[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71H4QTQSDGDYYWBBVR8SSRV":3,"skill-same:01M71H4QTQSDGDYYWBBVR8SSRV":211},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":38,"linkOnly":39,"markdownContent":40,"markdownBody":41,"frontMatter":42,"refName":48,"commitSha":49,"skillDirectory":50,"forksCount":51,"repositoryHtmlUrl":52,"repositoryHomepageUrl":53,"ownerHtmlUrl":54,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":55,"install":56,"resources":69},"01M71H4QTQSDGDYYWBBVR8SSRV","deep-learning-book",null,"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 Indexes all 20 chapters, carries a 2016-to-2026 delta layer naming what the book got right, what was superseded (transformers, AdamW, diffusion, double descent) and what still holds, and ships four deterministic tools: a prerequisite-aware reading-path planner, a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\u002FFLOP\u002Factivation-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.","marshadgani\u002FArshad.AI","marshadgani","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F275367399?v=4","01M71G2BP4YMZ4AX5YY0QBHAZH","01M71G2BPPHHFRMHQAKJTZPGK4",1,"MIT","https:\u002F\u002Fgithub.com\u002Fmarshadgani\u002FArshad.AI\u002Fblob\u002Fclaude\u002Fai-personal-assistant-main\u002F.claude\u002Fskills\u002Fclaude-skills\u002Fdeep-learning-book\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"deep learning","deep learning book","learning","book","deep","2026","Transformers","math","knowledge base","route","teaching","when","free","knowledge","chapter","decision","training","memory","official","never","2026-11-10 10:12:30",false,"---\nname: deep-learning-book\ndescription: \"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\u002FFLOP\u002Factivation-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.\"\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  author: Alireza Rezvani\n  category: engineering\n  updated: 2026-08-25\n---\n\n# Deep Learning — Study Companion\n\n**Source book**: *Deep Learning*, Ian Goodfellow, Yoshua Bengio & Aaron Courville\n(MIT Press, 2016) · 20 chapters, 3 parts · read free at\n[deeplearningbook.org](https:\u002F\u002Fwww.deeplearningbook.org\u002F) · companion compiled 2026-08-25.\n\n**This is a companion, not a copy.** The book is copyrighted, and its site states that the\nHTML-only format exists to discourage copying under the authors' MIT Press contract. Nothing\nhere reproduces its text. Every chapter file is original synthesis — what the chapter\nestablishes, how to use it, where it has aged — plus a link to the official chapter. Read the\nbook at the link; use this to navigate it, keep it current, and turn it into decisions.\nSee [references\u002Frights_and_use.md](references\u002Frights_and_use.md).\n\n## How to Use This Skill\n\n- **No argument** — load the core frameworks below.\n- **A topic** — ask about `regularization`, `saddle points`, `partition function`; resolved\n  through the Topic Index, then that chapter file is read before answering.\n- **`chNN`** — load that chapter's file.\n- **\"is this still true?\"** — the 2016→2026 delta layer, in every chapter file and in\n  [references\u002Fbook_to_2026_delta.md](references\u002Fbook_to_2026_delta.md).\n- **\"where do I start?\"** — run `scripts\u002Freading_path_planner.py`.\n\nWhen asked about something outside these 20 chapters, say so and route to the delta reference\nrather than improvising the book's position on material published after it.\n\n---\n\n## Core Frameworks & Mental Models\n\n### The (T, P, E) frame — ch05\n\nName the **task**, the **performance measure**, and the **experience** in one sentence before any\nmodel code. Most failed projects failed at P: an unstated metric, or a proxy whose relationship\nto the real objective was never checked.