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 95% 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，它会提高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。
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。
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。
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。
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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。但之所以都列出来，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
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 操作系统作为所有应用软件和底层硬件交互的掌舵者，了解它的内部原理和设计原则对于一个不满足于调包侠的程序员来说是很有帮助的。同时，国外操统课程的质量也是让上了多年网课的我也感到瞠目结舌。
MIT 6.S081: Operating System Engineering ，MIT 著名 PDOS 实验室出品，11 个 Project 会让你在一个 2 万多行的教学用迷你操作系统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。我作为下学期北京大学操作系统实验班的助教，会尝试引入这个 Project，欢迎大家选课尝试，同时课程资源也会全部开源，目前课程网站 正在建设当中。
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。同时如今深度学习的兴起导致对计算机算力与存储的要求都达到了前所未有的高度，因此并行和分布式系统已成为一项热门技术话题。
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“安全素养”。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 ，也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 协议栈更能加深自己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自己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自己对编译器的理解了。
Stanford CS143: Compilers
计算机图形学 Games101
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
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: May 11, 2022
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- Stanford CS142: Web Applications - CS自学指南
Stanford CS142: Web Applications 课程简介 所属大学：Stanford 先修要求：有一定的编程经验 编程语言：JavaScript/HTML/CSS 课程难度：🌟🌟🌟🌟 预计学时：100 小时 斯坦福的 Web 应用开发课程，内容覆盖了 HTML, CSS, JavaScript, ReactJs, NodeJS, ExpressJS, Web安全等等。8 个 Project 会让你在实战中锻炼自己的 Web 开发技巧。
课程资源 最后更新: April 3, 2022
Stanford CS142: Web Applications 课程简介 所属大学：Stanford 先修要求：有一定的编程经验 编程语言：JavaScript/HTML/CSS 课程难度：🌟🌟🌟🌟 预计学时：100 小时 斯坦福的 Web 应用开发课程，内容覆盖了 HTML, CSS, JavaScript, ReactJs, NodeJS, ExpressJS, Web安全等等。8 个 Project 会让你在实战中锻炼自己的 Web 开发技巧。
课程资源 最后更新: April 3, 2022
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- MIT web development course - CS自学指南
MIT Web Development Crash Course 课程简介 所属大学：MIT 先修要求：掌握至少一门编程语言 编程语言：JavaScript/HTML/CSS/NoSQL 课程难度：🌟🌟🌟 预计学时：因人而异 MIT 在每年 1 月份会有一个为期 4 周的 Independent Activities Period (IAP)，在这个月里，MIT 的学生和老师可以自由地开设很多有趣的课程，而这门网站开发课程就是其中之一。
在一个月的时间里，你会从零开始掌握一个网站的设计、搭建、美化、交互等等核心内容，基本覆盖了 Web 开发的前后端大部分技术栈。如果你不需要系统地学习网络开发，而只是出于兴趣想把它加入自己的技能包里，那么这门课将非常适合你。
课程资源 最后更新: April 3, 2022
MIT Web Development Crash Course 课程简介 所属大学：MIT 先修要求：掌握至少一门编程语言 编程语言：JavaScript/HTML/CSS/NoSQL 课程难度：🌟🌟🌟 预计学时：因人而异 MIT 在每年 1 月份会有一个为期 4 周的 Independent Activities Period (IAP)，在这个月里，MIT 的学生和老师可以自由地开设很多有趣的课程，而这门网站开发课程就是其中之一。
在一个月的时间里，你会从零开始掌握一个网站的设计、搭建、美化、交互等等核心内容，基本覆盖了 Web 开发的前后端大部分技术栈。如果你不需要系统地学习网络开发，而只是出于兴趣想把它加入自己的技能包里，那么这门课将非常适合你。
课程资源 最后更新: April 3, 2022
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- CS自学指南
前言 更新：本书最新网址 csdiy.wiki ，欢迎大家访问 ～
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。
本书目前规划了以下部分(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 ... 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。环境配置：Linux 基础，bash，环境变量，Anaconda ... 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。 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: May 6, 2022
前言 更新：本书最新网址 csdiy.wiki ，欢迎大家访问 ～
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。
本书目前规划了以下部分(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 ... 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。环境配置：Linux 基础，bash，环境变量，Anaconda ... 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。 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: May 6, 2022
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diff --git a/search/search_index.json b/search/search_index.json
index deab1c0a..0e5ccd52 100644
--- a/search/search_index.json
+++ b/search/search_index.json
@@ -1 +1 @@
-{"config":{"indexing":"full","lang":["ja"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"\u524d\u8a00 \u66f4\u65b0\uff1a\u672c\u4e66\u6700\u65b0\u7f51\u5740 csdiy.wiki \uff0c\u6b22\u8fce\u5927\u5bb6\u8bbf\u95ee \uff5e \u8fd9\u662f\u4e00\u672c\u8ba1\u7b97\u673a\u7684\u81ea\u5b66\u6307\u5357\uff0c\u4e5f\u662f\u5bf9\u81ea\u5df1\u5927\u5b66\u4e09\u5e74\u81ea\u5b66\u751f\u6daf\u7684\u4e00\u4e2a\u7eaa\u5ff5\u3002 \u8fd9\u540c\u65f6\u4e5f\u662f\u4e00\u4efd\u732e\u7ed9\u5317\u5927\u4fe1\u79d1\u5b66\u5f1f\u5b66\u59b9\u4eec\u7684\u793c\u7269\u3002\u5982\u679c\u8fd9\u672c\u4e66\u80fd\u5bf9\u4f60\u4eec\u7684\u4fe1\u79d1\u751f\u6daf\u6709\u54ea\u6015\u4e00\u4e1d\u4e00\u6beb\u7684\u5e2e\u52a9\uff0c\u90fd\u662f\u5bf9\u6211\u6781\u5927\u7684\u9f13\u52b1\u548c\u6170\u85c9\u3002 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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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DevOps-Guide \uff0c\u5176\u4e2d\u6db5\u76d6\u4e86\u975e\u5e38\u591a\u7684\u8fd0\u7ef4\u65b9\u9762\u7684\u57fa\u7840\u77e5\u8bc6\u548c\u6559\u7a0b\uff0c\u4f8b\u5982 Docker, Kubernetes, Linux, CI-CD, GitHub Actions \u7b49\u7b49\u3002 \u53e6\u5916\u5927\u5bb6\u53ef\u4ee5\u53c2\u8003\u4e00\u4efd\u7075\u611f\u6765\u81ea 6.NULL MIT-Missing-Semester \u7684 \u73af\u5883\u914d\u7f6e\u6307\u5357 \uff0c\u91cd\u70b9\u5728\u4e8e\u7ec8\u7aef\u7684\u7f8e\u5316\u914d\u7f6e\u3002\u6b64\u5916\u8fd8\u5305\u62ec\u5e38\u7528\u8f6f\u4ef6\u6e90\uff08\u5982 GitHub, Anaconda, PyPI \u7b49\uff09\u7684\u52a0\u901f\u4e0e\u66ff\u6362\u4ee5\u53ca\u4e00\u4e9b IDE \u7684\u914d\u7f6e\u4e0e\u6fc0\u6d3b\u6559\u7a0b\u3002 More contents under construction. \u8bfe\u7a0b\u5730\u56fe \u6b63\u5982\u8fd9\u7ae0\u5f00\u5934\u63d0\u5230\u7684\uff0c\u8fd9\u4efd\u8bfe\u7a0b\u5730\u56fe\u4ec5\u4ec5\u662f\u4e00\u4e2a \u4ec5\u4f9b\u53c2\u8003 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\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 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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\u7684Python\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 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\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\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 \u6700\u597d\u7684\u8ba1\u7b97\u673a\u57fa\u7840\u5165\u95e8\u8bfe\uff0c\u4f60\u7edd\u5bf9\u4f1a\u7231\u4e0a\u7684\u3002 UCB CS61A: Structure and Interpretation of Computer Programs C++ 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 Stanford CS106B/X: Programming Abstractions 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 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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 \u64cd\u4f5c\u7cfb\u7edf\u4f5c\u4e3a\u6240\u6709\u5e94\u7528\u8f6f\u4ef6\u548c\u5e95\u5c42\u786c\u4ef6\u4ea4\u4e92\u7684\u638c\u8235\u8005\uff0c\u4e86\u89e3\u5b83\u7684\u5185\u90e8\u539f\u7406\u548c\u8bbe\u8ba1\u539f\u5219\u5bf9\u4e8e\u4e00\u4e2a\u4e0d\u6ee1\u8db3\u4e8e\u8c03\u5305\u4fa0\u7684\u7a0b\u5e8f\u5458\u6765\u8bf4\u662f\u5f88\u6709\u5e2e\u52a9\u7684\u3002\u540c\u65f6\uff0c\u56fd\u5916\u64cd\u7edf\u8bfe\u7a0b\u7684\u8d28\u91cf\u4e5f\u662f\u8ba9\u4e0a\u4e86\u591a\u5e74\u7f51\u8bfe\u7684\u6211\u4e5f\u611f\u5230\u77a0\u76ee\u7ed3\u820c\u3002 MIT 6.S081: Operating System Engineering \uff0cMIT \u8457\u540d PDOS \u5b9e\u9a8c\u5ba4\u51fa\u54c1\uff0c11 \u4e2a Project \u4f1a\u8ba9\u4f60\u5728\u4e00\u4e2a 2 \u4e07\u591a\u884c\u7684\u6559\u5b66\u7528\u8ff7\u4f60\u64cd\u4f5c\u7cfb\u7edf\u4e0a\u589e\u52a0\u5404\u7c7b\u529f\u80fd\u6a21\u5757\u3002\u8fd9\u95e8\u8bfe\u4e5f\u8ba9\u6211\u6df1\u523b\u8ba4\u8bc6\u5230\uff0c\u505a\u7cfb\u7edf\u4e0d\u662f\u9760 PPT \u5ff5\u51fa\u6765\u7684\uff0c\u662f\u5f97\u51e0\u4e07\u884c\u4ee3\u7801\u4e00\u70b9\u70b9\u7d2f\u8d77\u6765\u7684\u3002 UCB CS162: Operating System \uff0c\u4f2f\u514b\u5229\u7684\u64cd\u4f5c\u7cfb\u7edf\u8bfe\uff0c\u91c7\u7528\u548c Stanford \u540c\u6837\u7684 Project \u2014\u2014 \u4e00\u4e2a\u6559\u5b66\u7528\u64cd\u4f5c\u7cfb\u7edf Pintos\u3002\u6211\u4f5c\u4e3a\u4e0b\u5b66\u671f\u5317\u4eac\u5927\u5b66\u64cd\u4f5c\u7cfb\u7edf\u5b9e\u9a8c\u73ed\u7684\u52a9\u6559\uff0c\u4f1a\u5c1d\u8bd5\u5f15\u5165\u8fd9\u4e2a 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\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 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 \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\u5b89\u5168\u7d20\u517b\u201d\u3002 CTF \u593a\u65d7\u8d5b 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\u534f\u8bae\u6808\u66f4\u80fd\u52a0\u6df1\u81ea\u5df1\u5bf9\u8ba1\u7b97\u673a\u7f51\u7edc\u7684\u7406\u89e3\u4e86\uff0c\u6240\u4ee5\u4e0d\u59a8\u8bd5\u8bd5 Stanford CS144: Computer Network \uff0c8 \u4e2a Project \u5e26\u4f60\u5b9e\u73b0\u6574\u4e2a\u534f\u8bae\u6808\u3002 \u6570\u636e\u5e93\u7cfb\u7edf \u6ca1\u6709\u4ec0\u4e48\u80fd\u6bd4\u81ea\u5df1\u5199\u4e2a\u5173\u7cfb\u578b\u6570\u636e\u5e93\u66f4\u80fd\u52a0\u6df1\u81ea\u5df1\u5bf9\u6570\u636e\u5e93\u7cfb\u7edf\u7684\u7406\u89e3\u4e86\u3002 C++\u7248 CMU 15-445: Introduction to Database System Java\u7248 UCB CS186: Introduction to Database System \u7f16\u8bd1\u539f\u7406 \u6ca1\u6709\u4ec0\u4e48\u80fd\u6bd4\u81ea\u5df1\u5199\u4e2a\u7f16\u8bd1\u5668\u66f4\u80fd\u52a0\u6df1\u81ea\u5df1\u5bf9\u7f16\u8bd1\u5668\u7684\u7406\u89e3\u4e86\u3002 Stanford CS143: Compilers \u8ba1\u7b97\u673a\u56fe\u5f62\u5b66 Games101 Web\u5f00\u53d1 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\u5165\u95e8\u8bfe Coursera: Deep Learning \u53f0\u6e7e\u56fd\u7acb\u5927\u5b66\uff1a\u674e\u5b8f\u6bc5\u673a\u5668\u5b66\u4e60 \u8ba1\u7b97\u673a\u89c6\u89c9 Stanford CS231n: CNN for Visual Recognition \u81ea\u7136\u8bed\u8a00\u5904\u7406 Stanford CS224n: Natural Language Processing \u56fe\u795e\u7ecf\u7f51\u7edc Stanford CS224w: Machine Learning with Graphs \u5f3a\u5316\u5b66\u4e60 UCB CS285: Deep Reinforcement Learning \u5b9a\u5236\u5c5e\u4e8e\u4f60\u7684\u8bfe\u7a0b\u5730\u56fe \u6388\u4eba\u4ee5\u9c7c\u4e0d\u5982\u6388\u4eba\u4ee5\u6e14\u3002 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UCB CS70 : discrete Math and probability theory \u548c UCB CS126 : Probability theory \u662f UC Berkeley 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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 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\u7684\u7814\u7a76\u751f\u8bfe\u7a0b 18.335: Introduction to numerical method \u4f9b\u4f60\u53c2\u8003\u3002","title":"\u6570\u503c\u5206\u6790"},{"location":"CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_11","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":"CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#shell","text":"MIT-Missing-Semester","title":"Shell"},{"location":"CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#python","text":"Harvard CS50: This is CS50x \u6700\u597d\u7684\u8ba1\u7b97\u673a\u57fa\u7840\u5165\u95e8\u8bfe\uff0c\u4f60\u7edd\u5bf9\u4f1a\u7231\u4e0a\u7684\u3002 UCB CS61A: Structure and Interpretation of Computer Programs","title":"Python"},{"location":"CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#c","text":"Stanford CS106L: Standard C++ Programming","title":"C++"},{"location":"CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#rust","text":"Stanford CS110L: Safety in Systems Programming","title":"Rust"},{"location":"CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#ocaml","text":"Cornell CS3110 textbook: Functional Programming in 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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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\u6c47\u7f16\u7f16\u5199\u4e00\u4e2a\u795e\u7ecf\u7f51\u7edc\uff0c\u7528\u6765 \u8bc6\u522b MNIST \u624b\u5199\u6570\u5b57\uff0c\u975e\u5e38\u953b\u70bc\u4f60\u5bf9\u6c47\u7f16\u4ee3\u7801\u7684\u7406\u89e3\u548c\u8fd0\u7528\u3002Project3 \u4e2d\u4f60\u4f1a\u7528 Logisim \u8fd9\u4e2a\u6570\u5b57\u7535\u8def\u6a21\u62df\u8f6f\u4ef6\u642d\u5efa\u51fa\u4e00\u4e2a\u4e8c\u7ea7\u6d41\u6c34\u7ebf\u7684 CPU\uff0c\u5e76\u5728\u4e0a\u9762\u8fd0\u884c RISC-V \u6c47\u7f16\u4ee3\u7801\u3002Project4 \u4f1a\u8ba9\u4f60\u4f7f\u7528 OpenMP, SIMD \u7b49\u65b9\u6cd5\u5e76\u884c\u4f18\u5316\u77e9\u9635\u8fd0\u7b97\uff0c\u5b9e\u73b0\u4e00\u4e2a\u7b80\u6613\u7684 Numpy\u3002 \u603b\u800c\u8a00\u4e4b\uff0c\u8fd9\u662f\u4e2a\u4eba\u4e0a\u8fc7\u7684\u6700\u597d\u7684\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784\u7684\u8bfe\u7a0b\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://cs61c.org/su20/ \u8bfe\u7a0b\u89c6\u9891\uff1a B \u7ad9 , Youtube \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 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\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 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\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/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 : 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\u548c MIT18.02: Multi variable 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 \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","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/6.042J/#_2","text":"\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":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS126/","text":"UCB CS126 : Probability theory \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS70\u3001\u5fae\u79ef\u5206\u3001\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 \u4f2f\u514b\u5229\u7684\u6982\u7387\u8bba\u8fdb\u9636\u8bfe\u7a0b\uff0c\u6d89\u53ca\u5230\u7edf\u8ba1\u5b66\u3001\u968f\u673a\u8fc7\u7a0b\u7b49\u7406\u8bba\u76f8\u5bf9\u6df1\u5165\u7684\u5185\u5bb9\uff0c\u9700\u8981\u76f8\u5f53\u7684\u6570\u5b66\u57fa\u7840\uff0c\u6211\u5728\u4e0a\u8fd9\u95e8\u8bfe\u7684\u65f6\u5019\u4e5f\u611f\u5230\u6709\u4e9b\u5403\u529b\uff0c\u4e0d\u8fc7\u575a\u6301\u4e0b\u6765\u4e00\u5b9a\u4f1a\u8ba9\u4f60\u5bf9\u6982\u7387\u8bba\u7684\u638c\u63e1\u8fbe\u5230\u4e00\u4e2a\u65b0\u7684\u9ad8\u5ea6\u3002 \u540c\u65f6\u8fd9\u95e8\u8bfe\u975e\u5e38\u5f3a\u8c03\u7406\u8bba\u4e0e\u5b9e\u8df5\u7684\u7ed3\u5408\uff0c\u8bfe\u7a0b\u8bbe\u8ba1\u8005 Jean Walrand \u6559\u6388\u4e13\u95e8\u5199\u4e86\u4e00\u672c\u914d\u5957\u7684\u6559\u6750 Probability in Electrical Engineering and Computer Science \uff0c\u4e66\u4e2d\u6bcf\u4e2a\u7ae0\u8282\u90fd\u4f1a\u4ee5\u4e00\u4e2a\u5177\u4f53\u7684\u7b97\u6cd5\u5b9e\u8df5\u4f5c\u4e3a\u4f8b\u5b50\u6765\u5c55\u793a\u7406\u8bba\u5728\u5b9e\u9645\u5f53\u4e2d\u7684\u8fd0\u7528\uff0c\u4f8b\u5982 PageRank, Route Planing, Speech Recognition \u7b49\u7b49\uff0c\u5e76\u4e14\u5168\u4e66\u5f00\u6e90\uff0c\u53ef\u4ee5\u514d\u8d39\u4e0b\u8f7d PDF \u6216\u8005 Epub \u7248\u3002 \u8fd9\u8fd8\u4e0d\u7b97\u5b8c\uff0cJean Walrand \u8fd8\u4e3a\u6574\u672c\u4e66\u91cc\u7684\u4f8b\u5b50\u8bbe\u8ba1\u4e86\u914d\u5957\u7684 Python \u5b9e\u73b0\uff0c\u4ee5 Jupyter Notebook \u7684\u5f62\u5f0f\u5728\u7ebf\u53d1\u5e03\uff0c\u8bfb\u8005\u53ef\u4ee5\u5728\u7ebf\u4fee\u6539\u3001\u8c03\u8bd5\u548c\u8fd0\u884c\u3002 \u4e0e\u6b64\u540c\u65f6\uff0c\u8fd9\u95e8\u8bfe\u9664\u4e86\u7406\u8bba\u4f5c\u4e1a\u4e4b\u5916\uff0c\u8fd8\u6709 9 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a\uff0c\u4f1a\u8ba9\u4f60\u7528\u6982\u7387\u8bba\u7684\u77e5\u8bc6\u89e3\u51b3\u5b9e\u9645\u95ee\u9898\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://inst.eecs.berkeley.edu/~ee126/fa20/content.html \u8bfe\u7a0b\u6559\u6750\uff1a PDF , Epub , Jupyter Notebook \u8bfe\u7a0b\u4f5c\u4e1a\uff1a14 \u4e2a\u4e66\u9762\u4f5c\u4e1a + 9 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a\uff0c\u5177\u4f53\u8981\u6c42\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9\u3002 \u8d44\u6e90\u6c47\u603b \u6211\u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/EECS126 - GitHub \u4e2d\u3002","title":"UCB CS126: probability theory"},{"location":"%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS126/#ucb-cs126-probability-theory","text":"","title":"UCB CS126 : Probability theory"},{"location":"%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS126/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS70\u3001\u5fae\u79ef\u5206\u3001\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 \u4f2f\u514b\u5229\u7684\u6982\u7387\u8bba\u8fdb\u9636\u8bfe\u7a0b\uff0c\u6d89\u53ca\u5230\u7edf\u8ba1\u5b66\u3001\u968f\u673a\u8fc7\u7a0b\u7b49\u7406\u8bba\u76f8\u5bf9\u6df1\u5165\u7684\u5185\u5bb9\uff0c\u9700\u8981\u76f8\u5f53\u7684\u6570\u5b66\u57fa\u7840\uff0c\u6211\u5728\u4e0a\u8fd9\u95e8\u8bfe\u7684\u65f6\u5019\u4e5f\u611f\u5230\u6709\u4e9b\u5403\u529b\uff0c\u4e0d\u8fc7\u575a\u6301\u4e0b\u6765\u4e00\u5b9a\u4f1a\u8ba9\u4f60\u5bf9\u6982\u7387\u8bba\u7684\u638c\u63e1\u8fbe\u5230\u4e00\u4e2a\u65b0\u7684\u9ad8\u5ea6\u3002 \u540c\u65f6\u8fd9\u95e8\u8bfe\u975e\u5e38\u5f3a\u8c03\u7406\u8bba\u4e0e\u5b9e\u8df5\u7684\u7ed3\u5408\uff0c\u8bfe\u7a0b\u8bbe\u8ba1\u8005 Jean Walrand \u6559\u6388\u4e13\u95e8\u5199\u4e86\u4e00\u672c\u914d\u5957\u7684\u6559\u6750 Probability in Electrical Engineering and Computer Science 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\u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"%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 \u8bfe\u7a0b\u7b80\u4ecb \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... \u8bfe\u7a0b\u8d44\u6e90 \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 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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 \u6211\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/#standford-ee364a-convex-optimization","text":"","title":"Standford 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 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\u548c\u6545\u969c\u6062\u590d\u7684\u5173\u7cfb\u578b\u6570\u636e\u5e93\u3002 \u4ece\u5b9e\u7528\u89d2\u5ea6\u6765\u8bf4\uff0c\u8fd9\u95e8\u8bfe\u8fd8\u4f1a\u5728\u7f16\u7a0b\u4f5c\u4e1a\u4e2d\u953b\u70bc\u4f60\u7f16\u5199 SQL \u67e5\u8be2\u4ee5\u53ca NoSQL \u67e5\u8be2\u7684\u80fd\u529b\uff0c\u5bf9\u4e8e\u6784\u5efa\u4e00\u4e9b\u5168\u6808\u7684\u5de5\u7a0b\u9879\u76ee\u5f88\u6709\u5e2e\u52a9\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/CS186/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a https://cs186berkeley.net/sp21/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV13a411c7Qo \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a6 \u4e2a Project","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/CS186/#_3","text":"\u6211\u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS186 - GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6/Data100/","text":"UCB Data100: Principles and Techniques of Data Science \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS61A\uff0c\u7ebf\u6027\u4ee3\u6570 \u7f16\u7a0b\u8bed\u8a00\uff1aPython \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 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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 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u53c2\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/","text":"Coursera: Algorithms I & II \u8bfe\u7a0b\u7b80\u4ecb \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 \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 \u6211\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":"\u6211\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/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":"%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS106B_CS106X/#stanford-cs106bx-programming-abstractions-in-c","text":"","title":"Stanford CS106B/X: Programming Abstractions in C++"},{"location":"%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/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 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https://tbp.berkeley.edu/courses/cs/61B/ COS226 Spring 2008 Midterm Solution: https://www.cs.princeton.edu/courses/archive/spring15/cos226/exams/mid-s08-sol.pdf COS226 Fall 2008 Midterm Solution: https://www.cs.princeton.edu/courses/archive/spring15/cos226/exams/mid-f08-sol.pdf COS226 Fall 2009 Midterm Solution: https://www.cs.princeton.edu/courses/archive/spring15/cos226/exams/mid-f09-sol.pdf CS61B Fall 2009 Exam Solutions (Final, Midterm, Midterm2, Midterm3): https://inst.eecs.berkeley.edu//~cs61b/fa13/samples/ COS226 Fall 2010 Midterm Solution: https://www.cs.princeton.edu/courses/archive/spring15/cos226/exams/mid-f10.pdf COS226 Fall 2011 Midterm Solution: https://www.cs.princeton.edu/courses/archive/spring15/cos226/exams/mid-f11-sol.pdf COS226 Spring 2012 Midterm Solution: https://www.cs.princeton.edu/courses/archive/spring15/cos226/exams/mid-s12-sol.pdf COS226 Fall 2012 Midterm Solution: https://www.cs.princeton.edu/courses/archive/spring15/cos226/exams/mid-f12-sol.pdf COS226 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\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 \u6211\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":"\u6211\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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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 \u6211\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\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a7 \u4e2a\u5728\u7ebf HW + 3 \u4e2a Lab + 3 \u4e2a Project \u8d44\u6e90\u6c47\u603b \u6211\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\u65e0 \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":"\u6211\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/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 \u6211\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":"\u6211\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 \u5b9e\u73b0\u4e00\u4e2a\u8d1f\u8f7d\u5747\u8861\u5668 \u8d44\u6e90\u6c47\u603b \u6211\u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS110L - GitHub \u4e2d\u3002","title":"Stanford CS110L: Safety in Systems Programming"},{"location":"%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":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS110L/#_1","text":"\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 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\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: \u5173\u6ce8\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784\uff0c\u8ba9\u5b66\u751f\u7406\u89e3\u9ad8\u7ea7\u8bed\u8a00\uff08\u4f8b\u5982 C\uff09\u662f\u5982\u4f55\u4e00\u6b65\u6b65\u8f6c\u6362\u4e3a\u673a\u5668\u53ef\u4ee5\u7406\u89e3\u7684 01 \u4e32\u5e76\u5728 CPU \u6267\u884c\u7684\uff0c\u5b66\u751f\u5c06\u4f1a\u5b66\u4e60 RISC-V \u67b6\u6784\u5e76\u81ea\u5df1\u7528 Logism \u5b9e\u73b0\u4e00\u4e2a CPU\u3002 CS61B \u548c CS61C \u5728\u672c\u4e66\u4e2d\u5747\u6709\u6536\u5f55\u3002 \u56de\u5230 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\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 \u8bfe\u7a0b\u8d44\u6e90 \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/ \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 \u6211\u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPic/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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\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 \u6211\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","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. 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\u672c\u4e66\u76ee\u524d\u89c4\u5212\u4e86\u4ee5\u4e0b\u90e8\u5206(\u5982\u679c\u4f60\u6709\u5176\u4ed6\u597d\u7684\u5efa\u8bae\uff0c\u6216\u8005\u60f3\u52a0\u5165\u8d21\u732e\u8005\u7684\u884c\u5217\uff0c\u6b22\u8fce\u90ae\u4ef6 zhongyinmin@pku.edu.cn \u6216\u8005\u5728 issue \u91cc\u63d0\u95ee)\uff1a \u5fc5\u5b66\u5de5\u5177\uff1aIDE, \u7ffb\u5899, StackOverflow, Git, GitHub, Vim, LaTeX, GNU Make, \u5b9e\u7528\u5de5\u5177 ... \u56fd\u5916\u9ad8\u8d28\u91cf CS \u8bfe\u7a0b\u6c47\u603b \uff1a\u6211\u5c06\u628a\u6211\u4e0a\u8fc7\u7684\u6240\u6709\u9ad8\u8d28\u91cf\u7684\u56fd\u5916 CS \u8bfe\u7a0b\u5206\u95e8\u522b\u7c7b\u8fdb\u884c\u6c47\u603b\uff0c\u5e76\u7ed9\u51fa\u76f8\u5173\u7684\u81ea\u5b66\u5efa\u8bae\uff0c\u5927\u90e8\u5206\u8bfe\u7a0b\u90fd\u4f1a\u6709\u4e00\u4e2a\u72ec\u7acb\u7684\u4ed3\u5e93\u7ef4\u62a4\u76f8\u5173\u7684\u8d44\u6e90\u4ee5\u53ca\u6211\u7684\u4f5c\u4e1a\u5b9e\u73b0\u3002 \u73af\u5883\u914d\u7f6e\uff1aLinux \u57fa\u7840\uff0c bash \uff0c\u73af\u5883\u53d8\u91cf\uff0cAnaconda ... \u7ecf\u5178\u4e66\u7c4d\u63a8\u8350\uff1a\u770b\u8fc7 CSAPP \u8fd9\u672c\u4e66\u7684\u540c\u5b66\u4e00\u5b9a\u611f\u53f9\u597d\u4e66\u7684\u91cd\u8981\uff0c\u6211\u5c06\u5217\u4e3e\u63a8\u8350\u81ea\u5df1\u770b\u8fc7\u7684\u8ba1\u7b97\u673a\u9886\u57df\u7684\u5fc5\u770b\u597d\u4e66\u4e0e\u8d44\u6e90\u94fe\u63a5\u3002","title":"\u524d\u8a00"},{"location":"#cs61a","text":"\u5927\u4e00\u5165\u5b66\u65f6\u6211\u662f\u4e00\u4e2a\u5bf9\u8ba1\u7b97\u673a\u4e00\u65e0\u6240\u77e5\u7684\u5c0f\u767d\uff0c\u88c5\u4e86\u51e0\u5341\u4e2a G \u7684 Visual Studio \u5929\u5929\u548c OJ \u4f60\u6b7b\u6211\u6d3b\u3002\u51ed\u7740\u9ad8\u4e2d\u7684\u6570\u5b66\u5e95\u5b50\u6211\u6570\u5b66\u8bfe\u5b66\u5f97\u8fd8\u4e0d\u9519\uff0c\u4f46\u5728\u4e13\u4e1a\u8bfe\u4e0a\u5bf9\u7ade\u8d5b\u5927\u4f6c\u53ea\u6709\u4ef0\u671b\u3002\u63d0\u5230\u7f16\u7a0b\u6211\u53ea\u4f1a\u6253\u5f00\u90a3\u7b28\u91cd\u7684 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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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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\u7684Python\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\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 \u6700\u597d\u7684\u8ba1\u7b97\u673a\u57fa\u7840\u5165\u95e8\u8bfe\uff0c\u4f60\u7edd\u5bf9\u4f1a\u7231\u4e0a\u7684\u3002 UCB CS61A: Structure and Interpretation of Computer Programs C++ 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 Stanford CS106B/X: Programming Abstractions 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 \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 \u64cd\u4f5c\u7cfb\u7edf\u4f5c\u4e3a\u6240\u6709\u5e94\u7528\u8f6f\u4ef6\u548c\u5e95\u5c42\u786c\u4ef6\u4ea4\u4e92\u7684\u638c\u8235\u8005\uff0c\u4e86\u89e3\u5b83\u7684\u5185\u90e8\u539f\u7406\u548c\u8bbe\u8ba1\u539f\u5219\u5bf9\u4e8e\u4e00\u4e2a\u4e0d\u6ee1\u8db3\u4e8e\u8c03\u5305\u4fa0\u7684\u7a0b\u5e8f\u5458\u6765\u8bf4\u662f\u5f88\u6709\u5e2e\u52a9\u7684\u3002\u540c\u65f6\uff0c\u56fd\u5916\u64cd\u7edf\u8bfe\u7a0b\u7684\u8d28\u91cf\u4e5f\u662f\u8ba9\u4e0a\u4e86\u591a\u5e74\u7f51\u8bfe\u7684\u6211\u4e5f\u611f\u5230\u77a0\u76ee\u7ed3\u820c\u3002 MIT 6.S081: 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UCB CS70 : discrete Math and probability theory \u548c UCB CS126 : Probability theory \u662f UC Berkeley 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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\u7814\u7a76\u751f\u8bfe\u7a0b 18.335: Introduction to numerical method \u4f9b\u4f60\u53c2\u8003\u3002","title":"\u6570\u503c\u5206\u6790"},{"location":"CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_11","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 