claude-skills/engineering/deep-learning-book/README.md

deep-learning-book — study companion for Deep Learning (Goodfellow, Bengio & Courville)

A navigable, executable, date-stamped companion to the 2016 MIT Press textbook that is free to read at deeplearningbook.org.

Twenty chapter files, a glossary, a patterns file, a cheatsheet, four references and four deterministic tools — plus the thing a static compilation cannot give you: a 2016 → 2026 delta layer that says, per chapter, what still holds, what was superseded, and what is now purely historical.

Why this is a companion and not a compiled skill

This repository ships engineering/book-to-skill, which compiles a document on disk into a knowledge-base skill. It was deliberately not used here, for three reasons its own rights gate makes binding:

1. The book is copyrighted (MIT Press, 2016). Free to read ≠ free to redistribute.
2. deeplearningbook.org states that its HTML-only presentation is a deliberate friction against copying, required by the authors' contract, and that PDF distribution is not permitted.
3. This repository is public, and book-to-skill's rights gate refuses a shareable package without public-domain, open-license, internal-docs or author-permission. None applies. (Its hard rule 1 also forbids scraping a book from the web, so the pipeline could not have run against a URL in any case.)

So this skill contains no passages, paragraphs, figures, or per-paragraph paraphrase. Every chapter file is original synthesis — what the chapter establishes, how to use it, where it has aged — with a link to the official free chapter. The organizing structure is the book's published table of contents, which is factual metadata. Full reasoning in references/rights_and_use.md.

The rule this sets for the repository: when a user asks to convert a copyrighted work into a shareable skill, build a companion that indexes and updates the source, not a compilation that reproduces it. Compile only when the rights gate clears, and keep the output local when it does not.

What is in it

skills/deep-learning-book/
├── SKILL.md              core frameworks + chapter index + topic index (~1.9k tokens, resident)
├── chapters/ch01..ch20   one file per chapter, each with "What changed after 2016"
├── glossary.md           every key term → its chapter
├── patterns.md           techniques as instruments, with trade-offs
├── cheatsheet.md         decision tables: loss choice, fit verdict, optimizer defaults
├── references/           delta layer · prerequisite map · study method · rights
├── scripts/              4 stdlib tools
└── assets/               layer spec · chapter worksheet · study log

The tools

Tool	Does	Refuses
reading_path_planner.py	Goal + background + hours → prerequisite-closed, ordered path with an hour budget	A goal outside the book (exit 3, names what covers it); an unroutable goal (exit 4, prints the questions)
training_diagnostics.py	Measurements → ranked cause + next action + chapter, rules in priority order	Diagnosing with no instruments (exit 4). Never reports a NaN as overfitting
capacity_planner.py	Gap + params-per-example → ordered regularization ladder	A validation error materially below training error (exit 4 — leaky split)
model_arithmetic.py	Layer stack → parameters, FLOPs, activation memory per example	A stack whose shapes do not connect (exit 5); an unknown layer type (exit 4)

All four are standard-library only, support --help / --sample / --output json, make no network calls, and load no frameworks.

The delta layer

The book was published in 2016; Attention Is All You Need appeared in 2017. Five corrections carry most of the weight, each cited and confidence-rated in references/book_to_2026_delta.md:

1. Double descent qualifies Chapter 5's U-shaped capacity curve — "shrink the model when it overfits" is no longer the only correct move, and capacity_planner.py encodes the correction.
2. AdamW: weight decay and L2 are not equivalent under an adaptive optimizer; Chapter 7 treats them as interchangeable.
3. Transformers displaced Chapter 10's recurrence — keep its gradient-flow analysis, drop its architecture recommendation. (State-space models made that analysis live again.)
4. Diffusion grew directly out of Chapter 14's denoising autoencoders and Chapter 18's score matching, and displaced Chapter 20's model list.
5. Self-supervised learning vindicated Chapter 15's bet while replacing every method it names; unsupervised disentanglement was proven impossible without inductive bias.

The general rule the file states: the conflict is almost always in the recommendation, not the analysis. Keep the diagnosis, replace the prescription.

Use it

S=engineering/deep-learning-book/skills/deep-learning-book/scripts

python3 $S/reading_path_planner.py --goal "train and debug convnets" --background applied --hours-per-week 5
python3 $S/training_diagnostics.py --train-loss 0.02 --val-loss 1.9 --tiny-subset-fits yes
python3 $S/capacity_planner.py --params 12000000 --train-examples 50000 --train-error 0.01 --val-error 0.22
python3 $S/model_arithmetic.py --spec skills/deep-learning-book/assets/example_layer_spec.json

Slash commands: /cs:deep-learning (navigate and date the answer), /cs:dl-reading-path, /cs:dl-diagnose. Agent: cs-deep-learning-tutor.

Distinct from

• engineering/book-to-skill — the converter. This is what you build when its rights gate says no.
• engineering-team/senior-ml-engineer — production MLOps, deployment, serving. This is the theory underneath.
• engineering/llm-cost-optimizer — LLM economics, which the book predates entirely.
• teach / learn skills — general study workflows. This is one specific text, with its dependency graph and its expiry dates.

Scope

The twenty chapters and the delta between them and 2026 practice. Not covered, because the book does not cover them: reinforcement learning beyond passing mention, LLM training infrastructure, RLHF/DPO, agentic systems, MLOps tooling, fairness and safety evaluation. The skill is built to say so rather than improvise.

Attribution

Deep Learning, Ian Goodfellow, Yoshua Bengio and Aaron Courville, MIT Press, 2016 — https://www.deeplearningbook.org/. All rights in the book remain with its authors and publisher. This companion is an independent work: no text, figures, or exercises from the book are reproduced here, and it is not endorsed by or affiliated with the authors or MIT Press. The companion's own content is MIT-licensed as part of this repository.