{
  "name": "deep-learning-book",
  "description": "Study companion and working knowledge base for the Deep Learning textbook by Goodfellow, Bengio & Courville (MIT Press, 2016), free to read at deeplearningbook.org. Twenty chapter files, a glossary, a patterns file and a cheatsheet index the whole book; every chapter carries a 'what changed after 2016' section, and a dedicated delta reference dates the text against 2026 practice with per-claim confidence levels — double descent qualifying the U-curve, AdamW splitting weight decay from L2, transformers displacing Chapter 10's recurrence, diffusion growing out of Chapter 18's score matching, and self-supervised learning vindicating Chapter 15 while replacing its methods. Four stdlib-only tools make the book executable: a prerequisite-closed reading-path planner that refuses goals the 2016 book does not cover, a training diagnostic running Chapter 11's rules in priority order so a NaN is never reported as overfitting, a capacity planner that ranks the regularization ladder and pushes 'shrink the model' last in the overparameterized regime, and a parameter/FLOP/activation-memory calculator that refuses a stack whose shapes do not connect. Deliberately a companion, not a compilation: the book is copyrighted, so nothing here reproduces its text — every chapter file is original synthesis linking to the official free chapter. Use when studying or teaching this book, planning a route through it, or checking whether one of its recommendations is still current.",
  "version": "2.12.0",
  "author": {
    "name": "Alireza Rezvani",
    "url": "https://alirezarezvani.com"
  },
  "homepage": "https://github.com/alirezarezvani/claude-skills/tree/main/engineering/deep-learning-book",
  "repository": "https://github.com/alirezarezvani/claude-skills",
  "license": "MIT",
  "skills": [
    "./skills/deep-learning-book"
  ]
}
