claude

deep-learning-book

Study companion for the Deep Learning textbook by Goodfellow, Bengio & Courville (MIT Press, 2016), free to read at deeplearningbook.org. Twenty chapter files, a glossary, patterns and a cheatsheet index the whole book, and a delta reference dates it 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. Four stdlib tools make it executable — a prerequisite-closed reading-path planner that refuses goals the 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 'shrink the model' last when overparameterized, and a parameter/FLOP/activation-memory calculator that refuses a stack whose shapes do not connect. A companion, not a compilation: the book is copyrighted, so nothing here reproduces its text.

29,725 on GitHubby Alireza Rezvaniv2.14.6MITupdated yesterdaydevelopment
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Plugin deep-learning-book from marketplace claude-code-skills in alirezarezvani/claude-skills. Commands read from the repo's manifest — today.

What it installs

Skills (1)

  • deep-learning-book

    Use when studying or applying Goodfellow, Bengio and Courville's Deep Learning, or asking if its advice still holds. Indexes all 20 chapters with a 2016-to-2026 delta, plans reading paths with reading_path_planner.py and diagnoses training failures with training_diagnostics.py.

Slash commands (3)

  • /deep-learning-book:cs-deep-learning

    /cs-deep-learning — Study companion for the Deep Learning textbook (Goodfellow, Bengio & Courville, 2016). Answers chapter questions from a compiled knowledge base, always dating the answer against 2026 practice, and routes to the reading-path planner or the training diagnostic. Points at the free o

  • /deep-learning-book:cs-dl-diagnose

    /cs-dl-diagnose — Diagnose a training run against Chapter 11's decision tree: read training error before deciding anything, and never diagnose a NaN as overfitting. Returns a ranked cause, the specific next action, and the chapter that justifies it.

  • /deep-learning-book:cs-dl-reading-path

    /cs-dl-reading-path — Build a prerequisite-closed reading path through the Deep Learning book from a goal, a background and the hours you actually have. Refuses to route a goal the 2016 book does not cover, and names what covers it instead.

Subagents (1)

  • cs-deep-learning-tutor

    Study companion for the Deep Learning textbook (Goodfellow, Bengio & Courville, 2016). Plans a prerequisite-closed reading path, answers chapter questions from the compiled knowledge base, diagnoses training runs against Chapter 11's decision tree, and flags every place the 2016 text has been supers

View source on GitHub alirezarezvani.medium.com

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