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/rights_and_use.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/book_to_2026_delta.md.
• "where do I start?" — run scripts/reading_path_planner.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.

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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.

KL asymmetry decides your failure mode — ch03, ch19, ch20

D(p‖q) ≠ D(q‖p). Forward KL is mode-covering (blurry averages); reverse KL is mode-seeking (sharp but partial). This single fact predicts VAE blur, GAN mode collapse, and the characteristic over-confidence of mean-field variational posteriors.

Train-error-first triage — ch11, ch05

High training error → capacity or optimization is the bottleneck; more data will not help. Low training error with a large validation gap → data or regularization. This is the highest-value heuristic in the book. scripts/training_diagnostics.py runs it.

Capacity, the gap, and the U-curve's caveat — ch05, ch07

Regularization trades variance for bias. But the classical U-shaped capacity curve is incomplete: past the interpolation threshold, test error can fall again (double descent, 2019–2020, post-dating the book). Practical consequence: when a large model overfits, try more data, more regularization or longer training before shrinking it.

Architecture is a prior, not a trick — ch09, ch10, ch15

Convolution asserts translation equivariance and locality. Recurrence asserts that the past compresses into a state. A distributed representation asserts that factors combine combinatorially. When the assertion is false, the architecture cannot be rescued by tuning — and when it is true, it beats capacity. This is also why Vision Transformers need more data than ConvNets: they discard the prior and buy it back with examples.

Depth's real cost is gradient flow and activation memory — ch06, ch08, ch10

Backprop is the chain rule scheduled well: one forward-pass-equivalent of compute, and memory proportional to stored activations. Depth fails through vanishing/exploding gradients and ill-conditioning, which is why residual connections, normalization and clipping exist.

The partition function organizes Part III — ch16, ch17, ch18, ch19

For undirected models, the likelihood gradient needs samples from the model itself. Four escape routes: sample it (CD/PCD), sidestep it algebraically (pseudolikelihood, score matching), learn around it (NCE), or estimate it for evaluation (AIS). Score matching's descendants are today's diffusion models — which is why Part III repays reading even though its models did not survive.

Diagnose before you redesign — ch04, ch08, ch11

Gradient norm exploding → clip. Norm large but loss flat → ill-conditioning. Norm near zero with high loss → saturation or dead units. NaN → numerics first. Change one thing per experiment.

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Chapter Index

#	Title	Key content
ch01	Introduction	representation learning, depth as composition, curse of dimensionality
ch02	Linear Algebra	norms, SVD, eigendecomposition, conditioning, PCA
ch03	Probability & Information Theory	distributions, entropy, KL, cross-entropy
ch04	Numerical Computation	under/overflow, conditioning, gradient descent, KKT
ch05	Machine Learning Basics	capacity, bias–variance, No Free Lunch, MLE, manifolds
ch06	Deep Feedforward Networks	output/hidden units, universal approximation, backprop
ch07	Regularization	norm penalties, augmentation, early stopping, dropout
ch08	Optimization	SGD, momentum, init, Adam, batch norm, saddles
ch09

Topic Index

• Activation functions, ReLU, GELU → ch06
• Adam, AdamW, adaptive optimizers → ch08, ch07
• Attention, transformers → ch10, ch12
• Autoencoders, denoising, sparse → ch14, ch13
• Backpropagation, autodiff → ch06
• Batch / layer normalization → ch08
• Bias–variance, double descent → ch05
• Convolution, pooling, receptive field → ch09
• Cross-entropy, KL divergence, entropy → ch03
• Diffusion, score matching → ch18, ch14, ch20
• Dropout, weight decay, early stopping → ch07
• ELBO, variational inference, EM → ch19
• Energy-based models, graphical models → ch16
• GANs, VAEs, generative taxonomy → ch20
• Gradient clipping, exploding/vanishing → ch10, ch08
• Hyperparameter search → ch11
• Initialization → ch08
• LSTM, GRU, BPTT, teacher forcing → ch10
• Maximum likelihood, MAP → ch05, ch03
• MCMC, Gibbs, importance sampling → ch17
• Numerical stability, softmax, log-space → ch04
• Partition function, CD, PCD, NCE → ch18, ch16
• PCA, ICA, factor analysis → ch13, ch02
• Representation learning, transfer, probes → ch15, ch01
• Saddle points, ill-conditioning → ch08, ch04
• SVD, eigendecomposition, condition number → ch02
• Training diagnostics, metric choice → ch11
• Universal approximation → ch06

Supporting Files

• glossary.md — every key term with its chapter
• patterns.md — techniques as instruments, with trade-offs
• cheatsheet.md — decision tables and defaults
• references/book_to_2026_delta.md — what changed, per chapter
• references/prerequisite_map.md — the real dependency graph
• references/study_method_canon.md — how to study a hard text
• references/rights_and_use.md — why this is a companion

Tools

S=engineering/deep-learning-book/skills/deep-learning-book/scripts
python3 $S/reading_path_planner.py --goal "train a transformer" --background applied --hours-per-week 5
python3 $S/training_diagnostics.py --train-loss 0.02 --val-loss 1.9 --grad-norm 0.4 --epochs 30
python3 $S/capacity_planner.py --params 12000000 --train-examples 50000 --train-error 0.01 --val-error 0.22
python3 $S/model_arithmetic.py --spec-sample

Every tool supports --help, --sample and --output json, uses the standard library only, and returns typed exit codes.

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Scope & Limits

This companion covers the 2016 edition's 20 chapters and the delta between them and 2026 practice. It does not cover: reinforcement learning beyond passing mention, LLM training infrastructure, RLHF/DPO alignment, agentic systems, MLOps tooling, or fairness and safety evaluation — none of which the book treats. For production ML engineering use engineering-team/senior-ml-engineer; for LLM cost work use engineering/llm-cost-optimizer.

When a question lands outside the book, say the book does not cover it and cite the delta reference for what replaced its position. A companion that quietly extrapolates is worse than one that names its boundary.