Agent skill · alirezarezvani
deep-learning-book
Use when studying or applying Goodfellow, Bengio and Courville's Deep Learning, or asking if its advice still holds. Indexes all 20 chapters with a 2016-to-2026 delta, plans reading paths with reading_path_planner.py and diagnoses training failures with training_diagnostics.py.
What it needs
About 6k tokens when loaded.
What this skill does
Deep Learning — Study Companion 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. How to Use This Skill 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 position on material published after it. --- Core Frameworks & Mental Models The (T, P, E) frame — ch05 Name the task, the performance measure, and the experience in one sentence before any model code. Most failed projects failed at P: an unstated metric, or a proxy whose relationship to the real objective was never checked. Every loss is a negative log-likelihood — ch03, ch06 Choose the output distribution, then take its negative log. Gaussian → MSE, Bernoulli → binary cross-entropy, categorical → cross-entropy, Laplace → MAE. "Which loss?" is always the question "which distribution?" in disguise. Modern contrastive and preference objectives sit outside this frame — a real limit of the book, not a gap in your understanding. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills alirezarezvani/deep-learning-book