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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
15 lines
1.8 KiB
JSON
15 lines
1.8 KiB
JSON
{
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"name": "deep-learning-book",
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"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.",
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"version": "2.12.0",
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"author": {
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"name": "Alireza Rezvani",
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"url": "https://alirezarezvani.com"
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},
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"homepage": "https://github.com/alirezarezvani/claude-skills/tree/main/engineering/deep-learning-book",
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"repository": "https://github.com/alirezarezvani/claude-skills",
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"license": "MIT",
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"skills": [
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"./skills/deep-learning-book"
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]
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}
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