Exact public version
deep-learning-book v1
Study companion and working knowledge base for the Deep Learning textbook by Goodfellow, Bengio & Courville (MIT Press, 2016), read free at deeplearningbook.org. Indexes all 20 chapters, carries a 2016-to-2026 delta layer naming what the book got right, what was superseded (transformers, AdamW, diffusion, double descent) and what still holds, and ships four deterministic tools: a prerequisite-aware reading-path planner, a training-failure diagnostic, a capacity-and-regularization planner, and a parameter/FLOP/activation-memory calculator. Use when studying or teaching this book, planning a route through it, deciding whether a chapter's advice is still current, or translating its math into a training decision. It points at the official chapters — it never reproduces them.
af68f6fe90e7f4f131f347a87736eafd91eb52471011d09804566c2b35a94ffaPackage contents
Manifest
Study companion and working knowledge base for the Deep Learning textbook by Goodfellow, Bengio & Courville (MIT Press, 2016), read free at deeplearningbook.org. Indexes all 20 chapters, carries a 2016-to-2026 delta layer naming what the book got right, what was superseded (transformers, AdamW, diffusion, double descent) and what still holds, and ships four deterministic tools: a prerequisite-aware reading-path planner, a training-failure diagnostic, a capacity-and-regularization planner, and a parameter/FLOP/activation-memory calculator. Use when studying or teaching this book, planning a route through it, deciding whether a chapter's advice is still current, or translating its math into a training decision. It points at the official chapters — it never reproduces them.
46d8555d807febe39ff4eaf45a610b7d9c18af401419ca3ca38ae24c10bf8c29- SKILL.md11,601 bytes
- assets/chapter_worksheet.md2,348 bytes
- assets/example_layer_spec.json1,242 bytes
- assets/study_log_template.md2,069 bytes
- chapters/ch01-introduction.md3,176 bytes
- chapters/ch02-linear-algebra.md3,174 bytes
- chapters/ch03-probability-information-theory.md3,704 bytes
- chapters/ch04-numerical-computation.md3,104 bytes
- chapters/ch05-machine-learning-basics.md3,610 bytes
- chapters/ch06-deep-feedforward-networks.md3,267 bytes
- chapters/ch07-regularization.md3,644 bytes
- chapters/ch08-optimization.md3,560 bytes
- chapters/ch09-convolutional-networks.md3,485 bytes
- chapters/ch10-sequence-modeling.md3,627 bytes
- chapters/ch11-practical-methodology.md3,277 bytes
- chapters/ch12-applications.md3,122 bytes
- chapters/ch13-linear-factor-models.md3,207 bytes
- chapters/ch14-autoencoders.md3,368 bytes
- chapters/ch15-representation-learning.md3,539 bytes
- chapters/ch16-structured-probabilistic-models.md3,272 bytes
- chapters/ch17-monte-carlo-methods.md3,024 bytes
- chapters/ch18-partition-function.md3,560 bytes
- chapters/ch19-approximate-inference.md3,363 bytes
- chapters/ch20-deep-generative-models.md3,759 bytes
- cheatsheet.md3,250 bytes
- glossary.md6,032 bytes
- patterns.md4,803 bytes
- references/book_to_2026_delta.md6,536 bytes
- references/prerequisite_map.md5,007 bytes
- references/rights_and_use.md4,543 bytes
- references/study_method_canon.md5,473 bytes
- scripts/capacity_planner.py14,682 bytes
- scripts/model_arithmetic.py17,282 bytes
- scripts/reading_path_planner.py20,125 bytes
- scripts/training_diagnostics.py14,454 bytes