\n\n### Every loss is a negative log-likelihood — ch03, ch06\n\nChoose the output distribution, then take its negative log. Gaussian → MSE, Bernoulli → binary\ncross-entropy, categorical → cross-entropy, Laplace → MAE. \"Which loss?\" is always the question\n\"which distribution?\" in disguise. Modern contrastive and preference objectives sit outside this\nframe — a real limit of the book, not a gap in your understanding.\n\n### KL asymmetry decides your failure mode — ch03, ch19, ch20\n\nD(p‖q) ≠ D(q‖p). Forward KL is mode-covering (blurry averages); reverse KL is mode-seeking\n(sharp but partial). This single fact predicts VAE blur, GAN mode collapse, and the\ncharacteristic over-confidence of mean-field variational posteriors.\n\n### Train-error-first triage — ch11, ch05\n\nHigh training error → capacity or optimization is the bottleneck; **more data will not help**.\nLow training error with a large validation gap → data or regularization. This is the highest-value\nheuristic in the book. `scripts\u002Ftraining_diagnostics.py` runs it.\n\n### Capacity, the gap, and the U-curve's caveat — ch05, ch07\n\nRegularization trades variance for bias. But the classical U-shaped capacity curve is incomplete:\npast the interpolation threshold, test error can fall again (double descent, 2019–2020,\npost-dating the book). Practical consequence: when a large model overfits, try more data, more\nregularization or longer training **before** shrinking it.\n\n### Architecture is a prior, not a trick — ch09, ch10, ch15\n\nConvolution asserts translation equivariance and locality. Recurrence asserts that the past\ncompresses into a state. A distributed representation asserts that factors combine\ncombinatorially. When the assertion is false, the architecture cannot be rescued by tuning — and\nwhen it is true, it beats capacity. This is also why Vision Transformers need more data than\nConvNets: they discard the prior and buy it back with examples.\n\n### Depth's real cost is gradient flow and activation memory — ch06, ch08, ch10\n\nBackprop is the chain rule scheduled well: one forward-pass-equivalent of compute, and memory\nproportional to stored activations. Depth fails through vanishing\u002Fexploding gradients and\nill-conditioning, which is why residual connections, normalization and clipping exist.\n\n### The partition function organizes Part III — ch16, ch17, ch18, ch19\n\nFor undirected models, the likelihood gradient needs samples from the model itself. Four escape\nroutes: sample it (CD\u002FPCD), sidestep it algebraically (pseudolikelihood, **score matching**),\nlearn around it (NCE), or estimate it for evaluation (AIS). Score matching's descendants are\ntoday's diffusion models — which is why Part III repays reading even though its models did not\nsurvive.\n\n### Diagnose before you redesign — ch04, ch08, ch11\n\nGradient norm exploding → clip. Norm large but loss flat → ill-conditioning. Norm near zero with\nhigh loss → saturation or dead units. NaN → numerics first. Change one thing per experiment.\n\n---\n\n## Chapter Index\n\n| # | Title | Key content |\n|---|-------|-------------|\n| [ch01](chapters\u002Fch01-introduction.md) | Introduction | representation learning, depth as composition, curse of dimensionality |\n| [ch02](chapters\u002Fch02-linear-algebra.md) | Linear Algebra | norms, SVD, eigendecomposition, conditioning, PCA |\n| [ch03](chapters\u002Fch03-probability-information-theory.md) | Probability & Information Theory | distributions, entropy, KL, cross-entropy |\n| [ch04](chapters\u002Fch04-numerical-computation.md) | Numerical Computation | under\u002Foverflow, conditioning, gradient descent, KKT |\n| [ch05](chapters\u002Fch05-machine-learning-basics.md) | Machine Learning Basics | capacity, bias–variance, No Free Lunch, MLE, manifolds |\n| [ch06](chapters\u002Fch06-deep-feedforward-networks.md) | Deep Feedforward Networks | output\u002Fhidden units, universal approximation, backprop |\n| [ch07](chapters\u002Fch07-regularization.md) | Regularization | norm penalties, augmentation, early stopping, dropout |\n| [ch08](chapters\u002Fch08-optimization.md) | Optimization | SGD, momentum, init, Adam, batch norm, saddles |\n| [ch09](chapters\u002Fch09-convolutional-networks.md) | Convolutional Networks | sparse interactions, sharing, equivariance, pooling |\n| [ch10](chapters\u002Fch10-sequence-modeling.md) | Sequence Modeling | BPTT, vanishing gradients, LSTM\u002FGRU, attention |\n| [ch11](chapters\u002Fch11-practical-methodology.md) | Practical Methodology | metrics, baselines, the data-vs-capacity rule, debugging |\n| [ch12](chapters\u002Fch12-applications.md) | Applications | scaling, compression, vision, speech, NLP (dated) |\n| [ch13](chapters\u002Fch13-linear-factor-models.md) | Linear Factor Models | PPCA, factor analysis, ICA, sparse coding |\n| [ch14](chapters\u002Fch14-autoencoders.md) | Autoencoders | undercomplete, sparse, denoising, contractive |\n| [ch15](chapters\u002Fch15-representation-learning.md) | Representation Learning | transfer, distributed codes, disentanglement |\n| [ch16](chapters\u002Fch16-structured-probabilistic-models.md) | Structured Probabilistic Models | directed\u002Fundirected, energy-based, d-separation |\n| [ch17](chapters\u002Fch17-monte-carlo-methods.md) | Monte Carlo Methods | importance sampling, MCMC, Gibbs, mixing |\n| [ch18](chapters\u002Fch18-partition-function.md) | Confronting the Partition Function | CD\u002FPCD, pseudolikelihood, score matching, NCE, AIS |\n| [ch19](chapters\u002Fch19-approximate-inference.md) | Approximate Inference | ELBO, EM, mean field, amortization |\n| [ch20](chapters\u002Fch20-deep-generative-models.md) | Deep Generative Models | Boltzmann machines, VAE, GAN, autoregressive |\n\n## Topic Index\n\n- **Activation functions, ReLU, GELU** → ch06\n- **Adam, AdamW, adaptive optimizers** → ch08, ch07\n- **Attention, transformers** → ch10, ch12\n- **Autoencoders, denoising, sparse** → ch14, ch13\n- **Backpropagation, autodiff** → ch06\n- **Batch \u002F layer normalization** → ch08\n- **Bias–variance, double descent** → ch05\n- **Convolution, pooling, receptive field** → ch09\n- **Cross-entropy, KL divergence, entropy** → ch03\n- **Diffusion, score matching** → ch18, ch14, ch20\n- **Dropout, weight decay, early stopping** → ch07\n- **ELBO, variational inference, EM** → ch19\n- **Energy-based models, graphical models** → ch16\n- **GANs, VAEs, generative taxonomy** → ch20\n- **Gradient clipping, exploding\u002Fvanishing** → ch10, ch08\n- **Hyperparameter search** → ch11\n- **Initialization** → ch08\n- **LSTM, GRU, BPTT, teacher forcing** → ch10\n- **Maximum likelihood, MAP** → ch05, ch03\n- **MCMC, Gibbs, importance sampling** → ch17\n- **Numerical stability, softmax, log-space** → ch04\n- **Partition function, CD, PCD, NCE** → ch18, ch16\n- **PCA, ICA, factor analysis** → ch13, ch02\n- **Representation learning, transfer, probes** → ch15, ch01\n- **Saddle points, ill-conditioning** → ch08, ch04\n- **SVD, eigendecomposition, condition number** → ch02\n- **Training diagnostics, metric choice** → ch11\n- **Universal approximation** → ch06\n\n## Supporting Files\n\n- [glossary.md](glossary.md) — every key term with its chapter\n- [patterns.md](patterns.md) — techniques as instruments, with trade-offs\n- [cheatsheet.md](cheatsheet.md) — decision tables and defaults\n- [references\u002Fbook_to_2026_delta.md](references\u002Fbook_to_2026_delta.md) — what changed, per chapter\n- [references\u002Fprerequisite_map.md](references\u002Fprerequisite_map.md) — the real dependency graph\n- [references\u002Fstudy_method_canon.md](references\u002Fstudy_method_canon.md) — how to study a hard text\n- [references\u002Frights_and_use.md](references\u002Frights_and_use.md) — why this is a companion\n\n## Tools\n\n```bash\nS=engineering\u002Fdeep-learning-book\u002Fskills\u002Fdeep-learning-book\u002Fscripts\npython3 $S\u002Freading_path_planner.py --goal \"train a transformer\" --background applied --hours-per-week 5\npython3 $S\u002Ftraining_diagnostics.py --train-loss 0.02 --val-loss 1.9 --grad-norm 0.4 --epochs 30\npython3 $S\u002Fcapacity_planner.py --params 12000000 --train-examples 50000 --train-error 0.01 --val-error 0.22\npython3 $S\u002Fmodel_arithmetic.py --spec-sample\n```\n\nEvery tool supports `--help`, `--sample` and `--output json`, uses the standard library only, and\nreturns typed exit codes.