Teaches the math behind modern deep learning across 21 chapters, from shallow nets to transformers and diffusion models. Each idea is explained in words, then in equations, then visually. Full PDF, slides, and Python notebooks are free.
Official code companion to the O'Reilly book by Jay Alammar and Maarten Grootendorst: 12 chapters of runnable notebooks on tokens, embeddings, Transformers, text classification, clustering, prompt engineering, semantic search, RAG, and fine-tuning.
Publishes a structured open textbook on large language model foundations, covering language modeling, LLM architectures, prompt engineering, PEFT, model editing, and RAG.
Companion resources for Chip Huyen's AI Engineering book: chapter summaries, study notes, prompt examples, case studies, and a few analysis scripts. Focuses on engineering practices for adapting foundation models to production rather than step-by-step code tutorials.
An open, intuition-first textbook that teaches the maths, computing, and practical foundations needed for AI engineering. Organized into focused chapters (vectors, matrices, calculus, ML, NLP, CV, GPU/Inference, ML systems) with code-first explanations and interview-ready emphasis.