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Since there's no universally perfect tool, there's no universally perfect language.","title":"\u7f16\u7a0b\u5165\u95e8"},{"location":"CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#shell","text":"MIT-Missing-Semester","title":"Shell"},{"location":"CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#python","text":"Harvard CS50: This is CS50x \u6700\u597d\u7684\u8ba1\u7b97\u673a\u57fa\u7840\u5165\u95e8\u8bfe\uff0c\u4f60\u7edd\u5bf9\u4f1a\u7231\u4e0a\u7684\u3002 UCB CS61A: Structure and Interpretation of Computer Programs","title":"Python"},{"location":"CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#c","text":"Stanford CS106L: Standard C++ Programming","title":"C++"},{"location":"CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#rust","text":"Stanford CS110L: Safety in Systems Programming","title":"Rust"},{"location":"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":"CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_19","text":"","title":"\u7535\u5b50\u57fa\u7840"},{"location":"CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_20","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":"CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_21","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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II","title":"\u6570\u636e\u7ed3\u6784\u4e0e\u7b97\u6cd5"},{"location":"CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_24","text":"UCB CS170: Efficient Algorithms and Intractable Problems","title":"\u7b97\u6cd5\u8bbe\u8ba1\u4e0e\u5206\u6790"},{"location":"CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_25","text":"","title":"\u8f6f\u4ef6\u5de5\u7a0b"},{"location":"CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_26","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 \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/#_27","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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\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 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\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/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 : 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\u548c MIT18.02: Multi variable 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 \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","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/6.042J/#_2","text":"\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":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS126/","text":"UCB CS126 : Probability theory \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS70\u3001\u5fae\u79ef\u5206\u3001\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 \u4f2f\u514b\u5229\u7684\u6982\u7387\u8bba\u8fdb\u9636\u8bfe\u7a0b\uff0c\u6d89\u53ca\u5230\u7edf\u8ba1\u5b66\u3001\u968f\u673a\u8fc7\u7a0b\u7b49\u7406\u8bba\u76f8\u5bf9\u6df1\u5165\u7684\u5185\u5bb9\uff0c\u9700\u8981\u76f8\u5f53\u7684\u6570\u5b66\u57fa\u7840\uff0c\u6211\u5728\u4e0a\u8fd9\u95e8\u8bfe\u7684\u65f6\u5019\u4e5f\u611f\u5230\u6709\u4e9b\u5403\u529b\uff0c\u4e0d\u8fc7\u575a\u6301\u4e0b\u6765\u4e00\u5b9a\u4f1a\u8ba9\u4f60\u5bf9\u6982\u7387\u8bba\u7684\u638c\u63e1\u8fbe\u5230\u4e00\u4e2a\u65b0\u7684\u9ad8\u5ea6\u3002 \u540c\u65f6\u8fd9\u95e8\u8bfe\u975e\u5e38\u5f3a\u8c03\u7406\u8bba\u4e0e\u5b9e\u8df5\u7684\u7ed3\u5408\uff0c\u8bfe\u7a0b\u8bbe\u8ba1\u8005 Jean Walrand \u6559\u6388\u4e13\u95e8\u5199\u4e86\u4e00\u672c\u914d\u5957\u7684\u6559\u6750 Probability in Electrical Engineering and Computer Science \uff0c\u4e66\u4e2d\u6bcf\u4e2a\u7ae0\u8282\u90fd\u4f1a\u4ee5\u4e00\u4e2a\u5177\u4f53\u7684\u7b97\u6cd5\u5b9e\u8df5\u4f5c\u4e3a\u4f8b\u5b50\u6765\u5c55\u793a\u7406\u8bba\u5728\u5b9e\u9645\u5f53\u4e2d\u7684\u8fd0\u7528\uff0c\u4f8b\u5982 PageRank, Route Planing, Speech Recognition \u7b49\u7b49\uff0c\u5e76\u4e14\u5168\u4e66\u5f00\u6e90\uff0c\u53ef\u4ee5\u514d\u8d39\u4e0b\u8f7d PDF \u6216\u8005 Epub \u7248\u3002 \u8fd9\u8fd8\u4e0d\u7b97\u5b8c\uff0cJean Walrand \u8fd8\u4e3a\u6574\u672c\u4e66\u91cc\u7684\u4f8b\u5b50\u8bbe\u8ba1\u4e86\u914d\u5957\u7684 Python \u5b9e\u73b0\uff0c\u4ee5 Jupyter Notebook \u7684\u5f62\u5f0f\u5728\u7ebf\u53d1\u5e03\uff0c\u8bfb\u8005\u53ef\u4ee5\u5728\u7ebf\u4fee\u6539\u3001\u8c03\u8bd5\u548c\u8fd0\u884c\u3002 \u4e0e\u6b64\u540c\u65f6\uff0c\u8fd9\u95e8\u8bfe\u9664\u4e86\u7406\u8bba\u4f5c\u4e1a\u4e4b\u5916\uff0c\u8fd8\u6709 9 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a\uff0c\u4f1a\u8ba9\u4f60\u7528\u6982\u7387\u8bba\u7684\u77e5\u8bc6\u89e3\u51b3\u5b9e\u9645\u95ee\u9898\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://inst.eecs.berkeley.edu/~ee126/fa20/content.html \u8bfe\u7a0b\u6559\u6750\uff1a PDF , Epub , Jupyter Notebook \u8bfe\u7a0b\u4f5c\u4e1a\uff1a14 \u4e2a\u4e66\u9762\u4f5c\u4e1a + 9 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a\uff0c\u5177\u4f53\u8981\u6c42\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9\u3002 \u8d44\u6e90\u6c47\u603b \u6211\u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/EECS126 - GitHub \u4e2d\u3002","title":"UCB CS126: probability theory"},{"location":"%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS126/#ucb-cs126-probability-theory","text":"","title":"UCB CS126 : Probability theory"},{"location":"%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS126/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS70\u3001\u5fae\u79ef\u5206\u3001\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 \u4f2f\u514b\u5229\u7684\u6982\u7387\u8bba\u8fdb\u9636\u8bfe\u7a0b\uff0c\u6d89\u53ca\u5230\u7edf\u8ba1\u5b66\u3001\u968f\u673a\u8fc7\u7a0b\u7b49\u7406\u8bba\u76f8\u5bf9\u6df1\u5165\u7684\u5185\u5bb9\uff0c\u9700\u8981\u76f8\u5f53\u7684\u6570\u5b66\u57fa\u7840\uff0c\u6211\u5728\u4e0a\u8fd9\u95e8\u8bfe\u7684\u65f6\u5019\u4e5f\u611f\u5230\u6709\u4e9b\u5403\u529b\uff0c\u4e0d\u8fc7\u575a\u6301\u4e0b\u6765\u4e00\u5b9a\u4f1a\u8ba9\u4f60\u5bf9\u6982\u7387\u8bba\u7684\u638c\u63e1\u8fbe\u5230\u4e00\u4e2a\u65b0\u7684\u9ad8\u5ea6\u3002 \u540c\u65f6\u8fd9\u95e8\u8bfe\u975e\u5e38\u5f3a\u8c03\u7406\u8bba\u4e0e\u5b9e\u8df5\u7684\u7ed3\u5408\uff0c\u8bfe\u7a0b\u8bbe\u8ba1\u8005 Jean Walrand \u6559\u6388\u4e13\u95e8\u5199\u4e86\u4e00\u672c\u914d\u5957\u7684\u6559\u6750 Probability in Electrical Engineering and Computer Science 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\u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"%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 \u8bfe\u7a0b\u7b80\u4ecb \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... \u8bfe\u7a0b\u8d44\u6e90 \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 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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 \u6211\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/#standford-ee364a-convex-optimization","text":"","title":"Standford 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 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\u548c\u6545\u969c\u6062\u590d\u7684\u5173\u7cfb\u578b\u6570\u636e\u5e93\u3002 \u4ece\u5b9e\u7528\u89d2\u5ea6\u6765\u8bf4\uff0c\u8fd9\u95e8\u8bfe\u8fd8\u4f1a\u5728\u7f16\u7a0b\u4f5c\u4e1a\u4e2d\u953b\u70bc\u4f60\u7f16\u5199 SQL \u67e5\u8be2\u4ee5\u53ca NoSQL \u67e5\u8be2\u7684\u80fd\u529b\uff0c\u5bf9\u4e8e\u6784\u5efa\u4e00\u4e9b\u5168\u6808\u7684\u5de5\u7a0b\u9879\u76ee\u5f88\u6709\u5e2e\u52a9\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/CS186/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a https://cs186berkeley.net/sp21/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV13a411c7Qo \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a6 \u4e2a Project","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/CS186/#_3","text":"\u6211\u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS186 - GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6/Data100/","text":"UCB Data100: Principles and Techniques of Data Science \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS61A\uff0c\u7ebf\u6027\u4ee3\u6570 \u7f16\u7a0b\u8bed\u8a00\uff1aPython \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 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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 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u53c2\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/","text":"Coursera: Algorithms I & II \u8bfe\u7a0b\u7b80\u4ecb \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 \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 \u6211\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":"\u6211\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/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":"%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS106B_CS106X/#stanford-cs106bx-programming-abstractions-in-c","text":"","title":"Stanford CS106B/X: Programming Abstractions in C++"},{"location":"%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/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 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https://tbp.berkeley.edu/courses/cs/61B/ COS226 Spring 2008 Midterm Solution: https://www.cs.princeton.edu/courses/archive/spring15/cos226/exams/mid-s08-sol.pdf COS226 Fall 2008 Midterm Solution: https://www.cs.princeton.edu/courses/archive/spring15/cos226/exams/mid-f08-sol.pdf COS226 Fall 2009 Midterm Solution: https://www.cs.princeton.edu/courses/archive/spring15/cos226/exams/mid-f09-sol.pdf CS61B Fall 2009 Exam Solutions (Final, Midterm, Midterm2, Midterm3): https://inst.eecs.berkeley.edu//~cs61b/fa13/samples/ COS226 Fall 2010 Midterm Solution: https://www.cs.princeton.edu/courses/archive/spring15/cos226/exams/mid-f10.pdf COS226 Fall 2011 Midterm Solution: https://www.cs.princeton.edu/courses/archive/spring15/cos226/exams/mid-f11-sol.pdf COS226 Spring 2012 Midterm Solution: https://www.cs.princeton.edu/courses/archive/spring15/cos226/exams/mid-s12-sol.pdf COS226 Fall 2012 Midterm Solution: https://www.cs.princeton.edu/courses/archive/spring15/cos226/exams/mid-f12-sol.pdf COS226 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\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 \u6211\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":"\u6211\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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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 \u6211\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\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a7 \u4e2a\u5728\u7ebf HW + 3 \u4e2a Lab + 3 \u4e2a Project \u8d44\u6e90\u6c47\u603b \u6211\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\u65e0 \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":"\u6211\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/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 \u6211\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":"\u6211\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 \u5b9e\u73b0\u4e00\u4e2a\u8d1f\u8f7d\u5747\u8861\u5668 \u8d44\u6e90\u6c47\u603b \u6211\u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS110L - GitHub \u4e2d\u3002","title":"Stanford CS110L: Safety in Systems Programming"},{"location":"%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":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS110L/#_1","text":"\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 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\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: \u5173\u6ce8\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784\uff0c\u8ba9\u5b66\u751f\u7406\u89e3\u9ad8\u7ea7\u8bed\u8a00\uff08\u4f8b\u5982 C\uff09\u662f\u5982\u4f55\u4e00\u6b65\u6b65\u8f6c\u6362\u4e3a\u673a\u5668\u53ef\u4ee5\u7406\u89e3\u7684 01 \u4e32\u5e76\u5728 CPU \u6267\u884c\u7684\uff0c\u5b66\u751f\u5c06\u4f1a\u5b66\u4e60 RISC-V \u67b6\u6784\u5e76\u81ea\u5df1\u7528 Logism \u5b9e\u73b0\u4e00\u4e2a CPU\u3002 CS61B \u548c CS61C \u5728\u672c\u4e66\u4e2d\u5747\u6709\u6536\u5f55\u3002 \u56de\u5230 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\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 \u8bfe\u7a0b\u8d44\u6e90 \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/ \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 \u6211\u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPic/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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\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 \u6211\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","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":"\u6211\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","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 Havard \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 \u6211\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 Havard \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":"\u6211\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"}]}
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2022-05-11
daily
+
+ https://csdiy.wiki/%E4%BD%93%E7%B3%BB%E7%BB%93%E6%9E%84/DDCA/
+ 2022-05-11
+ daily
+
https://csdiy.wiki/%E4%BD%93%E7%B3%BB%E7%BB%93%E6%9E%84/N2T/
2022-05-11
diff --git a/sitemap.xml.gz b/sitemap.xml.gz
index 5848ff0e9cd4f01d92e70c285f85d0a715ee2e39..fe324f4fa69136c5d76bc4434b299dd1bbacf06d 100644
GIT binary patch
