modify llm course

docs/大语言与深度生成模型/CS194.en.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)
+
+"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.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>
+
+《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/CS50.en.md

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-# Harvard's CS50: Introduction to AI with Python
-
-## Descriptions
-
-- Offered by: Harvard University
-- Prerequisites: Basic knowledge of probability theory and Python
-- Programming Languages: Python
-- Difficulty: 🌟🌟🌟
-- Class Hour: 30
-
-A very basic introductory AI course, what makes it stand out is the 12 well-designed programming assignments, all of which will use the learned knowledge to implement a simple game AI, such as using reinforcement learning to play Nim game, using max-min search with alpha-beta pruning to sweep mines, and so on. It's perfect for newbies to get started or bigwigs to relax.
-
-## Course Resources
-
-- Course Website: <https://cs50.harvard.edu/ai/2020/>
-- Recordings: <https://cs50.harvard.edu/ai/2020/>
-- Textbooks: No textbook is needed in this course.
-- Assignments: <https://cs50.harvard.edu/ai/2020/> with 12 programming labs of high quality mentioned above.
-
-## Personal Resources
-
-All the resources and assignments used by @PKUFlyingPig in this course are maintained in [PKUFlyingPig/cs50_ai - GitHub](https://github.com/PKUFlyingPig/cs50_ai).

docs/大语言与深度生成模型/CS50.md

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-# CS324 - Large Language Models
-
-## 课程简介
-
-- 所属大学：Stanford
-- 先修要求：深度学习
-- 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
-
-- 课程网站：<https://stanford-cs324.github.io/winter2022/>
-
-