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Requested as "convert deeplearningbook.org into a skill". Built as a companion rather than a compilation, because the repo's own rights gate forbids the latter for a public plugin. Why not book-to-skill: its emitter refuses a shareable package without public-domain / open-license / internal-docs / author-permission, none of which applies to an MIT Press title whose site states its HTML-only format exists as a friction against copying under the authors' contract. Its rights reference lists publishing a compiled skill of a copyrighted book to a public marketplace under "Do not", and its hard rule 1 forbids scraping a book from the web, so the pipeline could not have run against a URL either. What shipped instead: the compiled-skill shape (master SKILL.md ~2.0k tokens with chapter and topic indexes, chapters/ch01..ch20, glossary, patterns, cheatsheet) filled with original synthesis and linking to the official free chapters. No passages, figures, or per-paragraph paraphrase. Passes book-to-skill's own book_skill_validator.py clean, with every file inside token_budget_estimator's caps. The differentiator is the delta layer. A compilation freezes a source at its publication date; this one dates it. Every chapter carries "What changed after 2016", and references/book_to_2026_delta.md gives five corrections with primary citations and per-claim confidence levels: double descent qualifying Ch 5's U-curve, AdamW splitting weight decay from L2, transformers displacing Ch 10's recurrence, diffusion growing out of Ch 18's score matching, and self-supervised learning vindicating Ch 15 while replacing its methods. Two claims are marked contested rather than propagated, two named as folklore. Four stdlib-only tools, each with a real refusal: - reading_path_planner.py — prerequisite closure over the book's actual dependency graph; exit 3 for a goal the book does not cover, exit 4 with forcing questions - training_diagnostics.py — Ch 11's rules in priority order, so a NaN is never reported as overfitting; exit 4 rather than diagnosing with no instruments - capacity_planner.py — regularization ladder in cost order, "shrink the model" ranked last in the overparameterized regime; exit 4 on a val-below-train split - model_arithmetic.py — params/FLOPs/activation memory for conv, linear, MHA and LSTM/GRU stacks; exit 5 naming the layer whose shapes do not connect Also: 4 references citing 7-8 sources each, 3 assets, cs-deep-learning-tutor agent, /cs:deep-learning + /cs:dl-reading-path + /cs:dl-diagnose. Counters: skills 386 to 387, tools 723 to 727, refs 838 to 842, agents 116 to 117, commands 146 to 149, plugins 97 to 98 (verified by derive_counters.py --check). Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01BswsZp5zrJWFAGU6KWNA1s
118 lines
6.8 KiB
Markdown
118 lines
6.8 KiB
Markdown
# deep-learning-book — study companion for *Deep Learning* (Goodfellow, Bengio & Courville)
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A navigable, executable, **date-stamped** companion to the 2016 MIT Press textbook that is free
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to read at [deeplearningbook.org](https://www.deeplearningbook.org/).
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Twenty chapter files, a glossary, a patterns file, a cheatsheet, four references and four
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deterministic tools — plus the thing a static compilation cannot give you: a **2016 → 2026 delta
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layer** that says, per chapter, what still holds, what was superseded, and what is now purely
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historical.
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## Why this is a companion and not a compiled skill
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This repository ships [`engineering/book-to-skill`](../book-to-skill/), which compiles a document
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on disk into a knowledge-base skill. It was deliberately **not** used here, for three reasons its
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own rights gate makes binding:
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1. The book is copyrighted (MIT Press, 2016). Free to read ≠ free to redistribute.
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2. deeplearningbook.org states that its HTML-only presentation is a deliberate friction against
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copying, required by the authors' contract, and that PDF distribution is not permitted.
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3. This repository is public, and `book-to-skill`'s rights gate refuses a `shareable` package
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without `public-domain`, `open-license`, `internal-docs` or `author-permission`. None applies.
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(Its hard rule 1 also forbids scraping a book from the web, so the pipeline could not have run
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against a URL in any case.)
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So this skill contains **no passages, paragraphs, figures, or per-paragraph paraphrase**. Every
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chapter file is original synthesis — what the chapter establishes, how to use it, where it has
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aged — with a link to the official free chapter. The organizing structure is the book's published
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table of contents, which is factual metadata. Full reasoning in
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[`references/rights_and_use.md`](skills/deep-learning-book/references/rights_and_use.md).