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index 8f94ceb0..58484891 100644
--- a/人工智能/CS188/index.html
+++ b/人工智能/CS188/index.html
@@ -1,2 +1,2 @@
- UCB CS188: Introduction to Artificial Intelligence - CS自学指南
CS188: Introduction to Artificial Intelligence 课程简介 所属大学：UC Berkeley 先修要求：CS70 编程语言：Python 课程难度：🌟🌟🌟 预计学时：50 小时 伯克利的人工智能入门课，课程 notes 写得非常深入浅出，基本不需要观看课程视频。课程内容的安排基本按照人工智能的经典教材 Artificial intelligence: A Modern Approach 的章节顺序，覆盖了搜索剪枝、约束满足问题、马尔可夫决策过程、强化学习、贝叶斯网络、隐马尔可夫模型以及基础的机器学习和神经网络的相关内容。
2018年秋季学期的版本免费开放了 gradescope，大家可以在线完成书面作业并实时得到测评结果。同时课程的 6 个 Project 也是质量爆炸，复现了经典的 Packman（吃豆人）小游戏，会让你利用学到的 AI 知识，去实现相关算法，让你的吃豆人在迷宫里自由穿梭，躲避鬼怪，收集豆子。
课程资源 最后更新: April 3, 2022
CS188: Introduction to Artificial Intelligence 课程简介 所属大学：UC Berkeley 先修要求：CS70 编程语言：Python 课程难度：🌟🌟🌟 预计学时：50 小时 伯克利的人工智能入门课，课程 notes 写得非常深入浅出，基本不需要观看课程视频。课程内容的安排基本按照人工智能的经典教材 Artificial intelligence: A Modern Approach 的章节顺序，覆盖了搜索剪枝、约束满足问题、马尔可夫决策过程、强化学习、贝叶斯网络、隐马尔可夫模型以及基础的机器学习和神经网络的相关内容。
2018年秋季学期的版本免费开放了 gradescope，大家可以在线完成书面作业并实时得到测评结果。同时课程的 6 个 Project 也是质量爆炸，复现了经典的 Packman（吃豆人）小游戏，会让你利用学到的 AI 知识，去实现相关算法，让你的吃豆人在迷宫里自由穿梭，躲避鬼怪，收集豆子。
课程资源 最后更新: April 3, 2022
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diff --git a/人工智能/CS50/index.html b/人工智能/CS50/index.html
index b23761a5..cb9ae8ff 100644
--- a/人工智能/CS50/index.html
+++ b/人工智能/CS50/index.html
@@ -1,2 +1,2 @@
- Harvard CS50's Introduction to AI with Python - CS自学指南
CS50’s Introduction to AI with Python 课程简介 所属大学：Harvard 先修要求：基本概率论 + Python基础 编程语言：Python 课程难度：🌟🌟🌟 预计学时：30 小时 一门非常基础的AI入门课，让人眼前一亮的是12个设计精巧的编程作业，都会用学到的AI知识去实现一个简易的游戏AI，比如用强化学习训练一个Nim游戏的AI，用alpha-beta剪枝去扫雷等等，非常适合新手入门或者大佬休闲。
课程资源 资源汇总 我在学习这门课中用到的所有资源和作业实现都汇总在 PKUFlyingPig/cs50_ai - GitHub 中。
最后更新: April 3, 2022
CS50’s Introduction to AI with Python 课程简介 所属大学：Harvard 先修要求：基本概率论 + Python基础 编程语言：Python 课程难度：🌟🌟🌟 预计学时：30 小时 一门非常基础的AI入门课，让人眼前一亮的是12个设计精巧的编程作业，都会用学到的AI知识去实现一个简易的游戏AI，比如用强化学习训练一个Nim游戏的AI，用alpha-beta剪枝去扫雷等等，非常适合新手入门或者大佬休闲。
课程资源 资源汇总 我在学习这门课中用到的所有资源和作业实现都汇总在 PKUFlyingPig/cs50_ai - GitHub 中。
最后更新: April 3, 2022
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diff --git a/人工智能/CYJ/index.html b/人工智能/CYJ/index.html
index f2cad2a9..be41d73f 100644
--- a/人工智能/CYJ/index.html
+++ b/人工智能/CYJ/index.html
@@ -1,2 +1,2 @@
- 智能计算系统 - CS自学指南
智能计算系统 课程简介 所属大学：中国科学院大学 先修要求：体系结构，深度学习 编程语言：Python, C++, BCL 课程难度：🌟🌟🌟 预计学时：100 小时+ 智能计算系统是智能的核心物质载体，每年全球要制造数以十亿计的智能计算系统（包括智能手机、智能服务器、智能可穿戴设备等），需要大量的智能计算系统的设计者和开发者。智能计算系统人才的培养直接关系到我国智能产业的核心竞争力。因此，对智能计算系统的认识和理解是智能时代计算机类专业学生培养方案中不可或缺的重要组成部分，是计算机类专业学生的核心竞争力。
国内的陈云霁老师开的课，在其他若干个大学也都有开对应的课程（比如我们这里）。这门课用一个个实验带大家以一个完整的视野理解人工智能的技术栈。从上层的深度学习框架，到用底层语言编写算子，再到硬件中MLU的设计，让大家形成系统思维，体会自上而下，融会贯通的乐趣。
课程资源 资源汇总 我在学习这门课中用到的所有资源和作业实现都汇总在 ysj1173886760/Learning: ai-system - GitHub 中。
我做了其中的 2,3,4,5 这几个实验，其中综合实验和硬件实验没有做，如果有做了的同学欢迎大家补上你的链接
个人体会是第三章实现算子的实验让我对深度学习框架的了解加深了很多。第五章的实验BCL语言编写算子如果了解 CUDA 的话会感觉很熟悉。
推荐去买一本教材看一看，会让我们理解整体的技术栈。熟悉深度学习的同学可以直接从第五章开始看，看看深度学习框架底层到底是什么样的。
我因为这门课的启发，参考一本书（书名在仓库中）写了一个简易的深度学习框架 。在这个框架里可以看到智能计算系统实验中的一些影子。同时受到 build-your-own-x 系列的启发，我也打算写一下教程，教大家写一个自己的深度学习框架。代码用 Python 写的，代码量较少，适合有一定基础的同学阅读。之后打算添加更多的算子，有望实现一个较为全面的框架，并希望移植到 C++ 中，以兼顾性能与开发效率。
最后更新: April 3, 2022
智能计算系统 课程简介 所属大学：中国科学院大学 先修要求：体系结构，深度学习 编程语言：Python, C++, BCL 课程难度：🌟🌟🌟 预计学时：100 小时+ 智能计算系统是智能的核心物质载体，每年全球要制造数以十亿计的智能计算系统（包括智能手机、智能服务器、智能可穿戴设备等），需要大量的智能计算系统的设计者和开发者。智能计算系统人才的培养直接关系到我国智能产业的核心竞争力。因此，对智能计算系统的认识和理解是智能时代计算机类专业学生培养方案中不可或缺的重要组成部分，是计算机类专业学生的核心竞争力。
国内的陈云霁老师开的课，在其他若干个大学也都有开对应的课程（比如我们这里）。这门课用一个个实验带大家以一个完整的视野理解人工智能的技术栈。从上层的深度学习框架，到用底层语言编写算子，再到硬件中MLU的设计，让大家形成系统思维，体会自上而下，融会贯通的乐趣。
课程资源 资源汇总 我在学习这门课中用到的所有资源和作业实现都汇总在 ysj1173886760/Learning: ai-system - GitHub 中。
我做了其中的 2,3,4,5 这几个实验，其中综合实验和硬件实验没有做，如果有做了的同学欢迎大家补上你的链接
个人体会是第三章实现算子的实验让我对深度学习框架的了解加深了很多。第五章的实验BCL语言编写算子如果了解 CUDA 的话会感觉很熟悉。
推荐去买一本教材看一看，会让我们理解整体的技术栈。熟悉深度学习的同学可以直接从第五章开始看，看看深度学习框架底层到底是什么样的。
我因为这门课的启发，参考一本书（书名在仓库中）写了一个简易的深度学习框架 。在这个框架里可以看到智能计算系统实验中的一些影子。同时受到 build-your-own-x 系列的启发，我也打算写一下教程，教大家写一个自己的深度学习框架。代码用 Python 写的，代码量较少，适合有一定基础的同学阅读。之后打算添加更多的算子，有望实现一个较为全面的框架，并希望移植到 C++ 中，以兼顾性能与开发效率。
最后更新: April 3, 2022
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diff --git a/体系结构/CS61C/index.html b/体系结构/CS61C/index.html
index 35cd60c5..d5410e51 100644
--- a/体系结构/CS61C/index.html
+++ b/体系结构/CS61C/index.html
@@ -1,2 +1,2 @@
- UCB CS61C: Great Ideas in Computer Architecture - CS自学指南
CS61C: Great Ideas in Computer Architecture 课程简介 所属大学：UC Berkeley 先修要求：CS61A, CS61B 编程语言：C 课程难度：🌟🌟🌟🌟 预计学时：100 小时 伯克利 CS61 系列的最后一门课程，深入计算机的硬件细节，带领学生逐步理解 C 语言是如何一步步转化为 RISC-V 汇编并在 CPU 上执行的。和 Nand2Tetris 不同，这门课 在难度和深度上都会提高很多，具体会涉及到流水线、Cache、虚存以及并发相关的内容。
这门课的 Project 也非常新颖有趣。Project1 会让你用 C 语言写一个小程序，20 年秋季学期是著名的游戏 Game of Life 。Project2 会让你用 RISC-V 汇编编写一个神经网络，用来 识别 MNIST 手写数字，非常锻炼你对汇编代码的理解和运用。Project3 中你会用 Logisim 这个数字电路模拟软件搭建出一个二级流水线的 CPU，并在上面运行 RISC-V 汇编代码。Project4 会让你使用 OpenMP, SIMD 等方法并行优化矩阵运算，实现一个简易的 Numpy。
总而言之，这是个人上过的最好的计算机体系结构的课程。
课程资源 资源汇总 我在学习这门课中用到的所有资源和作业实现都汇总在 PKUFlyingPig/CS61C-summer20 - GitHub 中。
最后更新: May 11, 2022
\ No newline at end of file
+ UCB CS61C: Great Ideas in Computer Architecture - CS自学指南
CS61C: Great Ideas in Computer Architecture 课程简介 所属大学：UC Berkeley 先修要求：CS61A, CS61B 编程语言：C 课程难度：🌟🌟🌟🌟 预计学时：100 小时 伯克利 CS61 系列的最后一门课程，深入计算机的硬件细节，带领学生逐步理解 C 语言是如何一步步转化为 RISC-V 汇编并在 CPU 上执行的。和 Nand2Tetris 不同，这门课 在难度和深度上都会提高很多，具体会涉及到流水线、Cache、虚存以及并发相关的内容。
这门课的 Project 也非常新颖有趣。Project1 会让你用 C 语言写一个小程序，20 年秋季学期是著名的游戏 Game of Life 。Project2 会让你用 RISC-V 汇编编写一个神经网络，用来 识别 MNIST 手写数字，非常锻炼你对汇编代码的理解和运用。Project3 中你会用 Logisim 这个数字电路模拟软件搭建出一个二级流水线的 CPU，并在上面运行 RISC-V 汇编代码。Project4 会让你使用 OpenMP, SIMD 等方法并行优化矩阵运算，实现一个简易的 Numpy。
总而言之，这是个人上过的最好的计算机体系结构的课程。
课程资源 资源汇总 我在学习这门课中用到的所有资源和作业实现都汇总在 PKUFlyingPig/CS61C-summer20 - GitHub 中。
最后更新: May 11, 2022
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diff --git a/体系结构/CSAPP/index.html b/体系结构/CSAPP/index.html
index 752ffb85..19dcd266 100644
--- a/体系结构/CSAPP/index.html
+++ b/体系结构/CSAPP/index.html
@@ -1,2 +1,2 @@
- CMU 15-213: CSAPP - CS自学指南
CMU CS15213: CSAPP 课程简介 所属大学：CMU 先修要求：CS61A, CS61B 编程语言：C 课程难度：🌟🌟🌟🌟🌟 预计学时：150 小时 CMU 大名鼎鼎的镇系神课，以其内容庞杂，Project 巨难而闻名遐迩。课程内容覆盖了汇编语言、体系结构、操作系统、编译链接、并行、网络等，作为系统入门课，兼具深度和广度，如果自学确实需要相当的毅力和代码功底。
这门课配合的教材由 CMU 计算机系主任 Bryant 教授执笔，也即所谓的 CSAPP。这也是我第一本认认真真一页一页读过去的计算机教材，虽然很难啃，但着实收获良多。
北大购买了这门课的版权并开设了 Introduction to Computer System 这门课，但其实 CSAPP 所有的课程资源和实验代码都能在它的官方主页上访问到（具体参见下方链接）。
这门课由于过于出名，全世界的码农争相学习，导致其 Project 的答案在网上几乎唾手可得。但如果你真的想锻炼自己的代码能力，希望你不要借鉴任何第三方代码。
认真学完这一门课，你对计算机系统的理解绝对会上升一个台阶。
课程资源 补充： 英语有困难的同学可以参考B站UP主九曲阑干 对 CSAPP 的中文讲解 （据说CMU的中国留学生也在CMU的课堂上看这个视频呢）。另外如果大家在看完 CSAPP 后对书中的第七章链接有一定的疑问，推荐阅读《程序员的自我修养》这本书，书的副标题是链接，装载与库。这本书能够帮助我们完善对程序链接的理解，相信你在看完这本书以后可以对程序的链接，ELF 文件，动态库都将有一个更加深入的理解。十分推荐在读完 CSAPP，对计算机系统有一定的了解以后作为补充资料来阅读。
最后更新: May 11, 2022
CMU CS15213: CSAPP 课程简介 所属大学：CMU 先修要求：CS61A, CS61B 编程语言：C 课程难度：🌟🌟🌟🌟🌟 预计学时：150 小时 CMU 大名鼎鼎的镇系神课，以其内容庞杂，Project 巨难而闻名遐迩。课程内容覆盖了汇编语言、体系结构、操作系统、编译链接、并行、网络等，作为系统入门课，兼具深度和广度，如果自学确实需要相当的毅力和代码功底。
这门课配合的教材由 CMU 计算机系主任 Bryant 教授执笔，也即所谓的 CSAPP。这也是我第一本认认真真一页一页读过去的计算机教材，虽然很难啃，但着实收获良多。
北大购买了这门课的版权并开设了 Introduction to Computer System 这门课，但其实 CSAPP 所有的课程资源和实验代码都能在它的官方主页上访问到（具体参见下方链接）。
这门课由于过于出名，全世界的码农争相学习，导致其 Project 的答案在网上几乎唾手可得。但如果你真的想锻炼自己的代码能力，希望你不要借鉴任何第三方代码。
认真学完这一门课，你对计算机系统的理解绝对会上升一个台阶。
课程资源 补充： 英语有困难的同学可以参考B站UP主九曲阑干 对 CSAPP 的中文讲解 （据说CMU的中国留学生也在CMU的课堂上看这个视频呢）。另外如果大家在看完 CSAPP 后对书中的第七章链接有一定的疑问，推荐阅读《程序员的自我修养》这本书，书的副标题是链接，装载与库。这本书能够帮助我们完善对程序链接的理解，相信你在看完这本书以后可以对程序的链接，ELF 文件，动态库都将有一个更加深入的理解。十分推荐在读完 CSAPP，对计算机系统有一定的了解以后作为补充资料来阅读。
最后更新: May 11, 2022
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diff --git a/体系结构/DDCA.MD b/体系结构/DDCA.MD
deleted file mode 100644
index ef4badb6..00000000
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+++ /dev/null
@@ -1,22 +0,0 @@
-# Digital Design and Computer Architecture
-
-## 课程简介
-
-- 所属大学：ETH Zurich
-- 先修要求：cs50 或同阶课程，最好有 C 语言基础。
-- 编程语言：C，Verilog，MIPS 汇编，LC3 汇编
-- 课程难度：🌟🌟🌟
-- 预计学时：100 小时
-
-体系结构领域的大牛 Onur Mutlu 来教你数字电路和计算机体系结构。课程完全从计算机设计的角度出发，从晶体管、逻辑门开始，一直讲解到微架构、缓存和虚拟内存，还会介绍
-很多体系结构领域最新的研究进展。课程共有 9 个 lab，使用 Basys 3 FPGA 开发板（可自行购买）和 Xilinx 公司的 [Vivado 软件](https://china.xilinx.com/products/design-tools/vivado.html)（可在官网免费下载使用）进行电路设计，从组合电路
-和时序电路开始，一直到最后部署一个完整的 CPU。课程资料除了 lab 答案和当期考试答案之外全部开源，学完之后你可以掌握计算机相关的数字电路，Verilog 硬件描述语言，MIPS 与 C
-之间的转换关系，MIPS 单周期多周期流水线 CPU 的设计和性能分析，缓存，虚拟内存等重要概念。
-
-## 课程资源
-
-- 课程网站：
-- 课程教材1：Patt and Patel, Introduction to Computing Systems
-- 课程教材2：Harris and Harris, Digital Design and Computer Architecture (MIPS Edition)
-- 课程实验：9 个实验从零开始设计 MIPS CPU，详见课程网站
diff --git a/体系结构/DDCA/index.html b/体系结构/DDCA/index.html
new file mode 100644
index 00000000..5bc53400
--- /dev/null
+++ b/体系结构/DDCA/index.html
@@ -0,0 +1,2 @@
+ Digital Design and Computer Architecture - CS自学指南
Digital Design and Computer Architecture 课程简介 所属大学：ETH Zurich 先修要求：cs50 或同阶课程，最好有 C 语言基础。 编程语言：C，Verilog，MIPS 汇编，LC3 汇编 课程难度：🌟🌟🌟 预计学时：100 小时 体系结构领域的大牛 Onur Mutlu 来教你数字电路和计算机体系结构。课程完全从计算机设计的角度出发，从晶体管、逻辑门开始，一直讲解到微架构、缓存和虚拟内存，还会介绍 很多体系结构领域最新的研究进展。课程共有 9 个 lab，使用 Basys 3 FPGA 开发板（可自行购买）和 Xilinx 公司的 Vivado 软件 （可在官网免费下载使用）进行电路设计，从组合电路 和时序电路开始，一直到最后部署一个完整的 CPU。课程资料除了 lab 答案和当期考试答案之外全部开源，学完之后你可以掌握计算机相关的数字电路，Verilog 硬件描述语言，MIPS 与 C 之间的转换关系，MIPS 单周期多周期流水线 CPU 的设计和性能分析，缓存，虚拟内存等重要概念。
课程资源 最后更新: May 11, 2022
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diff --git a/体系结构/N2T/index.html b/体系结构/N2T/index.html
index 5d7eecd2..02914c15 100644
--- a/体系结构/N2T/index.html
+++ b/体系结构/N2T/index.html
@@ -1,2 +1,2 @@
- Coursera: Nand2Tetris - CS自学指南
Coursera: Nand2Tetris 课程简介 所属大学：希伯来大学 先修要求：无 编程语言：任选一个编程语言 课程难度：🌟🌟🌟 预计学时：40 小时 Coursera 上被数万人评为满分，在全球四百多所高校、高中被采用，让一个完全没有计算机基础的人从与非门开始造一台计算机，并在上面运行俄罗斯方块小游戏。
听起来就很酷对不对？实现起来更酷！这门课分为硬件和软件两个部分。在硬件部分，你将进入 01 的世界，用与非门构造出逻辑电路，并逐步搭建出一个 CPU 来运行一套课程作者定义的简易汇编代码。在软件部分，你将编写一个编译器，将作者开发的一个名为Jack的高级语言编译为可以运行在虚拟机上的字节码，然后进一步翻译为汇编代码。你还将开发一个简易的 OS，让你的计算机支持输入输出图形界面。至此，你可以用 Jack 开发一个俄罗斯方块的小游戏，将它编译为汇编代码，运行在你用与非门搭建出的 CPU 上，通过你开发的 OS 进行交互。学完这门课程，你将对整个计算机的体系结构有一个全局且深刻的理解，对于你后续课程的学习有着莫大的帮助。
你也许会担心课程会不会很难，但这门课面向的人群是完全没有计算机基础的人，课程作者的目标是让高中生都能理解。因此，只要你按部就班跟着课程规划走，一个月内学完应该绰绰有余。麻雀虽小但是五脏俱全，这门课很好地提取出了计算机的本质，而不过多地陷于现代计算机为了性能而设计出的众多复杂细节。让学习者能在轻松愉快的学习体验中感受计算机的优雅与神奇。
课程资源 资源汇总 我在学习这门课中用到的所有资源和作业实现都汇总在 PKUFlyingPig/NandToTetris - GitHub 中。
最后更新: April 3, 2022
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+ Coursera: Nand2Tetris - CS自学指南
Coursera: Nand2Tetris 课程简介 所属大学：希伯来大学 先修要求：无 编程语言：任选一个编程语言 课程难度：🌟🌟🌟 预计学时：40 小时 Coursera 上被数万人评为满分，在全球四百多所高校、高中被采用，让一个完全没有计算机基础的人从与非门开始造一台计算机，并在上面运行俄罗斯方块小游戏。
听起来就很酷对不对？实现起来更酷！这门课分为硬件和软件两个部分。在硬件部分，你将进入 01 的世界，用与非门构造出逻辑电路，并逐步搭建出一个 CPU 来运行一套课程作者定义的简易汇编代码。在软件部分，你将编写一个编译器，将作者开发的一个名为Jack的高级语言编译为可以运行在虚拟机上的字节码，然后进一步翻译为汇编代码。你还将开发一个简易的 OS，让你的计算机支持输入输出图形界面。至此，你可以用 Jack 开发一个俄罗斯方块的小游戏，将它编译为汇编代码，运行在你用与非门搭建出的 CPU 上，通过你开发的 OS 进行交互。学完这门课程，你将对整个计算机的体系结构有一个全局且深刻的理解，对于你后续课程的学习有着莫大的帮助。
你也许会担心课程会不会很难，但这门课面向的人群是完全没有计算机基础的人，课程作者的目标是让高中生都能理解。因此，只要你按部就班跟着课程规划走，一个月内学完应该绰绰有余。麻雀虽小但是五脏俱全，这门课很好地提取出了计算机的本质，而不过多地陷于现代计算机为了性能而设计出的众多复杂细节。让学习者能在轻松愉快的学习体验中感受计算机的优雅与神奇。
课程资源 资源汇总 我在学习这门课中用到的所有资源和作业实现都汇总在 PKUFlyingPig/NandToTetris - GitHub 中。
最后更新: April 3, 2022
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diff --git a/后记/index.html b/后记/index.html
index 875442fb..dc78827f 100644
--- a/后记/index.html
+++ b/后记/index.html
@@ -1,2 +1,2 @@
- 后记 - CS自学指南
后记 从最初的想法开始，到断断续续完成这本书，再到树洞的热烈反响，我很激动，但也五味杂陈。原来在北大这个园子里，也有那么多人，对自己的本科生涯并不满意。而这里，可是囊括了中国非常优秀的一帮年轻人。所以问题出在哪里？我不知道。
我只是个籍籍无名的本科生呀，只是一个单纯的求学者，我的目标只是想快乐地、自由地、高质量地掌握那些专业知识，我想，正在看这本书的大多数本科生也是如此，谁想付出时间但却收效甚微呢？又是谁迫使大家带着痛苦去应付呢？我不知道。
我写这本书绝不是为了鼓励大家翘课自学，试问谁不想在课堂上和那么多优秀的同学济济一堂，热烈讨论呢？谁不想遇到问题直接找老师答疑解惑呢？谁不想辛苦学习的成果可以直接化作学校承认的学分绩点呢？可如果一个兢兢业业、按时到堂的学生收获的却是痛苦，而那个一学期只有考试会出席的学生却学得自得其乐，这公平吗？我不知道。
我只是不甘，不甘心这些通过高考战胜无数人进入高校的学子本可以收获一个更快乐的本科生涯，但现实却留给了他们遗憾。我反问自己，本科教育究竟应该带给我们什么呢？是学完所有这些课程吗？倒也未必，它也许只适合我这种nerd。但我觉得，本科教育至少得展现它应有的诚意，一种分享知识的诚意，一种以人为本的诚意，一种注重学生体验的诚意。它至少不应该是一种恶意，一种拼比知识的恶意，一种胜者为王的恶意，一种让人学无所得的恶意。但这一切能改变吗？我不知道。
我只知道我做了应该做的事情，学生们会用脚投票，树洞的关注量和回帖数证明了这样一份资料是有价值的，也道出了国内CS本科教育和国外的差距。也许这样的改变是微乎其微的，但别忘了我只是一个籍籍无名的本科生，是北大信科一千多名本科生中的普通一员，是中国几百万在读本科生中的一分子，如果有更多的人站出来，每个人做一点点，也许是分享一个帖子，也许是当一门课的助教，也许是精心设计一门课的lab，更或许是将来获得教职之后开设一门高质量的课程，出版一本经典的教材。本科教育真的有什么技术壁垒吗？我看未必，教育靠的是诚意，靠的是育人之心。
今天是2021年12月12日，我期待在不久的将来这个帖子会被遗忘，大家可以满心欢喜地选着自己培养方案上的课程，做着学校自行设计的各类编程实验，课堂没有签到也能济济一堂，学生踊跃地发言互动，大家的收获可以和努力成正比，那些曾经的遗憾和痛苦可以永远成为历史。我真的很期待那一天，真的真的真的很期待。
PKUFlyingPig
2021年12月12日写于燕园
最后更新: April 3, 2022
后记 从最初的想法开始，到断断续续完成这本书，再到树洞的热烈反响，我很激动，但也五味杂陈。原来在北大这个园子里，也有那么多人，对自己的本科生涯并不满意。而这里，可是囊括了中国非常优秀的一帮年轻人。所以问题出在哪里？我不知道。
我只是个籍籍无名的本科生呀，只是一个单纯的求学者，我的目标只是想快乐地、自由地、高质量地掌握那些专业知识，我想，正在看这本书的大多数本科生也是如此，谁想付出时间但却收效甚微呢？又是谁迫使大家带着痛苦去应付呢？我不知道。
我写这本书绝不是为了鼓励大家翘课自学，试问谁不想在课堂上和那么多优秀的同学济济一堂，热烈讨论呢？谁不想遇到问题直接找老师答疑解惑呢？谁不想辛苦学习的成果可以直接化作学校承认的学分绩点呢？可如果一个兢兢业业、按时到堂的学生收获的却是痛苦，而那个一学期只有考试会出席的学生却学得自得其乐，这公平吗？我不知道。
我只是不甘，不甘心这些通过高考战胜无数人进入高校的学子本可以收获一个更快乐的本科生涯，但现实却留给了他们遗憾。我反问自己，本科教育究竟应该带给我们什么呢？是学完所有这些课程吗？倒也未必，它也许只适合我这种nerd。但我觉得，本科教育至少得展现它应有的诚意，一种分享知识的诚意，一种以人为本的诚意，一种注重学生体验的诚意。它至少不应该是一种恶意，一种拼比知识的恶意，一种胜者为王的恶意，一种让人学无所得的恶意。但这一切能改变吗？我不知道。
我只知道我做了应该做的事情，学生们会用脚投票，树洞的关注量和回帖数证明了这样一份资料是有价值的，也道出了国内CS本科教育和国外的差距。也许这样的改变是微乎其微的，但别忘了我只是一个籍籍无名的本科生，是北大信科一千多名本科生中的普通一员，是中国几百万在读本科生中的一分子，如果有更多的人站出来，每个人做一点点，也许是分享一个帖子，也许是当一门课的助教，也许是精心设计一门课的lab，更或许是将来获得教职之后开设一门高质量的课程，出版一本经典的教材。本科教育真的有什么技术壁垒吗？我看未必，教育靠的是诚意，靠的是育人之心。
今天是2021年12月12日，我期待在不久的将来这个帖子会被遗忘，大家可以满心欢喜地选着自己培养方案上的课程，做着学校自行设计的各类编程实验，课堂没有签到也能济济一堂，学生踊跃地发言互动，大家的收获可以和努力成正比，那些曾经的遗憾和痛苦可以永远成为历史。我真的很期待那一天，真的真的真的很期待。
PKUFlyingPig
2021年12月12日写于燕园
最后更新: April 3, 2022
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diff --git a/培养方案Pro/index.html b/培养方案Pro/index.html
index 106c1704..b320ea92 100644
--- a/培养方案Pro/index.html
+++ b/培养方案Pro/index.html
@@ -1,2 +1,2 @@
- 培养方案Pro - CS自学指南
培养方案Pro under construction.
最后更新: November 30, 2021
培养方案Pro under construction.
