deep-learning-book

Topics in the source

• Deep Learning — Study Companion
• How to Use This Skill
• Core Frameworks & Mental Models
• The (T, P, E) frame — ch05
• Every loss is a negative log-likelihood — ch03, ch06
• KL asymmetry decides your failure mode — ch03, ch19, ch20
• Train-error-first triage — ch11, ch05
• Capacity, the gap, and the U-curve's caveat — ch05, ch07

Source book: Deep Learning, Ian Goodfellow, Yoshua Bengio & Aaron Courville (MIT Press, 2016) · 20 chapters, 3 parts · read free at deeplearningbook.org · companion compiled 2026-08-25. This is a companion, not a copy. The book is copyrighted, and its site states that the HTML-only format exists to discourage copying under the authors' MIT Press contract. Nothing here reproduces its text. Every chapter file is original synthesis — what the chapter establishes, how to use it, where it has aged — plus a link to the official chapter. Read the book at the link; use this to navigate it, keep it current, and turn it into decisions. See references/rightsanduse.md. No argument — load the core frameworks below. A topic — ask about regularization, saddle points, partition function; resolved through the Topic Index, then that chapter file is read before answering. chNN — load that chapter's file. "is this still true?" — the 2016→2026 delta layer, in every chapter file and in references/bookto2026delta.md. "where do I start?" — run scripts/readingpathplanner.py. When asked about something outside these 20 chapters, say so and route to the delta reference rather than improvising the book's posi

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