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**The rule this sets for the repository:** when a user asks to convert a copyrighted work into a
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shareable skill, build a companion that indexes and updates the source, not a compilation that
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reproduces it. Compile only when the rights gate clears, and keep the output local when it does not.
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## What is in it
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```
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skills/deep-learning-book/
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├── SKILL.md core frameworks + chapter index + topic index (~1.9k tokens, resident)
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├── chapters/ch01..ch20 one file per chapter, each with "What changed after 2016"
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├── glossary.md every key term → its chapter
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├── patterns.md techniques as instruments, with trade-offs
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├── cheatsheet.md decision tables: loss choice, fit verdict, optimizer defaults
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├── references/ delta layer · prerequisite map · study method · rights
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├── scripts/ 4 stdlib tools
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└── assets/ layer spec · chapter worksheet · study log
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```
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## The tools
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| Tool | Does | Refuses |
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|---|---|---|
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| `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) |
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| `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 |
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| `capacity_planner.py` | Gap + params-per-example → ordered regularization ladder | A validation error materially below training error (exit 4 — leaky split) |
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| `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) |
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All four are standard-library only, support `--help` / `--sample` / `--output json`, make no
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network calls, and load no frameworks.
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## The delta layer
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The book was published in 2016; *Attention Is All You Need* appeared in 2017. Five corrections
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carry most of the weight, each cited and confidence-rated in
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[`references/book_to_2026_delta.md`](skills/deep-learning-book/references/book_to_2026_delta.md):
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1. **Double descent** qualifies Chapter 5's U-shaped capacity curve — "shrink the model when it
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overfits" is no longer the only correct move, and `capacity_planner.py` encodes the correction.
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2. **AdamW**: weight decay and L2 are not equivalent under an adaptive optimizer; Chapter 7 treats
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them as interchangeable.
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3. **Transformers** displaced Chapter 10's recurrence — keep its gradient-flow analysis, drop its
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architecture recommendation. (State-space models made that analysis live again.)
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4. **Diffusion** grew directly out of Chapter 14's denoising autoencoders and Chapter 18's score
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matching, and displaced Chapter 20's model list.
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5. **Self-supervised learning** vindicated Chapter 15's bet while replacing every method it names;
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unsupervised disentanglement was proven impossible without inductive bias.
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The general rule the file states: the conflict is almost always in the **recommendation**, not the
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**analysis**. Keep the diagnosis, replace the prescription.
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## Use it
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```bash
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S=engineering/deep-learning-book/skills/deep-learning-book/scripts
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python3 $S/reading_path_planner.py --goal "train and debug convnets" --background applied --hours-per-week 5
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python3 $S/training_diagnostics.py --train-loss 0.02 --val-loss 1.9 --tiny-subset-fits yes
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python3 $S/capacity_planner.py --params 12000000 --train-examples 50000 --train-error 0.01 --val-error 0.22
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python3 $S/model_arithmetic.py --spec skills/deep-learning-book/assets/example_layer_spec.json
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```
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Slash commands: `/cs:deep-learning` (navigate and date the answer), `/cs:dl-reading-path`,
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`/cs:dl-diagnose`. Agent: `cs-deep-learning-tutor`.
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## Distinct from
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- **`engineering/book-to-skill`** — the converter. This is what you build when its rights gate
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says no.
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- **`engineering-team/senior-ml-engineer`** — production MLOps, deployment, serving. This is the
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theory underneath.
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- **`engineering/llm-cost-optimizer`** — LLM economics, which the book predates entirely.
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- **`teach` / `learn` skills** — general study workflows. This is one specific text, with its
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dependency graph and its expiry dates.
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## Scope
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The twenty chapters and the delta between them and 2026 practice. **Not covered**, because the
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book does not cover them: reinforcement learning beyond passing mention, LLM training
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infrastructure, RLHF/DPO, agentic systems, MLOps tooling, fairness and safety evaluation. The
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skill is built to say so rather than improvise.
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## Attribution
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*Deep Learning*, Ian Goodfellow, Yoshua Bengio and Aaron Courville, MIT Press, 2016 —
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https://www.deeplearningbook.org/. All rights in the book remain with its authors and publisher.
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This companion is an independent work: no text, figures, or exercises from the book are
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reproduced here, and it is not endorsed by or affiliated with the authors or MIT Press. The
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companion's own content is MIT-licensed as part of this repository.
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