最后更新: November 30, 2021
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diff --git a/好书推荐/index.html b/好书推荐/index.html
index 21846f7c..f7550cb7 100644
--- a/好书推荐/index.html
+++ b/好书推荐/index.html
@@ -1,2 +1,2 @@
- 好书推荐 - CS自学指南
好书推荐 由于版权原因，下面列举的图书中除了开源书籍提供了链接，其他的资源请大家自行通过 libgen 查找。
另外再安利一个 GitHub 顶流热门项目 free-programming-books ，收集了非常多的免费开源编程书籍。
系统入门 Principles of Computer System Design: An Introduction Computer Systems: A Programmer's Perspective 操作系统 计算机网络 编译原理 计算机语言（PL） 体系结构 Computer Architecture: A Quantitative Approach 5th Edition Computer Organization and Design: The Hardware/Software Interface 计算机体系结构基础(第三版) 超标量处理器设计(姚永斌) CPU 设计实战 (汪文祥 邢金璋) 分布式系统 数据密集型系统设计 Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems (开源中文翻译 ) 密码学 Introduction to modern cryptography (second edition) by Jonathon Katz & Yehuda Lindell Cryptography Engineering: Design Principles and Practical Applications 数据库系统 计算机图形学 Fundamentals of Computer Graphics Fluid Simulation for Computer Graphics Real-Time Rendering, 4th edition Physically Based Rendering: From Theory To Implementation Advanced global illumination, 2nd Edition Monte Carlo theory, methods and examples 深度学习 软件工程 面试 Cracking The Coding Interview 最后更新: May 11, 2022
好书推荐 由于版权原因，下面列举的图书中除了开源书籍提供了链接，其他的资源请大家自行通过 libgen 查找。
另外再安利一个 GitHub 顶流热门项目 free-programming-books ，收集了非常多的免费开源编程书籍。
系统入门 Principles of Computer System Design: An Introduction Computer Systems: A Programmer's Perspective 操作系统 计算机网络 编译原理 计算机语言（PL） 体系结构 Computer Architecture: A Quantitative Approach 5th Edition Computer Organization and Design: The Hardware/Software Interface 计算机体系结构基础(第三版) 超标量处理器设计(姚永斌) CPU 设计实战 (汪文祥 邢金璋) 分布式系统 数据密集型系统设计 Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems (开源中文翻译 ) 密码学 Introduction to modern cryptography (second edition) by Jonathon Katz & Yehuda Lindell Cryptography Engineering: Design Principles and Practical Applications 数据库系统 计算机图形学 Fundamentals of Computer Graphics Fluid Simulation for Computer Graphics Real-Time Rendering, 4th edition Physically Based Rendering: From Theory To Implementation Advanced global illumination, 2nd Edition Monte Carlo theory, methods and examples 深度学习 软件工程 面试 Cracking The Coding Interview 最后更新: May 11, 2022
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diff --git a/并行与分布式系统/CS149/index.html b/并行与分布式系统/CS149/index.html
index 6e8da98f..2999e3aa 100644
--- a/并行与分布式系统/CS149/index.html
+++ b/并行与分布式系统/CS149/index.html
@@ -1,2 +1,2 @@
- CMU 15-418/Stanford CS149: Parallel Computing - CS自学指南
CMU 15-418/Stanford CS149: Parallel Computing 课程简介 所属大学：CMU 和 Stanford 先修要求：计算机体系结构，熟悉 C++ 编程语言：C++ 课程难度：🌟🌟🌟🌟🌟 预计学时：150 小时 Kayvon Fatahalian 教授此前在 CMU 开了 15-418 这门课，后来他成为 Stanford 的助理教授后又开了类似的课程 CS149。但总体来说，15-418 包含的课程内容更丰富，并且有课程回放，但 CS149 的编程作业更 fashion 一些。我个人是观看的 15-418 的课程录影但完成的 CS149 的作业。
这门课会带你深入理解现代并行计算架构的设计原则与必要权衡，并学会如何充分利用硬件资源以及软件编程框架（例如 CUDA，MPI，OpenMP 等）编写高性能的并行程序。由于并行计算架构的复杂性，这门课会涉及诸多高级体系结构与网络通信的内容，知识点相当底层且硬核。与此同时，5 个编程作业则是从软件的层面培养学生对上层抽象的理解与运用，具体会让你分析并行程序的瓶颈、编写多线程同步代码、学习 CUDA 编程、OpenMP 编程以及前段时间大热的 Spark 框架等等。真正意义上将理论与实践完美地结合在了一起。
课程资源 资源汇总 我在学习这门课中用到的所有资源和作业实现都汇总在 PKUFlyingPig/CS149-parallel-computing - GitHub 中。
最后更新: April 3, 2022
CMU 15-418/Stanford CS149: Parallel Computing 课程简介 所属大学：CMU 和 Stanford 先修要求：计算机体系结构，熟悉 C++ 编程语言：C++ 课程难度：🌟🌟🌟🌟🌟 预计学时：150 小时 Kayvon Fatahalian 教授此前在 CMU 开了 15-418 这门课，后来他成为 Stanford 的助理教授后又开了类似的课程 CS149。但总体来说，15-418 包含的课程内容更丰富，并且有课程回放，但 CS149 的编程作业更 fashion 一些。我个人是观看的 15-418 的课程录影但完成的 CS149 的作业。
这门课会带你深入理解现代并行计算架构的设计原则与必要权衡，并学会如何充分利用硬件资源以及软件编程框架（例如 CUDA，MPI，OpenMP 等）编写高性能的并行程序。由于并行计算架构的复杂性，这门课会涉及诸多高级体系结构与网络通信的内容，知识点相当底层且硬核。与此同时，5 个编程作业则是从软件的层面培养学生对上层抽象的理解与运用，具体会让你分析并行程序的瓶颈、编写多线程同步代码、学习 CUDA 编程、OpenMP 编程以及前段时间大热的 Spark 框架等等。真正意义上将理论与实践完美地结合在了一起。
课程资源 资源汇总 我在学习这门课中用到的所有资源和作业实现都汇总在 PKUFlyingPig/CS149-parallel-computing - GitHub 中。
最后更新: April 3, 2022
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diff --git a/并行与分布式系统/MIT6.824/index.html b/并行与分布式系统/MIT6.824/index.html
index 21e11f54..f452dd1e 100644
--- a/并行与分布式系统/MIT6.824/index.html
+++ b/并行与分布式系统/MIT6.824/index.html
@@ -1,2 +1,2 @@
- MIT 6.824: Distributed System - CS自学指南
MIT6.824: Distributed System 课程简介 所属大学：MIT 先修要求：计算机体系结构，并行编程 编程语言：Go 课程难度：🌟🌟🌟🌟🌟🌟 预计学时：200 小时 这门课和 MIT 6.S081 一样，出品自 MIT 大名鼎鼎的 PDOS 实验室，授课老师 Robert Morris 教授曾是一位顶尖黑客，世界上第一个蠕虫病毒 Morris 病毒就是出自他之手。
这门课每节课都会精读一篇分布式系统领域的经典论文，并由此传授分布式系统设计与实现的重要原则和关键技术。同时其课程 Project 也是以其难度之大而闻名遐迩，4 个编程作业循序渐进带你实现一个基于 Raft 共识算法的 KV-store 框架，让你在痛苦的 debug 中体会并行与分布式带来的随机性和复杂性。
同样，这门课由于太过出名，网上答案无数，希望大家不要参考，而是力图自主实现整个 Project。
课程资源 资源汇总 我在学习这门课中用到的所有资源和作业实现都汇总在 PKUFlyingPig/MIT6.824 - GitHub 中。此外也可以参考 OneSizeFitsQuorum 的 Lab 文档 ，其较为清晰地介绍了实现 lab 1-4 和 challenge 1-2 需要考虑的许多细节，在遇到瓶颈期时可以阅读一下~
最后更新: April 3, 2022
MIT6.824: Distributed System 课程简介 所属大学：MIT 先修要求：计算机体系结构，并行编程 编程语言：Go 课程难度：🌟🌟🌟🌟🌟🌟 预计学时：200 小时 这门课和 MIT 6.S081 一样，出品自 MIT 大名鼎鼎的 PDOS 实验室，授课老师 Robert Morris 教授曾是一位顶尖黑客，世界上第一个蠕虫病毒 Morris 病毒就是出自他之手。
这门课每节课都会精读一篇分布式系统领域的经典论文，并由此传授分布式系统设计与实现的重要原则和关键技术。同时其课程 Project 也是以其难度之大而闻名遐迩，4 个编程作业循序渐进带你实现一个基于 Raft 共识算法的 KV-store 框架，让你在痛苦的 debug 中体会并行与分布式带来的随机性和复杂性。
同样，这门课由于太过出名，网上答案无数，希望大家不要参考，而是力图自主实现整个 Project。
课程资源 资源汇总 我在学习这门课中用到的所有资源和作业实现都汇总在 PKUFlyingPig/MIT6.824 - GitHub 中。此外也可以参考 OneSizeFitsQuorum 的 Lab 文档 ，其较为清晰地介绍了实现 lab 1-4 和 challenge 1-2 需要考虑的许多细节，在遇到瓶颈期时可以阅读一下~
最后更新: April 3, 2022
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diff --git a/必学工具/CMake/index.html b/必学工具/CMake/index.html
index 2fa7f460..d101576c 100644
--- a/必学工具/CMake/index.html
+++ b/必学工具/CMake/index.html
@@ -1,2 +1,2 @@
- CMake - CS自学指南
CMake 为什么学习 CMake CMake 是类似于 GNU make 的跨平台自动软件构件工具，使用 CMakeLists.txt 定义构建规则，相比于 make 它提供了更多的功能，在各种软件构建上广泛使用。强烈建议学习使用 GNU Make 和熟悉 Makefile 后再学习 CMake 。
如何学习 CMake CMakeLists.txt 比 Makefile 更为抽象，理解和使用难度也更大。现阶段很多 IDE (如 Visual Studio, CLion) 提供了自动生成 CMakeLists.txt 的功能，但掌握 CMakeLists.txt 的基本用法仍然很有必要。除了 CMake 官方 Tutorial 外，上海交通大学 IPADS 组新人培训也提供了大约一小时的视频教程 。
最后更新: April 3, 2022
CMake 为什么学习 CMake CMake 是类似于 GNU make 的跨平台自动软件构件工具，使用 CMakeLists.txt 定义构建规则，相比于 make 它提供了更多的功能，在各种软件构建上广泛使用。强烈建议学习使用 GNU Make 和熟悉 Makefile 后再学习 CMake 。
如何学习 CMake CMakeLists.txt 比 Makefile 更为抽象，理解和使用难度也更大。现阶段很多 IDE (如 Visual Studio, CLion) 提供了自动生成 CMakeLists.txt 的功能，但掌握 CMakeLists.txt 的基本用法仍然很有必要。除了 CMake 官方 Tutorial 外，上海交通大学 IPADS 组新人培训也提供了大约一小时的视频教程 。
最后更新: April 3, 2022
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diff --git a/必学工具/Docker/index.html b/必学工具/Docker/index.html
index 54c3617b..8a35af5f 100644
--- a/必学工具/Docker/index.html
+++ b/必学工具/Docker/index.html
@@ -1,2 +1,2 @@
- Docker - CS自学指南
Docker 为什么使用 Docker 使用别人写好的软件/工具最大的障碍是什么——必然是配环境。配环境带来的折磨会极大地消解你对软件、编程本身的兴趣。虚拟机可以解决配环境的一部分问题，但它庞大笨重，且为了某个应用的环境配置好像也不值得模拟一个全新的操作系统。
Docker 的出现让环境配置变得（或许）不再折磨。简单来说 Docker 使用轻量级的“容器”（container）而不是整个操作系统去支持一个应用的配置。应用自身连同它的环境配置被打包为一个个 image 可以自由运行在不同平台的一个个 container 中，这极大地节省了所有人的时间成本。
如何学习 Docker Docker 官方文档 当然是最好的初学教材，但最好的导师一定是你自己——尝试去使用 Docker 才能享受它带来的便利。Docker 在工业界发展迅猛并已经非常成熟，你可以下载它的桌面端并使用图形界面。
当然，如果你像我一样，是一个疯狂的造轮子爱好者，那不妨自己亲手写一个迷你 Docker 来加深理解。
KodeKloud Docker for the Absolute Beginner 全面的介绍了 Docker 的基础功能，并且有大量的配套练习，同时提供免费的云环境来完成练习。其余的云相关的课程如 Kubernetes 需要付费，但个人强烈推荐：讲解非常仔细，适合从 0 开始的新手；有配套的 Kubernetes 的实验环境，不用被搭建环境劝退。
最后更新: April 3, 2022
Docker 为什么使用 Docker 使用别人写好的软件/工具最大的障碍是什么——必然是配环境。配环境带来的折磨会极大地消解你对软件、编程本身的兴趣。虚拟机可以解决配环境的一部分问题，但它庞大笨重，且为了某个应用的环境配置好像也不值得模拟一个全新的操作系统。
Docker 的出现让环境配置变得（或许）不再折磨。简单来说 Docker 使用轻量级的“容器”（container）而不是整个操作系统去支持一个应用的配置。应用自身连同它的环境配置被打包为一个个 image 可以自由运行在不同平台的一个个 container 中，这极大地节省了所有人的时间成本。
如何学习 Docker Docker 官方文档 当然是最好的初学教材，但最好的导师一定是你自己——尝试去使用 Docker 才能享受它带来的便利。Docker 在工业界发展迅猛并已经非常成熟，你可以下载它的桌面端并使用图形界面。
当然，如果你像我一样，是一个疯狂的造轮子爱好者，那不妨自己亲手写一个迷你 Docker 来加深理解。
KodeKloud Docker for the Absolute Beginner 全面的介绍了 Docker 的基础功能，并且有大量的配套练习，同时提供免费的云环境来完成练习。其余的云相关的课程如 Kubernetes 需要付费，但个人强烈推荐：讲解非常仔细，适合从 0 开始的新手；有配套的 Kubernetes 的实验环境，不用被搭建环境劝退。
最后更新: April 3, 2022
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diff --git a/必学工具/GNU_Make/index.html b/必学工具/GNU_Make/index.html
index 298cd95b..416e8731 100644
--- a/必学工具/GNU_Make/index.html
+++ b/必学工具/GNU_Make/index.html
@@ -1,2 +1,2 @@
- GNU Make - CS自学指南
GNU Make 为什么学 GNU Make 大家第一次写 hello world 程序的时候一定都记得，在编辑完 helloworld.c 之后，需要用 gcc 编译生成可执行文件，然后再执行（如果你不理解前面这段话，请先自行谷歌 gcc 编译 并理解相关内容）。但如果你的项目由成百上千个 C 源文件组成，并且星罗棋布在各个子目录下，你该如何将它们编译链接到一起呢？假如你的项目编译一次需要半个小时（大型项目相当常见），而你只修改了一个分号，是不是还需要再等半个小时呢？
这时候 GNU Make 就闪亮登场了，它能让你在一个脚本里（即所谓的 Makefile）定义整个编译流程以及各个目标文件与源文件之间的依赖关系，并且只重新编译你的修改会影响到的部分，从而降低编译的时间。
如何学习 GNU Make 这里有一篇写得深入浅出的文档 供大家参考。
GNU Make 掌握起来相对容易，但用好它需要不断的练习。将它融入到自己的日常开发中，勤于学习和模仿其他优秀开源项目里的 Makefile 的写法，总结出适合自己的 template，久而久之，你对 GNU Make 的使用会愈加纯熟。
最后更新: April 3, 2022
GNU Make 为什么学 GNU Make 大家第一次写 hello world 程序的时候一定都记得，在编辑完 helloworld.c 之后，需要用 gcc 编译生成可执行文件，然后再执行（如果你不理解前面这段话，请先自行谷歌 gcc 编译 并理解相关内容）。但如果你的项目由成百上千个 C 源文件组成，并且星罗棋布在各个子目录下，你该如何将它们编译链接到一起呢？假如你的项目编译一次需要半个小时（大型项目相当常见），而你只修改了一个分号，是不是还需要再等半个小时呢？
这时候 GNU Make 就闪亮登场了，它能让你在一个脚本里（即所谓的 Makefile）定义整个编译流程以及各个目标文件与源文件之间的依赖关系，并且只重新编译你的修改会影响到的部分，从而降低编译的时间。
如何学习 GNU Make 这里有一篇写得深入浅出的文档 供大家参考。
GNU Make 掌握起来相对容易，但用好它需要不断的练习。将它融入到自己的日常开发中，勤于学习和模仿其他优秀开源项目里的 Makefile 的写法，总结出适合自己的 template，久而久之，你对 GNU Make 的使用会愈加纯熟。
最后更新: April 3, 2022
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diff --git a/必学工具/Git/index.html b/必学工具/Git/index.html
index 0d2fdd6d..cf09af47 100644
--- a/必学工具/Git/index.html
+++ b/必学工具/Git/index.html
@@ -1,2 +1,2 @@
- Git - CS自学指南
Git 为什么使用 Git Git 是一款分布式的代码版本控制工具，Linux 之父 Linus 嫌弃当时主流的中心式的版本控制工具太难用还要花钱，就自己开发出了 Git 用来维护 Linux 的版本（给大佬跪了）。
Git 的设计非常优雅，但初学者通常因为很难理解其内部逻辑因此会觉得非常难用。对 Git 不熟悉的初学者很容易出现因为误用命令将代码给控制版本控制没了的状况（好吧是我）。
但相信我，和 Vim 一样，Git 是一款你最终掌握之后会感叹“它值得！”的神器。
如何学习 Git 和 Vim 不同，我不建议初学者在一知半解的情况下贸然使用 Git，因为它的内部逻辑并不能熟能生巧，而是需要花时间去理解。我推荐的学习路线如下：
阅读这篇 Git tutorial 。 阅读这本开源书籍 Pro Git 的 Chapter1 - Chapter5，是的没错，学 Git 需要读一本书（捂脸）。 此时你已经掌握了 Git 的原理和绝大部分用法，接下来就可以在实践中反复巩固 Git 的命令了。但用好它同样是一门哲学，我个人觉得这篇如何写好 Commit Message 的博客非常值得一读。 好的此时你已经爱上了 Git，你已经不满足于学会它了，你想自己实现一个 Git！巧了，我当年也有这样的想法，这篇 tutorial 可以满足你！ 什么？光实现一个 Git 无法满足你？小伙子/小仙女有前途，巧的是我也喜欢造轮子，这两个 GitHub 项目 build-your-own-x 和 project-based-learning 收录了你能想到的各种造轮子教程，比如：自己造个编辑器、自己写个虚拟机、自己写个 docker、自己写个 TCP 等等等等。 最后更新: April 3, 2022
Git 为什么使用 Git Git 是一款分布式的代码版本控制工具，Linux 之父 Linus 嫌弃当时主流的中心式的版本控制工具太难用还要花钱，就自己开发出了 Git 用来维护 Linux 的版本（给大佬跪了）。
Git 的设计非常优雅，但初学者通常因为很难理解其内部逻辑因此会觉得非常难用。对 Git 不熟悉的初学者很容易出现因为误用命令将代码给控制版本控制没了的状况（好吧是我）。
但相信我，和 Vim 一样，Git 是一款你最终掌握之后会感叹“它值得！”的神器。
如何学习 Git 和 Vim 不同，我不建议初学者在一知半解的情况下贸然使用 Git，因为它的内部逻辑并不能熟能生巧，而是需要花时间去理解。我推荐的学习路线如下：
阅读这篇 Git tutorial 。 阅读这本开源书籍 Pro Git 的 Chapter1 - Chapter5，是的没错，学 Git 需要读一本书（捂脸）。 此时你已经掌握了 Git 的原理和绝大部分用法，接下来就可以在实践中反复巩固 Git 的命令了。但用好它同样是一门哲学，我个人觉得这篇如何写好 Commit Message 的博客非常值得一读。 好的此时你已经爱上了 Git，你已经不满足于学会它了，你想自己实现一个 Git！巧了，我当年也有这样的想法，这篇 tutorial 可以满足你！ 什么？光实现一个 Git 无法满足你？小伙子/小仙女有前途，巧的是我也喜欢造轮子，这两个 GitHub 项目 build-your-own-x 和 project-based-learning 收录了你能想到的各种造轮子教程，比如：自己造个编辑器、自己写个虚拟机、自己写个 docker、自己写个 TCP 等等等等。 最后更新: April 3, 2022
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diff --git a/必学工具/GitHub/index.html b/必学工具/GitHub/index.html
index 8f90a6c3..c4ead54e 100644
--- a/必学工具/GitHub/index.html
+++ b/必学工具/GitHub/index.html
@@ -1,2 +1,2 @@
- GitHub - CS自学指南
GitHub GitHub 是什么 从功能上来说，GitHub 是一个在线代码托管平台。你可以将你的本地 Git 仓库托管到 GitHub 上，供多人同时开发浏览。但现如今 GitHub 的意义已远不止如此，它已经演变为一个非常活跃且资源极为丰富的开源交流社区。全世界的软件开发者在 GitHub 上分享各式各样种类繁多的开源软件。大到工业级的深度学习框架 PyTorch, TensorFlow，小到几十行的实用脚本，既有硬核的知识分享，也有保姆级的教程指导，甚至很多技术书籍也在 GitHub上开源（例如诸位正在看的这本——如果我厚着脸皮勉强称之为书的话）。闲来无事逛逛 GitHub 已经成为了我日常生活的一部分。
在 GitHub 里，星星是对一个项目至高无上的肯定，如果你觉得这本书对你有用的话，欢迎通过右上角的链接进入仓库主页献出你宝贵的星星✨。
如何使用 GitHub 如果你还从未在 GitHub 上建立过自己的远程仓库，也没有克隆过别人的代码，那么我建议你从 GitHub的官方教程 开始自己的开源之旅。
如果你想时刻关注 GitHub 上一些有趣的开源项目，那么我向你重磅推荐 HelloGitHub 这个网站以及它的同名微信公众号。它会定期收录 GitHub 上近期开始流行的或者非常有趣的开源项目，让你有机会第一时间接触各类优质资源。
GitHub 之所以成功，我想是得益于“我为人人，人人为我”的开源精神，得益于知识分享的快乐。如果你也想成为下一个万人敬仰的开源大佬，或者下一个 star 破万的项目作者。那就把你在开发过程中灵感一现的 idea 化作代码，展示在 GitHub 上吧～
不过需要提醒的是，开源社区不是法外之地，很多开源软件并不是可以随意复制分发甚至贩卖的，了解各类开源协议 并遵守，不仅是法律的要求，更是每个开源社区成员的责任。
最后更新: April 3, 2022
GitHub GitHub 是什么 从功能上来说，GitHub 是一个在线代码托管平台。你可以将你的本地 Git 仓库托管到 GitHub 上，供多人同时开发浏览。但现如今 GitHub 的意义已远不止如此，它已经演变为一个非常活跃且资源极为丰富的开源交流社区。全世界的软件开发者在 GitHub 上分享各式各样种类繁多的开源软件。大到工业级的深度学习框架 PyTorch, TensorFlow，小到几十行的实用脚本，既有硬核的知识分享，也有保姆级的教程指导，甚至很多技术书籍也在 GitHub上开源（例如诸位正在看的这本——如果我厚着脸皮勉强称之为书的话）。闲来无事逛逛 GitHub 已经成为了我日常生活的一部分。
在 GitHub 里，星星是对一个项目至高无上的肯定，如果你觉得这本书对你有用的话，欢迎通过右上角的链接进入仓库主页献出你宝贵的星星✨。
如何使用 GitHub 如果你还从未在 GitHub 上建立过自己的远程仓库，也没有克隆过别人的代码，那么我建议你从 GitHub的官方教程 开始自己的开源之旅。
如果你想时刻关注 GitHub 上一些有趣的开源项目，那么我向你重磅推荐 HelloGitHub 这个网站以及它的同名微信公众号。它会定期收录 GitHub 上近期开始流行的或者非常有趣的开源项目，让你有机会第一时间接触各类优质资源。
GitHub 之所以成功，我想是得益于“我为人人，人人为我”的开源精神，得益于知识分享的快乐。如果你也想成为下一个万人敬仰的开源大佬，或者下一个 star 破万的项目作者。那就把你在开发过程中灵感一现的 idea 化作代码，展示在 GitHub 上吧～
不过需要提醒的是，开源社区不是法外之地，很多开源软件并不是可以随意复制分发甚至贩卖的，了解各类开源协议 并遵守，不仅是法律的要求，更是每个开源社区成员的责任。
最后更新: April 3, 2022
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diff --git a/必学工具/LaTeX/index.html b/必学工具/LaTeX/index.html
index b30d8d9d..f4682eb9 100644
--- a/必学工具/LaTeX/index.html
+++ b/必学工具/LaTeX/index.html
@@ -1,2 +1,2 @@
- LaTeX - CS自学指南
LaTeX 为什么学 LaTeX 如果你需要写论文，那么请直接跳到下一节，因为你不学也得学。
LaTeX 是一种基于 TeX 的排版系统，由图灵奖得主 Lamport 开发，而 Tex 则是由 Knuth 最初开发，这两位都是计算机界的巨擘。当然开发者强并不是我们学习 LaTeX 的理由，LaTeX 和常见的所见即所得的 Word 文档最大的区别就是用户只需要关注写作的内容，而排版则完全交给软件自动完成。这让没有任何排版经验的普通人得以写出排版非常专业的论文或文章。
Berkeley计算机系教授 Christos Papadimitriou 曾说过一句半开玩笑的话：
Every time I read a LaTeX document, I think, wow, this must be correct!
