课程。

自学的好处

对我来说，自学最大的好处就在于可以完全根据自己的进度来调整学习速度。对于一些疑难知识点，我可以反复回看视频，在网上谷歌相关的内容，上 StackOverflow 提问题，直到完全将它弄明白。而对于自己掌握得相对较快的内容，则可以两倍速甚至三倍速略过。

自学的另一大好处就是博采众长。计算机系的几大核心课程：体系、网络、操统、编译，每一门我基本都上过不同大学的课程，不同的教材、不同的知识点侧重、不同的 project 将会极大丰富你的视野，也会让你理解错误的一些内容得到及时纠正。

自学的第三个好处是时间自由，具体原因省略。

自学的坏处

当然，作为 CS 自学主义的忠实拥趸，我不得不承认自学也有它的坏处。

第一就是交流沟通的不便。我其实是一个很热衷于提问的人，对于所有没有弄明白的点，我都喜欢穷追到底。但当你面对着屏幕听到老师讲了一个你没明白的知识点的时候，你无法顺着网线到另一端向老师问个明白。我努力通过独立思考和善用 Google 来缓解这一点，但是，如果能有几个志同道合的伙伴结伴自学，那将是极好的。关于交流群的建立，大家可以参考仓库 README 中的教程。

第二就是这些自学的课程基本都是英文的。从视频到slides到作业全是英文，所以有一定的门槛。不过我觉得这个挑战如果你克服了的话对你是极为有利的。因为在当下，虽然我很不情愿，但也不得不承认，在计算机领域，很多优质的文档、论坛、网站都是全英文的。养成英文阅读的习惯，在赤旗插遍世界之前，还是有一定好处的（狗头保命）。

第三，也是我觉得最困难的一点，就是自律。因为没有 DDL 有时候真的是一件可怕的事情，特别是随着学习的深入，国外的很多课程是相当虐的。你得有足够的驱动力强迫自己静下心来，阅读几十页的 Project Handout，理解上千行的代码框架，忍受数个小时的 debug 时光。而这一切，没有学分，没有绩点，没有老师，没有同学，只有一个信念 —— 你在变强。

这本书适合谁

正如我在前言里说的，任何有志于自学计算机的朋友都可以参考这本书。如果你已经有了一定的计算机基础，只是对某个特定的领域感兴趣，可以选择性地挑选你感兴趣的内容进行学习。当然，如果你是一个像我当年一样对计算机一无所知的小白，初入大学的校门，我希望这本书能成为你的攻略，让你花最少的时间掌握你所需要的知识和能力。某种程度上，这本书更像是一个根据我的体验来排序的课程搜索引擎，帮助大家足不出户，体验世界顶级名校的计算机优质课程。

当然，作为一个还未毕业的本科生，我深感自己没有能力也没有权利去宣扬一种学习方式，我只是希望这份资料能让那些同样有自学之心和毅力朋友可以少走些弯路，收获更丰富、更多样、更满足的学习体验。

特别鸣谢

在这里，我怀着崇敬之心真诚地感谢所有将课程资源无偿开源的各位教授们。这些课程倾注了他们数十年教学生涯的积淀和心血，他们却选择无私地让所有人享受到如此高质量的CS教育。没有他们，我的大学生活不会这样充实而快乐。很多教授在我给他们发了感谢邮件之后，甚至会回复上百字的长文，真的让我无比感动。他们也时刻激励着我，做一件事，就得用心做好，无论是科研，还是为人。

你也想加入到贡献者的行列

一个人的力量终究是有限的，这本书也是我在繁重的科研之余熬夜抽空写出来的，难免有不够完善之处。另外，由于个人做的是系统方向，很多课程侧重系统领域，对于数学、理论计算机、高级算法相关的内容则相对少些。如果有大佬想在其他领域分享自己的自学经历与资源，可以直接在项目中发起 Pull Request，也欢迎和我邮件联系（zhongyinmin@pku.edu.cn）。

关于交流群的建立

方法参见仓库的 README.md。

最后更新: 2022年10月8日
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