\n\n---\n\n## Scope & Limits\n\nThis companion covers the 2016 edition's 20 chapters and the delta between them and 2026\npractice. It does **not** cover: reinforcement learning beyond passing mention, LLM training\ninfrastructure, RLHF\u002FDPO alignment, agentic systems, MLOps tooling, or fairness and safety\nevaluation — none of which the book treats. For production ML engineering use\n`engineering-team\u002Fsenior-ml-engineer`; for LLM cost work use `engineering\u002Fllm-cost-optimizer`.\n\nWhen a question lands outside the book, say the book does not cover it and cite the delta\nreference for what replaced its position. A companion that quietly extrapolates is worse than one\nthat names its boundary.\n","# Deep Learning — Study Companion\n\n**Source book**: *Deep Learning*, Ian Goodfellow, Yoshua Bengio & Aaron Courville\n(MIT Press, 2016) · 20 chapters, 3 parts · read free at\n[deeplearningbook.org](https:\u002F\u002Fwww.deeplearningbook.org\u002F) · companion compiled 2026-08-25.\n\n**This is a companion, not a copy.** The book is copyrighted, and its site states that the\nHTML-only format exists to discourage copying under the authors' MIT Press contract. Nothing\nhere reproduces its text. Every chapter file is original synthesis — what the chapter\nestablishes, how to use it, where it has aged — plus a link to the official chapter. Read the\nbook at the link; use this to navigate it, keep it current, and turn it into decisions.\nSee [references\u002Frights_and_use.md](references\u002Frights_and_use.md).\n\n## How to Use This Skill\n\n- **No argument** — load the core frameworks below.\n- **A topic** — ask about `regularization`, `saddle points`, `partition function`; resolved\n  through the Topic Index, then that chapter file is read before answering.\n- **`chNN`** — load that chapter's file.\n- **\"is this still true?\"** — the 2016→2026 delta layer, in every chapter file and in\n  [references\u002Fbook_to_2026_delta.md](references\u002Fbook_to_2026_delta.md).\n- **\"where do I start?\"** — run `scripts\u002Freading_path_planner.py`.\n\nWhen asked about something outside these 20 chapters, say so and route to the delta reference\nrather than improvising the book's position on material published after it.\n\n---\n\n## Core Frameworks & Mental Models\n\n### The (T, P, E) frame — ch05\n\nName the **task**, the **performance measure**, and the **experience** in one sentence before any\nmodel code. Most failed projects failed at P: an unstated metric, or a proxy whose relationship\nto the real objective was never checked.\n\n### Every loss is a negative log-likelihood — ch03, ch06\n\nChoose the output distribution, then take its negative log. Gaussian → MSE, Bernoulli → binary\ncross-entropy, categorical → cross-entropy, Laplace → MAE. \"Which loss?\" is always the question\n\"which distribution?\" in disguise. Modern contrastive and preference objectives sit outside this\nframe — a real limit of the book, not a gap in your understanding.\n\n### KL asymmetry decides your failure mode — ch03, ch19, ch20\n\nD(p‖q) ≠ D(q‖p). Forward KL is mode-covering (blurry averages); reverse KL is mode-seeking\n(sharp but partial). This single fact predicts VAE blur, GAN mode collapse, and the\ncharacteristic over-confidence of mean-field variational posteriors.\n\n### Train-error-first triage — ch11, ch05\n\nHigh training error → capacity or optimization is the bottleneck; **more data will not help**.