如何学习 LaTeX 推荐的学习路线如下：
LaTeX 的环境配置是个比较头疼的问题。如果你本地配置 LaTeX 环境出现了问题，可以考虑使用 Overleaf 这个在线 LaTeX 编辑网站。站内不仅有各种各样的 LaTeX 模版供你选择，还免去了环境配置的难题。 阅读下面三篇 Tutorial: Part-1 , Part-2 , Part-3 。 学习 LaTeX 最好的方式当然是写论文，不过从一门数学课入手用 LaTeX 写作业也是一个不错的选择。 最后更新: April 3, 2022
LaTeX 为什么学 LaTeX 如果你需要写论文，那么请直接跳到下一节，因为你不学也得学。
LaTeX 是一种基于 TeX 的排版系统，由图灵奖得主 Lamport 开发，而 Tex 则是由 Knuth 最初开发，这两位都是计算机界的巨擘。当然开发者强并不是我们学习 LaTeX 的理由，LaTeX 和常见的所见即所得的 Word 文档最大的区别就是用户只需要关注写作的内容，而排版则完全交给软件自动完成。这让没有任何排版经验的普通人得以写出排版非常专业的论文或文章。
Berkeley计算机系教授 Christos Papadimitriou 曾说过一句半开玩笑的话：
Every time I read a LaTeX document, I think, wow, this must be correct!
如何学习 LaTeX 推荐的学习路线如下：
LaTeX 的环境配置是个比较头疼的问题。如果你本地配置 LaTeX 环境出现了问题，可以考虑使用 Overleaf 这个在线 LaTeX 编辑网站。站内不仅有各种各样的 LaTeX 模版供你选择，还免去了环境配置的难题。 阅读下面三篇 Tutorial: Part-1 , Part-2 , Part-3 。 学习 LaTeX 最好的方式当然是写论文，不过从一门数学课入手用 LaTeX 写作业也是一个不错的选择。 最后更新: April 3, 2022
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diff --git a/必学工具/Vim/index.html b/必学工具/Vim/index.html
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@@ -1,2 +1,2 @@
- Vim - CS自学指南
Vim 为什么学习 Vim 在我看来 Vim 编辑器有如下的好处：
让你的整个开发过程手指不需要离开键盘，而且光标的移动不需要方向键使得你的手指一直处在打字的最佳位置。 方便的文件切换以及面板控制可以让你同时开发多份文件甚至同一个文件的不同位置。 Vim 的宏操作可以批量化处理重复操作（例如多行 tab，批量加双引号等等） Vim 是很多服务器自带的命令行编辑器，当你通过 ssh 连接远程服务器之后，由于没有图形界面，只能在命令行里进行开发（当然现在很多 IDE 如 VS Code 提供了 ssh 插件可以解决这个问题）。 异常丰富的插件生态，让你拥有世界上最花里胡哨的命令行编辑器。 如何学习 Vim 不幸的是 Vim 的学习曲线确实相当陡峭，我花了好几个星期才慢慢适应了用 Vim 进行开发的过程。最开始你会觉得非常不适应，但一旦熬过了初始阶段，相信我，你会爱上 Vim。
Vim 的学习资料浩如烟海，但掌握它最好的方式还是将它用在日常的开发过程中，而不是一上来就去学各种花里胡哨的高级 Vim 技巧。个人推荐的学习路线如下：
先阅读这篇 tutorial ，掌握基本的 Vim 概念和使用方式。 用 Vim 自带的 vimtutor 进行练习，安装完 Vim 之后直接在命令行里输入 vimtutor 即可进入练习程序。 最后就是强迫自己使用 Vim 进行开发，IDE 里可以安装 Vim 插件。 等你完全适应 Vim 之后新的世界便向你敞开了大门，你可以按需配置自己的 Vim（修改 .vimrc 文件），网上有数不胜数的资源可以借鉴。 推荐参考资料 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. 最后更新: April 3, 2022
Vim 为什么学习 Vim 在我看来 Vim 编辑器有如下的好处：
让你的整个开发过程手指不需要离开键盘，而且光标的移动不需要方向键使得你的手指一直处在打字的最佳位置。 方便的文件切换以及面板控制可以让你同时开发多份文件甚至同一个文件的不同位置。 Vim 的宏操作可以批量化处理重复操作（例如多行 tab，批量加双引号等等） Vim 是很多服务器自带的命令行编辑器，当你通过 ssh 连接远程服务器之后，由于没有图形界面，只能在命令行里进行开发（当然现在很多 IDE 如 VS Code 提供了 ssh 插件可以解决这个问题）。 异常丰富的插件生态，让你拥有世界上最花里胡哨的命令行编辑器。 如何学习 Vim 不幸的是 Vim 的学习曲线确实相当陡峭，我花了好几个星期才慢慢适应了用 Vim 进行开发的过程。最开始你会觉得非常不适应，但一旦熬过了初始阶段，相信我，你会爱上 Vim。
Vim 的学习资料浩如烟海，但掌握它最好的方式还是将它用在日常的开发过程中，而不是一上来就去学各种花里胡哨的高级 Vim 技巧。个人推荐的学习路线如下：
先阅读这篇 tutorial ，掌握基本的 Vim 概念和使用方式。 用 Vim 自带的 vimtutor 进行练习，安装完 Vim 之后直接在命令行里输入 vimtutor 即可进入练习程序。 最后就是强迫自己使用 Vim 进行开发，IDE 里可以安装 Vim 插件。 等你完全适应 Vim 之后新的世界便向你敞开了大门，你可以按需配置自己的 Vim（修改 .vimrc 文件），网上有数不胜数的资源可以借鉴。 推荐参考资料 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. 最后更新: April 3, 2022
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diff --git a/必学工具/tools/index.html b/必学工具/tools/index.html
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+++ b/必学工具/tools/index.html
@@ -1,2 +1,2 @@
- 实用工具箱 - CS自学指南
实用工具箱 下载工具 设计工具 学习网站 最后更新: April 3, 2022
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diff --git a/必学工具/翻墙/index.html b/必学工具/翻墙/index.html
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--- a/必学工具/翻墙/index.html
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- 翻墙 - CS自学指南
翻墙 此链接 出现在这里纯属二进制 bit 的随意组合，与本人毫无关系。
最后更新: April 3, 2022
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- UCB CS162: Operating System - CS自学指南
CS162: Operating System 课程简介 所属大学：UC Berkeley 先修要求：CS61A, CS61B, CS61C 编程语言：C, x86汇编 课程难度：🌟🌟🌟🌟🌟🌟 预计学时：200 小时+，上不封顶 这门课让我记忆犹新的有两个部分：
首先是教材，这本书用的教材 Operating Systems: Principles and Practice (2nd Edition) 一共四卷，写得非常深入浅出，很好地弥补了 MIT6.S081 在理论知识上的些许空白，非常建议大家阅读。相关资源会分享在本书的经典书籍推荐模块。
其次是这门课的 Project —— Pintos。Pintos 是由 Ben Pfaff 等人在 x86 平台上编写的教学用操作系统，Ben Pfaff 甚至专门发了篇 paper 来阐述 Pintos 的设计思想。
和 MIT 的 xv6 小而精的 lab 设计理念不同，Pintos 更注重系统的 Design and Implementation。Pintos 本身仅一万行左右，只提供了操作系统最基本的功能。而 4 个Project，就是让你在这个极为精简的操作系统之上，分别为其增加线程调度机制 (Project1)，系统调用 (Project2)，虚拟内存 (Project3) 以及文件系统 (Project4)。所有的 Project 都给学生留有很大的设计空间，总代码量在 2000 行左右。根据 Stanford 学生自己的反馈 ，在 3-4 人组队的情况下，后两个 Project 的人均耗时也在 40 个小时以上。
虽然难度很大，但 Stanford, Berkeley, JHU 等多所美国顶尖名校的操统课程均采用了 Pintos。如果你真的对操作系统很感兴趣，Pintos 会极大地提高你编写和 debug 底层系统代码的能力。在本科阶段，能自己设计、实现并 debug 一个大型系统，是一段非常珍贵的经历。
北大 2022 年春季学期的操作系统实验班也将会首次引入 Pintos 作为课程 Project。我和该课程的另一位助教 整理并完善了 Pintos 的实验文档 ，并利用 Docker 配置了跨平台的实验环境，想自学的同学可以按文档自行学习。在毕业前的最后一个学期，希望能用这样的尝试，让更多人爱上系统领域，为国内的系统研究添砖加瓦。
课程资源 资源汇总 由于北大的操统实验班采用了该课程的 Project，为了防止代码抄袭，我的代码实现没有开源。
最后更新: May 11, 2022
CS162: Operating System 课程简介 所属大学：UC Berkeley 先修要求：CS61A, CS61B, CS61C 编程语言：C, x86汇编 课程难度：🌟🌟🌟🌟🌟🌟 预计学时：200 小时+，上不封顶 这门课让我记忆犹新的有两个部分：
首先是教材，这本书用的教材 Operating Systems: Principles and Practice (2nd Edition) 一共四卷，写得非常深入浅出，很好地弥补了 MIT6.S081 在理论知识上的些许空白，非常建议大家阅读。相关资源会分享在本书的经典书籍推荐模块。
其次是这门课的 Project —— Pintos。Pintos 是由 Ben Pfaff 等人在 x86 平台上编写的教学用操作系统，Ben Pfaff 甚至专门发了篇 paper 来阐述 Pintos 的设计思想。
和 MIT 的 xv6 小而精的 lab 设计理念不同，Pintos 更注重系统的 Design and Implementation。Pintos 本身仅一万行左右，只提供了操作系统最基本的功能。而 4 个Project，就是让你在这个极为精简的操作系统之上，分别为其增加线程调度机制 (Project1)，系统调用 (Project2)，虚拟内存 (Project3) 以及文件系统 (Project4)。所有的 Project 都给学生留有很大的设计空间，总代码量在 2000 行左右。根据 Stanford 学生自己的反馈 ，在 3-4 人组队的情况下，后两个 Project 的人均耗时也在 40 个小时以上。
虽然难度很大，但 Stanford, Berkeley, JHU 等多所美国顶尖名校的操统课程均采用了 Pintos。如果你真的对操作系统很感兴趣，Pintos 会极大地提高你编写和 debug 底层系统代码的能力。在本科阶段，能自己设计、实现并 debug 一个大型系统，是一段非常珍贵的经历。
北大 2022 年春季学期的操作系统实验班也将会首次引入 Pintos 作为课程 Project。我和该课程的另一位助教 整理并完善了 Pintos 的实验文档 ，并利用 Docker 配置了跨平台的实验环境，想自学的同学可以按文档自行学习。在毕业前的最后一个学期，希望能用这样的尝试，让更多人爱上系统领域，为国内的系统研究添砖加瓦。
课程资源 资源汇总 由于北大的操统实验班采用了该课程的 Project，为了防止代码抄袭，我的代码实现没有开源。
最后更新: May 11, 2022
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diff --git a/操作系统/MIT6.S081/index.html b/操作系统/MIT6.S081/index.html
index d5e2da9a..326de067 100644
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+++ b/操作系统/MIT6.S081/index.html
@@ -1,2 +1,2 @@
- MIT 6.S081: Operating System Engineering - CS自学指南
MIT 6.S081: Operating System Engineering 课程简介 所属大学：麻省理工学院 先修要求：体系结构 + 扎实的 C 语言功底 + RISC-V 汇编语言 编程语言：C, RISC-V 课程难度：🌟🌟🌟🌟🌟 预计学时：150 小时 麻省理工学院大名鼎鼎的 PDOS 实验室开设的面向MIT本科生的操作系统课程。开设这门课的教授之一 —— Robert Morris 教授曾是一位顶尖黑客，世界上第一个蠕虫病毒 Morris 就是出自他之手。
这门课的前身是 MIT 著名的课程 6.828，MIT 的几位教授为了这门课曾专门开发了一个基于 x86 的教学用操作系统 JOS，被众多名校作为自己的操统课程实验。但随着 RISC-V 的横空出世，这几位教授又基于 RISC-V 开发了一个新的教学用操作系统 xv6，并开设了 MIT6.S081 这门课。由于 RISC-V 轻便易学的特点，学生不需要像此前 JOS 一样纠结于众多 x86 “特有的”为了兼容而遗留下来的复杂机制，而可以专注于操作系统层面的开发。
这几位教授还专门写了一本教程 ，详细讲解了 xv6 的设计思想和实现细节。
这门课的讲授也很有意思，老师会带着学生依照 xv6 的源代码去理解操作系统的众多机制和设计细节，而不是停留于理论知识。每周都会有一个 lab，让你在 xv6 上增加一些新的机制和特性，非常注重学生动手能力的培养。整个学期一共有 11 个 lab，让你全方位地深刻理解操作系统的每个部分，非常有成就感。而且所有的lab都有着非常完善的测试框架，有的测试代码甚至上千行，让人不得不佩服 MIT 的几位教授为了教好这门课所付出的心血。
这门课的后半程会讲授操作系统领域的多篇经典论文，涉及文件系统、系统安全、网络、虚拟化等等多个主题，让你有机会接触到学界最前沿的研究方向。
课程资源 xv6 补充资源 资源汇总 我在学习这门课中用到的所有资源和作业实现都汇总在 PKUFlyingPig/MIT6.S081-2020fall - GitHub 中。此外也可以参考 KuangjuX 实现的 MIT 6.S081 的几个 lab 的题解 ，里面亦有详细的解法和补充知识，另外，KuangjuX 还使用 Rust 语言重新实现过 xv6-riscv 操作系统：xv6-rust ，里面对于 xv6-riscv 有更为详细的思考和讨论，感兴趣的同学可以看一下哦。
一些可以参考的博客 最后更新: May 11, 2022
MIT 6.S081: Operating System Engineering 课程简介 所属大学：麻省理工学院 先修要求：体系结构 + 扎实的 C 语言功底 + RISC-V 汇编语言 编程语言：C, RISC-V 课程难度：🌟🌟🌟🌟🌟 预计学时：150 小时 麻省理工学院大名鼎鼎的 PDOS 实验室开设的面向MIT本科生的操作系统课程。开设这门课的教授之一 —— Robert Morris 教授曾是一位顶尖黑客，世界上第一个蠕虫病毒 Morris 就是出自他之手。
这门课的前身是 MIT 著名的课程 6.828，MIT 的几位教授为了这门课曾专门开发了一个基于 x86 的教学用操作系统 JOS，被众多名校作为自己的操统课程实验。但随着 RISC-V 的横空出世，这几位教授又基于 RISC-V 开发了一个新的教学用操作系统 xv6，并开设了 MIT6.S081 这门课。由于 RISC-V 轻便易学的特点，学生不需要像此前 JOS 一样纠结于众多 x86 “特有的”为了兼容而遗留下来的复杂机制，而可以专注于操作系统层面的开发。
这几位教授还专门写了一本教程 ，详细讲解了 xv6 的设计思想和实现细节。
这门课的讲授也很有意思，老师会带着学生依照 xv6 的源代码去理解操作系统的众多机制和设计细节，而不是停留于理论知识。每周都会有一个 lab，让你在 xv6 上增加一些新的机制和特性，非常注重学生动手能力的培养。整个学期一共有 11 个 lab，让你全方位地深刻理解操作系统的每个部分，非常有成就感。而且所有的lab都有着非常完善的测试框架，有的测试代码甚至上千行，让人不得不佩服 MIT 的几位教授为了教好这门课所付出的心血。
这门课的后半程会讲授操作系统领域的多篇经典论文，涉及文件系统、系统安全、网络、虚拟化等等多个主题，让你有机会接触到学界最前沿的研究方向。
课程资源 xv6 补充资源 资源汇总 我在学习这门课中用到的所有资源和作业实现都汇总在 PKUFlyingPig/MIT6.S081-2020fall - GitHub 中。此外也可以参考 KuangjuX 实现的 MIT 6.S081 的几个 lab 的题解 ，里面亦有详细的解法和补充知识，另外，KuangjuX 还使用 Rust 语言重新实现过 xv6-riscv 操作系统：xv6-rust ，里面对于 xv6-riscv 有更为详细的思考和讨论，感兴趣的同学可以看一下哦。
一些可以参考的博客 最后更新: May 11, 2022
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diff --git a/数学基础/MITLA/index.html b/数学基础/MITLA/index.html
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+++ b/数学基础/MITLA/index.html
@@ -1,2 +1,2 @@
- MIT18.06: Linear Algebra - CS自学指南
MIT18.06: Linear Algebra 课程简介 所属大学：MIT 先修要求：英文 编程语言：无 课程难度：🌟🌟🌟 预计学时：因人而异 数学大牛 Gilbert Strang 老先生年逾古稀仍坚持授课，其经典教材 Introduction to Linear Algebra 已被清华采用为官方教材。我当时看完盗版 PDF 之后深感愧疚，含泪花了两百多买了一本英文正版收藏。下面附上此书封面，如果你能完全理解封面图的数学含义，那你对线性代数的理解一定会达到新的高度。
配合油管数学网红 3Blue1Brown 的线性代数的本质 系列视频食用更佳。
课程资源 最后更新: April 3, 2022
MIT18.06: Linear Algebra 课程简介 所属大学：MIT 先修要求：英文 编程语言：无 课程难度：🌟🌟🌟 预计学时：因人而异 数学大牛 Gilbert Strang 老先生年逾古稀仍坚持授课，其经典教材 Introduction to Linear Algebra 已被清华采用为官方教材。我当时看完盗版 PDF 之后深感愧疚，含泪花了两百多买了一本英文正版收藏。下面附上此书封面，如果你能完全理解封面图的数学含义，那你对线性代数的理解一定会达到新的高度。
配合油管数学网红 3Blue1Brown 的线性代数的本质 系列视频食用更佳。
课程资源 最后更新: April 3, 2022
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- MIT18.01/18.02: Calculus - CS自学指南
MIT Calculus Course 课程简介 所属大学：MIT 先修要求：英语 编程语言：无 课程难度：🌟🌟 预计学时：因人而异 MIT 的微积分课由 MIT18.01: Single variable calculus 和 MIT18.02: Multi variable calculus 两门课组成。对自己数学基础比较自信的同学可以只看课程 notes，写得非常浅显生动并且抓住本质，让你不再疲于做题而是能够真正窥见微积分的本质魅力。
配合油管数学网红 3Blue1Brown 的微积分的本质 系列视频食用更佳。
课程资源 课程网站：18.01 , 18.02 课程视频：参见课程网站 课程教材：参见课程 notes 课程作业：书面作业及答案参见课程网站 最后更新: April 3, 2022
MIT Calculus Course 课程简介 所属大学：MIT 先修要求：英语 编程语言：无 课程难度：🌟🌟 预计学时：因人而异 MIT 的微积分课由 MIT18.01: Single variable calculus 和 MIT18.02: Multi variable calculus 两门课组成。对自己数学基础比较自信的同学可以只看课程 notes，写得非常浅显生动并且抓住本质，让你不再疲于做题而是能够真正窥见微积分的本质魅力。
配合油管数学网红 3Blue1Brown 的微积分的本质 系列视频食用更佳。
