add cmu11-667

docs/深度生成模型/大语言模型/CMU11-667.en.md

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+# Carnegie Mellon University CS 11-667: Large Language Models Methods and Applications
+
+## Course Introduction
+
+- **University**: Carnegie Mellon University
+- **Prerequisites**: 
+  - Basic understanding of machine learning (equivalent to 10-301/10-601)
+  - Familiarity with natural language processing concepts (equivalent to 11-411/11-611)
+  - Fluency in Python and knowledge of PyTorch or similar deep learning frameworks.
+- **Course Difficulty**: 🌟🌟🌟🌟🌟🌟
+- **Course Website**: [Large Language Models Methods and Applications](https://cmu-llms.org/)
+
+"Large Language Models Methods and Applications (11-667)" is a graduate-level course that provides a comprehensive overview of the state of large language models (LLMs). 
+
+This course explores the fundamentals and cutting-edge developments of LLMs, beginning with the basics of language model architectures, training, inference, and evaluation. It then delves into interpretability, alignment, emergent capabilities, scaling laws, and efficient training and deployment techniques. Students also learn about the risks and challenges in deploying LLMs and explore novel applications.
+
+### Topics Covered:
+- Foundations of language models
+- Network architectures, training, and evaluation
+- Emergent capabilities and scaling laws
+- Applications in natural language processing and beyond
+- Privacy, alignment, and robustness in LLMs
+- Challenges in deployment and ethical considerations
+
+### Learning Goals:
+By the end of the course, students will be able to:
+- Compare and analyze different LLMs and their use cases.
+- Train and implement neural language models in PyTorch.
+- Use open-source tools to fine-tune and perform inference with pre-trained LLMs.
+- Understand and apply LLMs to downstream tasks and identify the impact of pre-training decisions.
+- Read and comprehend research papers on LLMs, understanding terms like scaling laws, RLHF, and prompt engineering.
+- Develop innovative approaches for leveraging LLMs in new applications.

mkdocs.yml

@@ -285,6 +285,7 @@ nav:
       - "学习路线图": "深度生成模型/roadmap.md"
       - "大语言模型":
         - "CMU 11868: Large Language Model System": "深度生成模型/大语言模型/CMU11-868.md"
+        - "CMU 11667: Large Language Models: Methods and Applications": "深度生成模型/大语言模型/CMU11-667.md"
   - 机器学习进阶:
       - "学习路线图": "机器学习进阶/roadmap.md"
       - "CMU 10-708: Probabilistic Graphical Models": "机器学习进阶/CMU10-708.md"