\nLow training error with a large validation gap → data or regularization. This is the highest-value\nheuristic in the book. `scripts\u002Ftraining_diagnostics.py` runs it.\n\n### Capacity, the gap, and the U-curve's caveat — ch05, ch07\n\nRegularization trades variance for bias. But the classical U-shaped capacity curve is incomplete:\npast the interpolation threshold, test error can fall again (double descent, 2019–2020,\npost-dating the book). Practical consequence: when a large model overfits, try more data, more\nregularization or longer training **before** shrinking it.\n\n### Architecture is a prior, not a trick — ch09, ch10, ch15\n\nConvolution asserts translation equivariance and locality. Recurrence asserts that the past\ncompresses into a state. A distributed representation asserts that factors combine\ncombinatorially. When the assertion is false, the architecture cannot be rescued by tuning — and\nwhen it is true, it beats capacity. This is also why Vision Transformers need more data than\nConvNets: they discard the prior and buy it back with examples.\n\n### Depth's real cost is gradient flow and activation memory — ch06, ch08, ch10\n\nBackprop is the chain rule scheduled well: one forward-pass-equivalent of compute, and memory\nproportional to stored activations. Depth fails through vanishing\u002Fexploding gradients and\nill-conditioning, which is why residual connections, normalization and clipping exist.\n\n### The partition function organizes Part III — ch16, ch17, ch18, ch19\n\nFor undirected models, the likelihood gradient needs samples from the model itself. Four escape\nroutes: sample it (CD\u002FPCD), sidestep it algebraically (pseudolikelihood, **score matching**),\nlearn around it (NCE), or estimate it for evaluation (AIS). Score matching's descendants are\ntoday's diffusion models — which is why Part III repays reading even though its models did not\nsurvive.\n\n### Diagnose before you redesign — ch04, ch08, ch11\n\nGradient norm exploding → clip. Norm large but loss flat → ill-conditioning. Norm near zero with\nhigh loss → saturation or dead units. NaN → numerics first. Change one thing per experiment.\n\n---\n\n## Chapter Index\n\n| # | Title | Key content |\n|---|-------|-------------|\n| [ch01](chapters\u002Fch01-introduction.md) | Introduction | representation learning, depth as composition, curse of dimensionality |\n| [ch02](chapters\u002Fch02-linear-algebra.md) | Linear Algebra | norms, SVD, eigendecomposition, conditioning, PCA |\n| [ch03](chapters\u002Fch03-probability-information-theory.md) | Probability & Information Theory | distributions, entropy, KL, cross-entropy |\n| [ch04](chapters\u002Fch04-numerical-computation.md) | Numerical Computation | under\u002Foverflow, conditioning, gradient descent, KKT |\n| [ch05](chapters\u002Fch05-machine-learning-basics.md) | Machine Learning Basics | capacity, bias–variance, No Free Lunch, MLE, manifolds |\n| [ch06](chapters\u002Fch06-deep-feedforward-networks.md) | Deep Feedforward Networks | output\u002Fhidden units, universal approximation, backprop |\n| [ch07](chapters\u002Fch07-regularization.md) | Regularization | norm penalties, augmentation, early stopping, dropout |\n| [ch08](chapters\u002Fch08-optimization.md) | Optimization | SGD, momentum, init, Adam, batch norm, saddles |\n| [ch09](chapters\u002Fch09-convolutional-networks.md) | Convolutional Networks | sparse interactions, sharing, equivariance, pooling |\n| [ch10](chapters\u002Fch10-sequence-modeling.md) | Sequence Modeling | BPTT, vanishing gradients, LSTM\u002FGRU, attention |\n| [ch11](chapters\u002Fch11-practical-methodology.md) | Practical Methodology | metrics, baselines, the data-vs-capacity rule, debugging |\n| [ch12](chapters\u002Fch12-applications.md) | Applications | scaling, compression, vision, speech, NLP (dated) |\n| [ch13](chapters\u002Fch13-linear-factor-models.md) | Linear