Free AI library

James, Witten, Hastie, Tibshirani

The standard modern introduction to statistical learning: free PDF, Python and R labs, and a gentle path from regression to deep learning.

D2L.ai

An open, interactive deep learning textbook with math, runnable PyTorch/JAX code and notebooks kept current by the community.

Goodfellow, Bengio, Courville

The theory-heavy reference for deep learning. Read it after a practical book, when you want the foundations in depth.

Simon J. D. Prince

A modern 2024 textbook that balances intuition and rigor, with a free PDF and exercises for every chapter.

Andrew Ng

A short, free book about organizing ML projects: metrics, train/test splits, error analysis and what to fix next.

Deisenroth, Faisal, Ong

A free textbook that fills the linear algebra, probability and calculus gaps you need before serious ML work.

Sebastian Raschka

Build a GPT step by step in PyTorch, from tokenization and attention to training and fine-tuning. The code is the lesson.

Shubham Saboo

A large collection of runnable LLM apps: RAG, agents, multimodal demos and production patterns you can clone and adapt.

Maxime Labonne

A roadmap plus Colab notebooks for learning LLMs, from fundamentals and fine-tuning to deployment.

DAIR.AI

A practical, example-driven guide to prompting techniques, from zero-shot basics to chain-of-thought and agents.

Andrej Karpathy

Karpathy's minimal GPT training repo: small enough to read end to end, real enough to train on your own text.