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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.
+

docs/大语言与深度生成模型/CS11667.md

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+# Carnegie Mellon University CS 11-667: Large Language Models Methods and Applications
+
+## 课程简介
+
+- **所属大学**: 卡内基梅隆大学 (Carnegie Mellon University)
+- **先修要求**: 
+  - 机器学习的基本知识（相当于课程 10-301/10-601）
+  - 自然语言处理的相关概念（相当于课程 11-411/11-611）
+  - 熟练掌握 Python 编程，了解 PyTorch 或类似的深度学习框架。
+- **课程难度**: 🌟🌟🌟🌟🌟🌟
+- **课程网站**: [大型语言模型的方法与应用](https://cmu-llms.org/)
+
+《大型语言模型的方法与应用 (11-667)》是一门研究生课程，提供关于大型语言模型（LLMs）最新进展的全面概述。本课程在 **卡内基梅隆大学** 开设，授课时间为 **每周二和周四下午 2:00 - 3:20**，地点为 **Baker Hall A51**。
+
+课程涵盖了语言模型的基础知识、网络架构、训练、推理与评估，并深入探讨了解释性、对齐、涌现能力、扩展定律，以及高效训练和部署的最新技术。学生还将学习 LLM 的部署风险及挑战，并探索其在新应用中的潜力。
+
+### 课程内容：
+- 语言模型的基础
+- 网络架构、训练与评估
+- 涌现能力与扩展定律
+- 在自然语言处理及其他领域的应用
+- 隐私保护、对齐与鲁棒性
+- 部署中的挑战与伦理考虑
+
+### 学习目标：
+完成课程后，学生将能够：
+- 比较并分析不同的 LLM 模型及其应用场景。
+- 使用 PyTorch 实现和训练语言模型。
+- 使用开源工具微调并进行预训练模型的推理。
+- 理解 LLM 在下游任务中的应用，并评估训练时的决策对这些任务的影响。
+- 阅读并理解 LLM 领域的学术论文，掌握相关术语（如扩展定律、RLHF、提示工程等）。
+- 设计创新方法，将现有大型语言模型应用于新场景。
+
+

docs/大语言与深度生成模型/CS194 - Copy (2).md

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+# UC Berkeley CS 194/294-267： Understanding Large Language Models: Foundations and Safety
+
+## 课程简介
+
+- **所属大学**：UC Berkeley
+- **先修要求**：CS 182/282A 深度神经网络课程或等效课程，并具有深度学习的实际操作经验。
+- **课程难度**：🌟🌟🌟🌟🌟🌟
+- **课程网站**：<http://rdi.berkeley.edu/understanding_llms/s24>
+- **课程视频**：<https://www.youtube.com/playlist?list=PLJ66BAXN6D8H_gRQJGjmbnS5qCWoxJNfe>
+
+《理解大型语言模型：基础与安全》是 UC Berkeley 在 2024 年春季开设的一门课程，由 **Dawn Song 教授** 和 **Dan Hendrycks** 联合授课，助教为 **Yu Gai**。本课程重点探讨大型语言模型（如 ChatGPT）的基础原理、可解释性、扩展定律以及相关风险。
+
+课程内容包括：
+
+- 大型语言模型的基础
+- 可解释性
+- 扩展定律
+- 对抗鲁棒性
+- 人工智能对齐与治理
+- 隐私、水印与木马
+- 代理性、涌现性、推理与数学
+- 评估与基准测试
+

docs/大语言与深度生成模型/CS194.en - Copy (2).md

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+# UC Berkeley CS 194/294-267: Understanding Large Language Models: Foundations and Safety
+
+## Course Introduction
+
+- **University**: UC Berkeley
+- **Prerequisites**: CS 182/282A Deep Neural Networks or equivalent, with hands-on deep learning experience.
+- **Course Difficulty**: 🌟🌟🌟🌟🌟🌟
+- **Course Website**: [Understanding Large Language Models](http://rdi.berkeley.edu/understanding_llms/s24)
+- **Lecture Video**：<https://www.youtube.com/playlist?list=PLJ66BAXN6D8H_gRQJGjmbnS5qCWoxJNfe>
+
+"Understanding Large Language Models: Foundations and Safety" is a Spring 2024 course at UC Berkeley, co-taught by **Professor Dawn Song** and **Dan Hendrycks**, with **Yu Gai** as the GSI. This course explores the foundational principles, interpretability, scaling laws, and risks associated with large language models (LLMs) such as ChatGPT.
+
+The course provides a rigorous introduction to LLMs, discussing their emergence, limitations, and potential risks, as well as methods for safer and more beneficial applications. Topics covered include:
+
+- Foundations of LLMs
+- Interpretability
+- Scaling laws
+- Adversarial robustness
+- AI alignment and governance
+- Privacy, watermarking, and Trojans
+- Agency, emergence, reasoning, and mathematics
+- Evaluation and benchmarking
+
+

docs/大语言与深度生成模型/CS194.en.md

@@ -6,6 +6,7 @@
 - **Prerequisites**: CS 182/282A Deep Neural Networks or equivalent, with hands-on deep learning experience.
 - **Course Difficulty**: 🌟🌟🌟🌟🌟🌟
 - **Course Website**: [Understanding Large Language Models](http://rdi.berkeley.edu/understanding_llms/s24)
+- **Lecture Video**：<https://www.youtube.com/playlist?list=PLJ66BAXN6D8H_gRQJGjmbnS5qCWoxJNfe>
 
 "Understanding Large Language Models: Foundations and Safety" is a Spring 2024 course at UC Berkeley, co-taught by **Professor Dawn Song** and **Dan Hendrycks**, with **Yu Gai** as the GSI. This course explores the foundational principles, interpretability, scaling laws, and risks associated with large language models (LLMs) such as ChatGPT.
 

docs/大语言与深度生成模型/CS194.md

@@ -6,6 +6,7 @@
 - **先修要求**：CS 182/282A 深度神经网络课程或等效课程，并具有深度学习的实际操作经验。
 - **课程难度**：🌟🌟🌟🌟🌟🌟
 - **课程网站**：<http://rdi.berkeley.edu/understanding_llms/s24>
+- **课程视频**：<https://www.youtube.com/playlist?list=PLJ66BAXN6D8H_gRQJGjmbnS5qCWoxJNfe>
 
 《理解大型语言模型：基础与安全》是 UC Berkeley 在 2024 年春季开设的一门课程，由 **Dawn Song 教授** 和 **Dan Hendrycks** 联合授课，助教为 **Yu Gai**。本课程重点探讨大型语言模型（如 ChatGPT）的基础原理、可解释性、扩展定律以及相关风险。