@Crypto_hedyEth: 大多数人浪费大量时间寻找优质AI资源。 这个GitHub仓库悄悄放出13本免费AI书籍。全是干货，无废话。 https://github.com/AniruddhaChattopadhyay/Books… 这里面有啥 LLM基础 → 分词…
摘要
这个GitHub仓库提供了13本免费的AI/ML书籍，涵盖LLM、强化学习、深度学习面试等多个方面。
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缓存时间: 2026/06/02 05:57
大多数人浪费大量时间寻找优质AI资源。
这个GitHub仓库悄悄放出13本免费AI书籍。全是干货，无废话。 https://github.com/AniruddhaChattopadhyay/Books…
这里面有啥
LLM基础 → 分词到安全 → 训练简单解释 → 对新手友好的深入研究
强化学习 → 基于价值的方法 → 策略梯度方法 → 实践实施小贴士
深度学习面试 → 400+精选问答 → 从CNN到Transformer → 临时复习perfect
机器学习数学 → 线性代数要点 → 微积分与概率 → 包含实际案例
OpenAI Agent指南 → 验证过的设计模式 → Agent编排技巧 → 真实世界的防护
纸笔机器学习 → 理论优先问题 → 逐步解决方案 → 不需要键盘
微调大语言模型 → 从基础到突破 → 最佳实践解析 → 应用研究挑战
多Agent强化学习 → 博弈论×学习 → 基础概念 → 前沿研究
ML系统工程 → 哈佛最新指南 → 分布式训练 → AGI规模系统
保存备用
AniruddhaChattopadhyay/Books
Source: https://github.com/AniruddhaChattopadhyay/Books
📚 AI / ML Bookshelf
Welcome to my personal reference shelf of freely shareable AI & Machine-Learning books.
I keep the PDFs here so I can grep formulas, revisit algorithms, and point friends straight to the good stuff.
Table of contents
| # | Title | Snapshot |
|---|---|---|
| 1 | Deep Learning Interviews | 400 + curated Q&As spanning CNNs, transformers, maths and system design—perfect for pre-interview rapid-fire revision. |
| 2 | Foundation of LLM.pdf | A newcomer-friendly primer on how large language models are built, trained and aligned, from tokenization to safety. |
| 3 | Reinforcement Learning – An Overview | A panoramic survey of modern RL: value-based, policy-gradient, model-based and hybrid methods, with practical tips and further reading. |
| 4 | Alg4ai.pdf | Concise Stanford-style notes covering search, constraint satisfaction, probabilistic reasoning and planning in ~150 pages. |
| 5 | Math4ml.pdf | Linear algebra, calculus and probability essentials explained for ML practitioners, loaded with intuitive worked examples. |
| 6 | OpenAI guide to building practical agents | Design patterns, orchestration tricks and guardrails for shipping real-world AI agents with the OpenAI tool-chain. |
| 7 | Pen and paper exercise in ML | A workbook of theory-first problems (with solutions) to deepen mathematical intuition—no keyboard required. |
| 8 | Matrixcookbook | A concise “cheat-sheet” of hundreds of matrix identities, derivatives, decompositions, and statistical formulas you’ll reach for whenever linear-algebra algebra gets hairy; perfect as a desktop reference to speed up proofs and ML math. |
| 9 | Finetuning guide | The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities. |
| 10 | MULTI-AGENT REINFORCEMENT LEARNING | A definitive introduction to multi-agent reinforcement learning, this book blends game theory and deep learning to offer both foundational insights and cutting-edge research—ideal for newcomers and experts alike. |
| 11 | Context Engineering | A comprehensive 150+ pages survey on context engineering |
| 12 | Linear Algebra Essence and form book | A linear algebra book that connects to concepts in AI |
| 13 | Machine Learning Systems | A comprehensive, up-to-date guide from Harvard on ML Systems Engineering — covering everything from deep learning foundations to distributed training, model optimization, and emerging AGI-scale systems. |
How to use
-
Clone the repo
git clone https://github.com/AniruddhaChattopadhyay/Books.git -
Open any PDF in your favourite reader—or preview directly on GitHub.
-
Search the folder (ripgrep, Spotlight, etc.) when you half-remember that derivation.
-
⭐ Star the repo to catch new additions whenever I find a gem.
Contributing
Have a legally distributable AI/ML book that belongs here? Open a PR with the PDF and add a two-line description to this table. No pay-walled or pirated material, please.
License & attribution
Each PDF retains its original license (usually CC-BY-NC or similar)—see inside the book for details. This README and folder structure are released under the MIT License.
All materials are publicly available under the authors’ distribution terms. If a publisher requests removal, I will comply immediately. Support the authors—buy the print editions or leave reviews if you find these texts valuable.
Happy reading & building! 🚀
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