课程资源 课程网站：18.01 , 18.02 课程视频：参见课程网站 课程教材：参见课程 notes 课程作业：书面作业及答案参见课程网站 最后更新: April 3, 2022
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diff --git a/数学基础/information/index.html b/数学基础/information/index.html
index 36957740..188544fc 100644
--- a/数学基础/information/index.html
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- MIT6.050J: Information theory and Entropy - CS自学指南
课程简介 所属大学：MIT 先修要求：无 编程语言：无 课程难度：🌟🌟🌟 预计学时：100 小时 MIT 面向大一新生的信息论入门课程，Penfield 教授专门为这门课写了一本教材 作为课程 notes，内容深入浅出，生动有趣。
课程资源 最后更新: April 3, 2022
课程简介 所属大学：MIT 先修要求：无 编程语言：无 课程难度：🌟🌟🌟 预计学时：100 小时 MIT 面向大一新生的信息论入门课程，Penfield 教授专门为这门课写了一本教材 作为课程 notes，内容深入浅出，生动有趣。
课程资源 最后更新: April 3, 2022
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diff --git a/数学进阶/6.042J/index.html b/数学进阶/6.042J/index.html
index 23ff954d..1d71f867 100644
--- a/数学进阶/6.042J/index.html
+++ b/数学进阶/6.042J/index.html
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- MIT 6.042J: Mathematics for Computer Science - CS自学指南
MIT 6.042J: Mathematics for Computer Science 课程简介 所属大学：MIT 先修要求：Calculus, Linear Algebra 编程语言：Python preferred 课程难度：🌟🌟🌟 预计学时：50-70 小时 MIT 的离散数学以及概率综合课程，导师是大名鼎鼎的 Tom Leighton ( Akamai 的联合创始人之一)。学完之后对于后续的算法学习大有裨益。
课程资源 最后更新: April 3, 2022
MIT 6.042J: Mathematics for Computer Science 课程简介 所属大学：MIT 先修要求：Calculus, Linear Algebra 编程语言：Python preferred 课程难度：🌟🌟🌟 预计学时：50-70 小时 MIT 的离散数学以及概率综合课程，导师是大名鼎鼎的 Tom Leighton ( Akamai 的联合创始人之一)。学完之后对于后续的算法学习大有裨益。
课程资源 最后更新: April 3, 2022
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diff --git a/数学进阶/CS126/index.html b/数学进阶/CS126/index.html
index 34d194bb..96a6561d 100644
--- a/数学进阶/CS126/index.html
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@@ -1,2 +1,2 @@
- UCB CS126: probability theory - CS自学指南
UCB CS126 : Probability theory 课程简介 所属大学：UC Berkeley 先修要求：CS70、微积分、线性代数 编程语言：Python 课程难度：🌟🌟🌟🌟🌟 预计学时：100 小时 伯克利的概率论进阶课程，涉及到统计学、随机过程等理论相对深入的内容，需要相当的数学基础，我在上这门课的时候也感到有些吃力，不过坚持下来一定会让你对概率论的掌握达到一个新的高度。
同时这门课非常强调理论与实践的结合，课程设计者 Jean Walrand 教授专门写了一本配套的教材Probability in Electrical Engineering and Computer Science ，书中每个章节都会以一个具体的算法实践作为例子来展示理论在实际当中的运用，例如 PageRank, Route Planing, Speech Recognition 等等，并且全书开源，可以免费下载 PDF 或者 Epub 版。
这还不算完，Jean Walrand 还为整本书里的例子设计了配套的 Python 实现，以 Jupyter Notebook 的形式在线发布，读者可以在线修改、调试和运行。
与此同时，这门课除了理论作业之外，还有 9 个编程作业，会让你用概率论的知识解决实际问题。
课程资源 资源汇总 我在学习这门课中用到的所有资源和作业实现都汇总在 PKUFlyingPig/EECS126 - GitHub 中。
最后更新: April 3, 2022
UCB CS126 : Probability theory 课程简介 所属大学：UC Berkeley 先修要求：CS70、微积分、线性代数 编程语言：Python 课程难度：🌟🌟🌟🌟🌟 预计学时：100 小时 伯克利的概率论进阶课程，涉及到统计学、随机过程等理论相对深入的内容，需要相当的数学基础，我在上这门课的时候也感到有些吃力，不过坚持下来一定会让你对概率论的掌握达到一个新的高度。
同时这门课非常强调理论与实践的结合，课程设计者 Jean Walrand 教授专门写了一本配套的教材Probability in Electrical Engineering and Computer Science ，书中每个章节都会以一个具体的算法实践作为例子来展示理论在实际当中的运用，例如 PageRank, Route Planing, Speech Recognition 等等，并且全书开源，可以免费下载 PDF 或者 Epub 版。
这还不算完，Jean Walrand 还为整本书里的例子设计了配套的 Python 实现，以 Jupyter Notebook 的形式在线发布，读者可以在线修改、调试和运行。
与此同时，这门课除了理论作业之外，还有 9 个编程作业，会让你用概率论的知识解决实际问题。
课程资源 资源汇总 我在学习这门课中用到的所有资源和作业实现都汇总在 PKUFlyingPig/EECS126 - GitHub 中。
最后更新: April 3, 2022
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- UCB CS70: discrete Math and probability theory - CS自学指南
UCB CS70 : discrete Math and probability theory 课程简介 所属大学：UC Berkeley 先修要求：无 编程语言：无 课程难度：🌟🌟🌟 预计学时：60 小时 伯克利的离散数学入门课程，个人觉得这门课最大的亮点在于并不是单纯的理论知识的讲授，而是在每个模块都会介绍理论知识在实际算法中的运用，让计算机系的学生在夯实理论基础的同时，跳脱出冰冷形式化的数学符号，在实际应用中感受和体会理论的本质。
具体的理论与算法的对应关系列举如下：
逻辑证明：稳定匹配算法 图论：网络拓扑设计 基础数论：RSA 算法 多项式环：纠错码设计 概率论：哈希表设计、负载均衡等等 课程 notes 也写得非常深入浅出，公式推导与实际例子星罗棋布，阅读体验很好。
课程资源 资源汇总 我在学习这门课中用到的所有资源和作业实现都汇总在 PKUFlyingPig/UCB-CS70 - GitHub 中。
最后更新: April 3, 2022
UCB CS70 : discrete Math and probability theory 课程简介 所属大学：UC Berkeley 先修要求：无 编程语言：无 课程难度：🌟🌟🌟 预计学时：60 小时 伯克利的离散数学入门课程，个人觉得这门课最大的亮点在于并不是单纯的理论知识的讲授，而是在每个模块都会介绍理论知识在实际算法中的运用，让计算机系的学生在夯实理论基础的同时，跳脱出冰冷形式化的数学符号，在实际应用中感受和体会理论的本质。
具体的理论与算法的对应关系列举如下：
逻辑证明：稳定匹配算法 图论：网络拓扑设计 基础数论：RSA 算法 多项式环：纠错码设计 概率论：哈希表设计、负载均衡等等 课程 notes 也写得非常深入浅出，公式推导与实际例子星罗棋布，阅读体验很好。
课程资源 资源汇总 我在学习这门课中用到的所有资源和作业实现都汇总在 PKUFlyingPig/UCB-CS70 - GitHub 中。
最后更新: April 3, 2022
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diff --git a/数学进阶/The_Information_Theory_Pattern_Recognition_and_Neural_Networks/index.html b/数学进阶/The_Information_Theory_Pattern_Recognition_and_Neural_Networks/index.html
index 25262c81..3e855a79 100644
--- a/数学进阶/The_Information_Theory_Pattern_Recognition_and_Neural_Networks/index.html
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@@ -1,2 +1,2 @@
- The Information Theory, Patter Recognition, and Neural Networks - CS自学指南
课程简介 所属大学：Cambridge 先修要求：Calculus, Linear Algebra, Probabilities and Statistics 编程语言：Anything would be OK, Python preferred 课程难度：🌟🌟🌟 预计学时：30-50 小时 剑桥大学 Sir David MacKay 教授的信息论课程。教授是一位十分精通信息论与神经网络的学者，课程对应教材也是信息论领域的一部经典著作。可惜天妒英才...
课程资源 R.I.P Prof. David MacKay 最后更新: April 3, 2022
课程简介 所属大学：Cambridge 先修要求：Calculus, Linear Algebra, Probabilities and Statistics 编程语言：Anything would be OK, Python preferred 课程难度：🌟🌟🌟 预计学时：30-50 小时 剑桥大学 Sir David MacKay 教授的信息论课程。教授是一位十分精通信息论与神经网络的学者，课程对应教材也是信息论领域的一部经典著作。可惜天妒英才...
课程资源 R.I.P Prof. David MacKay 最后更新: April 3, 2022
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diff --git a/数学进阶/convex/index.html b/数学进阶/convex/index.html
index 2b66dc4d..03b43a93 100644
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- Standford EE364A: Convex Optimization - CS自学指南
Standford EE364A: Convex Optimization 课程简介 所属大学：Stanford 先修要求：Python，微积分，线性代数，概率论，数值分析 编程语言：Python 课程难度：🌟🌟🌟🌟🌟 预计学时：150 小时 Stephen Boyd 教授是凸优化领域的大牛，其编写的 Convex Optimization 这本教材被众多名校采用。另外其研究团队还专门开发了一个用于求解常见凸优化问题的编程框架，支持 Python, Julia 等主流编程语言，其课程作业也是采用这个编程框架去解决实际生活当中的凸优化问题。
在实际运用当中，你会深刻体会到对于同一个问题，建模过程中一个细小的改变，其方程的求解难度会有天壤之别，如何让你建模的方程是“凸”的，是一门艺术。
课程资源 资源汇总 我在学习这门课中用到的所有资源和作业实现都汇总在 PKUFlyingPig/Standford_CVX101 - GitHub 中。
最后更新: April 3, 2022
Standford EE364A: Convex Optimization 课程简介 所属大学：Stanford 先修要求：Python，微积分，线性代数，概率论，数值分析 编程语言：Python 课程难度：🌟🌟🌟🌟🌟 预计学时：150 小时 Stephen Boyd 教授是凸优化领域的大牛，其编写的 Convex Optimization 这本教材被众多名校采用。另外其研究团队还专门开发了一个用于求解常见凸优化问题的编程框架，支持 Python, Julia 等主流编程语言，其课程作业也是采用这个编程框架去解决实际生活当中的凸优化问题。
在实际运用当中，你会深刻体会到对于同一个问题，建模过程中一个细小的改变，其方程的求解难度会有天壤之别，如何让你建模的方程是“凸”的，是一门艺术。
课程资源 资源汇总 我在学习这门课中用到的所有资源和作业实现都汇总在 PKUFlyingPig/Standford_CVX101 - GitHub 中。
最后更新: April 3, 2022
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diff --git a/数学进阶/numerical/index.html b/数学进阶/numerical/index.html
index abf64c7b..540f489d 100644
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- MIT18.330: Introduction to numerical analysis - CS自学指南
MIT18.330 : Introduction to numerical analysis 课程简介 所属大学：MIT 先修要求：微积分，线性代数，概率论 编程语言：Julia 课程难度：🌟🌟🌟🌟🌟 预计学时：150 小时 计算机强大的计算能力帮助人们在科学领域不断突破边界，不过计算机的离散本质和这个连续的世界有着天然鸿沟，而如何用离散的表示去估计和逼近那些数学上连续的概念，则是数值分析的重要主题。
这门课会在浮点表示、方程求解、线性代数、微积分、线性代数、微分方程等领域探讨各类数值分析方法，让你在 Julia 的编程实践中反复体悟（1）如何建立估计（2）如何估计误差（3）如何用算法实现估计 这一系列步骤。
这门课的设计者还编写了配套的开源教材（参见下方链接），里面有丰富的 Julia 实例。
课程资源 资源汇总 我在学习这门课中用到的所有资源和作业实现都汇总在 PKUFlyingPig/MIT18.330 - GitHub 中。
最后更新: April 3, 2022
MIT18.330 : Introduction to numerical analysis 课程简介 所属大学：MIT 先修要求：微积分，线性代数，概率论 编程语言：Julia 课程难度：🌟🌟🌟🌟🌟 预计学时：150 小时 计算机强大的计算能力帮助人们在科学领域不断突破边界，不过计算机的离散本质和这个连续的世界有着天然鸿沟，而如何用离散的表示去估计和逼近那些数学上连续的概念，则是数值分析的重要主题。
这门课会在浮点表示、方程求解、线性代数、微积分、线性代数、微分方程等领域探讨各类数值分析方法，让你在 Julia 的编程实践中反复体悟（1）如何建立估计（2）如何估计误差（3）如何用算法实现估计 这一系列步骤。
这门课的设计者还编写了配套的开源教材（参见下方链接），里面有丰富的 Julia 实例。
课程资源 资源汇总 我在学习这门课中用到的所有资源和作业实现都汇总在 PKUFlyingPig/MIT18.330 - GitHub 中。
最后更新: April 3, 2022
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diff --git a/数据库系统/15445/index.html b/数据库系统/15445/index.html
index 10f729e7..fb74363b 100644
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- CMU 15-445: Database Systems - CS自学指南
CMU 15-445: Database Systems 课程简介 所属大学：CMU 先修要求：C++，数据结构与算法 编程语言：C++ 课程难度：🌟🌟🌟🌟 预计学时：100 小时 作为 CMU 数据库的入门课，这门课由数据库领域的大牛 Andy Pavlo 讲授（“这个世界上我只在乎两件事，一是我的老婆，二就是数据库”）。15-445 会自底向上地教你数据库系统的基本组成部分：存储、索引、查询，以及并发事务控制。 这门课的亮点在于 CMU db 专门为此课开发了一个教学用的关系型数据库 bustub ，并要求你对这个数据库的组成部分进行修改，实现上述部件的功能。此外 bustub 作为一个 C++ 编写的中小型项目涵盖了程序构建、代码规范、单元测试等众多要求，可以作为一个优秀的开源项目学习。
课程资源 课程网站：Fall2019 , Fall2020 课程视频：课程网站免费观看 课程教材：Database System Concepts 课程作业：4 个 Project 在 Fall2019 中，第二个 Project 是做哈希索引，第四个 Project 是做日志与恢复
在 Fall2020 中，第二个 Project 是做 B 树，第四个 Project 是做并发控制
如果大家有精力的话可以都去尝试一下，或者在对书中内容理解不是很透彻的时候，尝试用代码写一个会加深你的理解。
我在学习这门课中用到的所有资源和作业实现都汇总在 ysj1173886760/Learning: db - GitHub 中。
由于 Andy 的要求，仓库中没有 Project 的实现，只有 Homework 的 Solution。特别的，对于 Homework1，我还写了一个 Shell 脚本来帮大家执行自动判分
另外在课程结束后，我十分推荐阅读一篇论文 Architecture Of a Database System ，对应的中文版我上传到了链接中的仓库。论文里综述了数据库系统的整体架构，让大家可以对数据库有一个更加全面的视野。
后续课程 CMU15-721
主要讲主存数据库有关的内容，每节课都有对应的 paper 要读，推荐给希望进阶数据库的小伙伴
我目前也在跟进这门课，完成后会在这里提 PR 以提供进阶的指导
最后更新: April 3, 2022
CMU 15-445: Database Systems 课程简介 所属大学：CMU 先修要求：C++，数据结构与算法 编程语言：C++ 课程难度：🌟🌟🌟🌟 预计学时：100 小时 作为 CMU 数据库的入门课，这门课由数据库领域的大牛 Andy Pavlo 讲授（“这个世界上我只在乎两件事，一是我的老婆，二就是数据库”）。15-445 会自底向上地教你数据库系统的基本组成部分：存储、索引、查询，以及并发事务控制。 这门课的亮点在于 CMU db 专门为此课开发了一个教学用的关系型数据库 bustub ，并要求你对这个数据库的组成部分进行修改，实现上述部件的功能。此外 bustub 作为一个 C++ 编写的中小型项目涵盖了程序构建、代码规范、单元测试等众多要求，可以作为一个优秀的开源项目学习。
课程资源 课程网站：Fall2019 , Fall2020 课程视频：课程网站免费观看 课程教材：Database System Concepts 课程作业：4 个 Project 在 Fall2019 中，第二个 Project 是做哈希索引，第四个 Project 是做日志与恢复
在 Fall2020 中，第二个 Project 是做 B 树，第四个 Project 是做并发控制
如果大家有精力的话可以都去尝试一下，或者在对书中内容理解不是很透彻的时候，尝试用代码写一个会加深你的理解。
我在学习这门课中用到的所有资源和作业实现都汇总在 ysj1173886760/Learning: db - GitHub 中。
由于 Andy 的要求，仓库中没有 Project 的实现，只有 Homework 的 Solution。特别的，对于 Homework1，我还写了一个 Shell 脚本来帮大家执行自动判分
另外在课程结束后，我十分推荐阅读一篇论文 Architecture Of a Database System ，对应的中文版我上传到了链接中的仓库。论文里综述了数据库系统的整体架构，让大家可以对数据库有一个更加全面的视野。
后续课程 CMU15-721
主要讲主存数据库有关的内容，每节课都有对应的 paper 要读，推荐给希望进阶数据库的小伙伴
我目前也在跟进这门课，完成后会在这里提 PR 以提供进阶的指导
最后更新: April 3, 2022
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diff --git a/数据库系统/CS186/index.html b/数据库系统/CS186/index.html
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- UCB CS186: Introduction to Database System - CS自学指南
UCB CS186: Introduction to Database System 课程简介 所属大学：UC Berkeley 先修要求：CS61A, CS61B, CS61C 编程语言：Java 课程难度：🌟🌟🌟🌟🌟 预计学时：150 小时 如何编写 SQL 查询？SQL 命令是如何被一步步拆解、优化、转变为一个个磁盘查询指令的？如何实现高并发的数据库？如何实现数据库的故障恢复？什么又是非关系型数据库？这门课会带你深入理解关系型数据库的内部细节，并在掌握理论知识之后，动手用 Java 实现一个支持 SQL 并发查询、B+ 树 Index 和故障恢复的关系型数据库。
从实用角度来说，这门课还会在编程作业中锻炼你编写 SQL 查询以及 NoSQL 查询的能力，对于构建一些全栈的工程项目很有帮助。
课程资源 资源汇总 我在学习这门课中用到的所有资源和作业实现都汇总在 PKUFlyingPig/CS186 - GitHub 中。
最后更新: April 3, 2022
UCB CS186: Introduction to Database System 课程简介 所属大学：UC Berkeley 先修要求：CS61A, CS61B, CS61C 编程语言：Java 课程难度：🌟🌟🌟🌟🌟 预计学时：150 小时 如何编写 SQL 查询？SQL 命令是如何被一步步拆解、优化、转变为一个个磁盘查询指令的？如何实现高并发的数据库？如何实现数据库的故障恢复？什么又是非关系型数据库？这门课会带你深入理解关系型数据库的内部细节，并在掌握理论知识之后，动手用 Java 实现一个支持 SQL 并发查询、B+ 树 Index 和故障恢复的关系型数据库。
从实用角度来说，这门课还会在编程作业中锻炼你编写 SQL 查询以及 NoSQL 查询的能力，对于构建一些全栈的工程项目很有帮助。
课程资源 资源汇总 我在学习这门课中用到的所有资源和作业实现都汇总在 PKUFlyingPig/CS186 - GitHub 中。
最后更新: April 3, 2022
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diff --git a/数据科学/Data100/index.html b/数据科学/Data100/index.html
index 4d4d026b..a9691214 100644
--- a/数据科学/Data100/index.html
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@@ -1,2 +1,2 @@
- UCB Data100: Principles and Techniques of Data Science - CS自学指南
UCB Data100: Principles and Techniques of Data Science 课程简介 所属大学：UC Berkeley 先修要求：CS61A，线性代数 编程语言：Python 课程难度：🌟🌟🌟 预计学时：80 小时 伯克利的数据科学入门课程，内容相对基础，覆盖了数据清洗、特征提取、数据可视化以及机器学习和推理的基础内容，也会讲授 Pandas, Numpy, Matplotlib 等数据科学常用工具。其丰富有趣的编程作业也是这门课的一大亮点。
课程资源 最后更新: April 3, 2022
UCB Data100: Principles and Techniques of Data Science 课程简介 所属大学：UC Berkeley 先修要求：CS61A，线性代数 编程语言：Python 课程难度：🌟🌟🌟 预计学时：80 小时 伯克利的数据科学入门课程，内容相对基础，覆盖了数据清洗、特征提取、数据可视化以及机器学习和推理的基础内容，也会讲授 Pandas, Numpy, Matplotlib 等数据科学常用工具。其丰富有趣的编程作业也是这门课的一大亮点。
课程资源 最后更新: April 3, 2022
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diff --git a/数据结构与算法/Algo/index.html b/数据结构与算法/Algo/index.html
index 4af28674..53d3adb0 100644
--- a/数据结构与算法/Algo/index.html
+++ b/数据结构与算法/Algo/index.html
@@ -1,2 +1,2 @@
- Coursera: Algorithms I & II - CS自学指南
Coursera: Algorithms I & II 课程简介 所属大学：Princeton 先修要求：CS61A 编程语言：Java 课程难度：🌟🌟🌟 预计学时：60 小时 这是 Coursera 上评分最高的算法课程。Robert Sedgewick 教授有一种魔力，可以将无论多么复杂的算法讲得极为生动浅显。实不相瞒，困扰我多年的 KMP 以及网络流算法都是在这门课上让我茅塞顿开的，时隔两年我甚至还能写出这两个算法的推导与证明。
你是否觉得算法学了就忘呢？我觉得让你完全掌握一个算法的核心在于理解三点：
为什么这么做？（正确性推导，抑或是整个算法的核心本质） 如何实现它？（光学不用假把式） 用它解决实际问题（学以致用才是真本事） 这门课的构成就非常好地契合了上述三个步骤。观看课程视频并且阅读教授的开源课本 有助于你理解算法的本质，让你也可以用非常 生动浅显的话语向别人讲述为什么这个算法得长这个样子。
在理解算法之后，你可以阅读教授对于课程中讲授的所有数据结构与算法的代码实现 。 注意，这些实现可不是 demo 性质的，而是工业级的高效实现，从注释到变量命名都非常严谨，模块化也做得相当好，是质量很高的代码。我从这些代码中收获良多。
最后，就是这门课最激动人心的部分了，10 个高质量的 Project，并且全都有实际问题的背景描述，丰富的测试样例，自动的评分系统（代码风格也是评分的一环）。让你在实际生活中 领略算法的魅力。
课程资源 资源汇总 我在学习这门课中用到的所有资源和作业实现都汇总在 PKUFlyingPig/Princeton-Algorithm - GitHub 中。
最后更新: April 3, 2022
Coursera: Algorithms I & II 课程简介 所属大学：Princeton 先修要求：CS61A 编程语言：Java 课程难度：🌟🌟🌟 预计学时：60 小时 这是 Coursera 上评分最高的算法课程。Robert Sedgewick 教授有一种魔力，可以将无论多么复杂的算法讲得极为生动浅显。实不相瞒，困扰我多年的 KMP 以及网络流算法都是在这门课上让我茅塞顿开的，时隔两年我甚至还能写出这两个算法的推导与证明。
你是否觉得算法学了就忘呢？我觉得让你完全掌握一个算法的核心在于理解三点：
为什么这么做？（正确性推导，抑或是整个算法的核心本质） 如何实现它？（光学不用假把式） 用它解决实际问题（学以致用才是真本事） 这门课的构成就非常好地契合了上述三个步骤。观看课程视频并且阅读教授的开源课本 有助于你理解算法的本质，让你也可以用非常 生动浅显的话语向别人讲述为什么这个算法得长这个样子。
在理解算法之后，你可以阅读教授对于课程中讲授的所有数据结构与算法的代码实现 。 注意，这些实现可不是 demo 性质的，而是工业级的高效实现，从注释到变量命名都非常严谨，模块化也做得相当好，是质量很高的代码。我从这些代码中收获良多。
最后，就是这门课最激动人心的部分了，10 个高质量的 Project，并且全都有实际问题的背景描述，丰富的测试样例，自动的评分系统（代码风格也是评分的一环）。让你在实际生活中 领略算法的魅力。
课程资源 资源汇总 我在学习这门课中用到的所有资源和作业实现都汇总在 PKUFlyingPig/Princeton-Algorithm - GitHub 中。
最后更新: April 3, 2022
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index e9c1451f..5a192683 100644
--- a/数据结构与算法/CS106B_CS106X/index.html
+++ b/数据结构与算法/CS106B_CS106X/index.html
@@ -1,2 +1,2 @@
- Stanford CS106B/X - CS自学指南
Stanford CS106B/X: Programming Abstractions in C++ 课程简介 所属大学：Stanford 先修要求：计算机基础 (CS50/CS106A/CS61A or equivalent) 编程语言：C++ 课程难度：🌟🌟 预计学时：50-70 小时 Stanford 的进阶编程课，CS106X 在难度和深度上会比 CS106B 有所提高，但主体内容类似。主要通过 C++ 语言让学生在实际的编程作业里培养通过编程抽象解决实际问题的能力，同时也会涉及一些简单的数据结构和算法的知识，但总体来说没有一门专门的数据结构课那么系统。
课程资源 最后更新: April 3, 2022
Stanford CS106B/X: Programming Abstractions in C++ 课程简介 所属大学：Stanford 先修要求：计算机基础 (CS50/CS106A/CS61A or equivalent) 编程语言：C++ 课程难度：🌟🌟 预计学时：50-70 小时 Stanford 的进阶编程课，CS106X 在难度和深度上会比 CS106B 有所提高，但主体内容类似。主要通过 C++ 语言让学生在实际的编程作业里培养通过编程抽象解决实际问题的能力，同时也会涉及一些简单的数据结构和算法的知识，但总体来说没有一门专门的数据结构课那么系统。
课程资源 最后更新: April 3, 2022
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+++ b/数据结构与算法/CS170/index.html
@@ -1,2 +1,2 @@
- UCB CS170: Efficient Algorithms and Intractable Problems - CS自学指南
CS170: Efficient Algorithms and Intractable Problems 课程简介 所属大学：UC Berkeley 先修要求：CS61B, CS70 编程语言：LaTeX 课程难度：🌟🌟🌟 预计学时：60 小时 伯克利的算法设计课，更注重算法的理论基础与复杂度分析。课程内容涵盖了分治、图算法、最短路、生成树、贪心、动规、并查集、线性规划、网络流、NP 问题、随机算法、哈希算法等等。
这门课的教材写的很好，证明浅显易懂，非常适合作为工具书查阅。另外，这门课只有书面作业，并且推荐用 LaTeX 编写，大家可以借此机会锻炼自己的 LaTeX 技巧。
课程资源 资源汇总 我在学习这门课中用到的所有资源和作业实现都汇总在 PKUFlyingPig/UCB-CS170 - GitHub 中。
最后更新: April 3, 2022
CS170: Efficient Algorithms and Intractable Problems 课程简介 所属大学：UC Berkeley 先修要求：CS61B, CS70 编程语言：LaTeX 课程难度：🌟🌟🌟 预计学时：60 小时 伯克利的算法设计课，更注重算法的理论基础与复杂度分析。课程内容涵盖了分治、图算法、最短路、生成树、贪心、动规、并查集、线性规划、网络流、NP 问题、随机算法、哈希算法等等。
这门课的教材写的很好，证明浅显易懂，非常适合作为工具书查阅。另外，这门课只有书面作业，并且推荐用 LaTeX 编写，大家可以借此机会锻炼自己的 LaTeX 技巧。
课程资源 资源汇总 我在学习这门课中用到的所有资源和作业实现都汇总在 PKUFlyingPig/UCB-CS170 - GitHub 中。
最后更新: April 3, 2022
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diff --git a/数据结构与算法/CS61B/index.html b/数据结构与算法/CS61B/index.html
index 18d04fce..78f96065 100644
--- a/数据结构与算法/CS61B/index.html
+++ b/数据结构与算法/CS61B/index.html
@@ -1,2 +1,2 @@
- UCB CS61B: Data Structures and Algorithms - CS自学指南
CS61B: Data Structures and Algorithms 课程简介 所属大学：UC Berkeley 先修要求：CS61A 编程语言：Java 课程难度：🌟🌟🌟 预计学时：60 小时 伯克利 CS61 系列的第二门课程，注重数据结构与算法的设计，同时让学生有机会接触上千行的工程代码，通过 Java 初步领会软件工程的思想。
我上的是 2018 年春季学期的版本，该课的开课老师 Josh Hug 教授慷慨地将 autograder 开源了，大家可以通过网站公开的邀请码在 gradescope 免费加入课程，从而方便地测评自己的代码。
这门课所有的编程作业都是使用 Java 完成的。没有 Java 基础的同学也不用担心，课程会有保姆级的教程，从 IDEA（一款主流的 Java 编程环境）的配置讲起，把 Java 的核心语法与特性事无巨细地讲授，大家完全不用担心跟不上的问题。
这门课的作业质量也是绝绝子。14 个 lab 会让你自己实现课上所讲的绝大部分数据结构，10 个 Homework 会让你运用数据结构和算法解决实际问题， 另外还有 3 个 Project 更是让你有机会接触上千行的工程代码，在实战中磨练自己的 Java 能力。
课程资源 资源汇总 我在学习这门课中用到的所有资源和作业实现都汇总在 PKUFlyingPig/CS61B - GitHub 中。https://algs4.cs.princeton.edu/home/ https://tbp.berkeley.edu/courses/cs/61B/ https://www.cs.princeton.edu/courses/archive/spring15/cos226/exams/mid-s08-sol.pdf https://www.cs.princeton.edu/courses/archive/spring15/cos226/exams/mid-f08-sol.pdf https://www.cs.princeton.edu/courses/archive/spring15/cos226/exams/mid-f09-sol.pdf https://inst.eecs.berkeley.edu//~cs61b/fa13/samples/ https://www.cs.princeton.edu/courses/archive/spring15/cos226/exams/mid-f10.pdf https://www.cs.princeton.edu/courses/archive/spring15/cos226/exams/mid-f11-sol.pdf https://www.cs.princeton.edu/courses/archive/spring15/cos226/exams/mid-s12-sol.pdf https://www.cs.princeton.edu/courses/archive/spring15/cos226/exams/mid-f12-sol.pdf https://www.cs.princeton.edu/courses/archive/fall13/cos226/exams/mid-s13-sol.pdf https://inst.eecs.berkeley.edu/~cs61b/fa14/test-solutions/ https://www.youtube.com/watch?v=rciRgoiJVGY https://www.youtube.com/channel/UCqiudaLea8HNE23GBC34R5Q/videos https://www.youtube.com/channel/UCNBSbBTFx8nFahcQyZOYOgQ/videos https://www.youtube.com/channel/UCdh0Rpk6C1w1ypiEjjKh_Uw/videos https://www.youtube.com/channel/UCtic6FhXmfR0PSntIXjhwaw/videos
最后更新: April 5, 2022
CS61B: Data Structures and Algorithms 课程简介 所属大学：UC Berkeley 先修要求：CS61A 编程语言：Java 课程难度：🌟🌟🌟 预计学时：60 小时 伯克利 CS61 系列的第二门课程，注重数据结构与算法的设计，同时让学生有机会接触上千行的工程代码，通过 Java 初步领会软件工程的思想。
我上的是 2018 年春季学期的版本，该课的开课老师 Josh Hug 教授慷慨地将 autograder 开源了，大家可以通过网站公开的邀请码在 gradescope 免费加入课程，从而方便地测评自己的代码。
这门课所有的编程作业都是使用 Java 完成的。没有 Java 基础的同学也不用担心，课程会有保姆级的教程，从 IDEA（一款主流的 Java 编程环境）的配置讲起，把 Java 的核心语法与特性事无巨细地讲授，大家完全不用担心跟不上的问题。
这门课的作业质量也是绝绝子。14 个 lab 会让你自己实现课上所讲的绝大部分数据结构，10 个 Homework 会让你运用数据结构和算法解决实际问题， 另外还有 3 个 Project 更是让你有机会接触上千行的工程代码，在实战中磨练自己的 Java 能力。
课程资源 资源汇总 我在学习这门课中用到的所有资源和作业实现都汇总在 PKUFlyingPig/CS61B - GitHub 中。https://algs4.cs.princeton.edu/home/ https://tbp.berkeley.edu/courses/cs/61B/ https://www.cs.princeton.edu/courses/archive/spring15/cos226/exams/mid-s08-sol.pdf https://www.cs.princeton.edu/courses/archive/spring15/cos226/exams/mid-f08-sol.pdf https://www.cs.princeton.edu/courses/archive/spring15/cos226/exams/mid-f09-sol.pdf https://inst.eecs.berkeley.edu//~cs61b/fa13/samples/ https://www.cs.princeton.edu/courses/archive/spring15/cos226/exams/mid-f10.pdf https://www.cs.princeton.edu/courses/archive/spring15/cos226/exams/mid-f11-sol.pdf https://www.cs.princeton.edu/courses/archive/spring15/cos226/exams/mid-s12-sol.pdf https://www.cs.princeton.edu/courses/archive/spring15/cos226/exams/mid-f12-sol.pdf https://www.cs.princeton.edu/courses/archive/fall13/cos226/exams/mid-s13-sol.pdf https://inst.eecs.berkeley.edu/~cs61b/fa14/test-solutions/ https://www.youtube.com/watch?v=rciRgoiJVGY https://www.youtube.com/channel/UCqiudaLea8HNE23GBC34R5Q/videos https://www.youtube.com/channel/UCNBSbBTFx8nFahcQyZOYOgQ/videos https://www.youtube.com/channel/UCdh0Rpk6C1w1ypiEjjKh_Uw/videos https://www.youtube.com/channel/UCtic6FhXmfR0PSntIXjhwaw/videos
最后更新: April 5, 2022
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- UCB CS189: Introduction to Machine Learning - CS自学指南