Factor Models | PPCA, factor analysis, ICA, sparse coding |\n| [ch14](chapters\u002Fch14-autoencoders.md) | Autoencoders | undercomplete, sparse, denoising, contractive |\n| [ch15](chapters\u002Fch15-representation-learning.md) | Representation Learning | transfer, distributed codes, disentanglement |\n| [ch16](chapters\u002Fch16-structured-probabilistic-models.md) | Structured Probabilistic Models | directed\u002Fundirected, energy-based, d-separation |\n| [ch17](chapters\u002Fch17-monte-carlo-methods.md) | Monte Carlo Methods | importance sampling, MCMC, Gibbs, mixing |\n| [ch18](chapters\u002Fch18-partition-function.md) | Confronting the Partition Function | CD\u002FPCD, pseudolikelihood, score matching, NCE, AIS |\n| [ch19](chapters\u002Fch19-approximate-inference.md) | Approximate Inference | ELBO, EM, mean field, amortization |\n| [ch20](chapters\u002Fch20-deep-generative-models.md) | Deep Generative Models | Boltzmann machines, VAE, GAN, autoregressive |\n\n## Topic Index\n\n- **Activation functions, ReLU, GELU** → ch06\n- **Adam, AdamW, adaptive optimizers** → ch08, ch07\n- **Attention, transformers** → ch10, ch12\n- **Autoencoders, denoising, sparse** → ch14, ch13\n- **Backpropagation, autodiff** → ch06\n- **Batch \u002F layer normalization** → ch08\n- **Bias–variance, double descent** → ch05\n- **Convolution, pooling, receptive field** → ch09\n- **Cross-entropy, KL divergence, entropy** → ch03\n- **Diffusion, score matching** → ch18, ch14, ch20\n- **Dropout, weight decay, early stopping** → ch07\n- **ELBO, variational inference, EM** → ch19\n- **Energy-based models, graphical models** → ch16\n- **GANs, VAEs, generative taxonomy** → ch20\n- **Gradient clipping, exploding\u002Fvanishing** → ch10, ch08\n- **Hyperparameter search** → ch11\n- **Initialization** → ch08\n- **LSTM, GRU, BPTT, teacher forcing** → ch10\n- **Maximum likelihood, MAP** → ch05, ch03\n- **MCMC, Gibbs, importance sampling** → ch17\n- **Numerical stability, softmax, log-space** → ch04\n- **Partition function, CD, PCD, NCE** → ch18, ch16\n- **PCA, ICA, factor analysis** → ch13, ch02\n- **Representation learning, transfer, probes** → ch15, ch01\n- **Saddle points, ill-conditioning** → ch08, ch04\n- **SVD, eigendecomposition, condition number** → ch02\n- **Training diagnostics, metric choice** → ch11\n- **Universal approximation** → ch06\n\n## Supporting Files\n\n- [glossary.md](glossary.md) — every key term with its chapter\n- [patterns.md](patterns.md) — techniques as instruments, with trade-offs\n- [cheatsheet.md](cheatsheet.md) — decision tables and defaults\n- [references\u002Fbook_to_2026_delta.md](references\u002Fbook_to_2026_delta.md) — what changed, per chapter\n- [references\u002Fprerequisite_map.md](references\u002Fprerequisite_map.md) — the real dependency graph\n- [references\u002Fstudy_method_canon.md](references\u002Fstudy_method_canon.md) — how to study a hard text\n- [references\u002Frights_and_use.md](references\u002Frights_and_use.md) — why this is a companion\n\n## Tools\n\n```bash\nS=engineering\u002Fdeep-learning-book\u002Fskills\u002Fdeep-learning-book\u002Fscripts\npython3 $S\u002Freading_path_planner.py --goal \"train a transformer\" --background applied --hours-per-week 5\npython3 $S\u002Ftraining_diagnostics.py --train-loss 0.02 --val-loss 1.9 --grad-norm 0.4 --epochs 30\npython3 $S\u002Fcapacity_planner.py --params 12000000 --train-examples 50000 --train-error 0.01 --val-error 0.22\npython3 $S\u002Fmodel_arithmetic.py --spec-sample\n```\n\nEvery tool supports `--help`, `--sample` and `--output json`, uses the standard library only, and\nreturns typed exit codes.\n\n---\n\n## Scope & Limits\n\nThis companion covers the 2016 edition's 20 chapters and the delta between them and 2026\npractice. It does **not** cover: reinforcement learning beyond passing mention, LLM training\ninfrastructure, RLHF\u002FDPO alignment, agentic systems, MLOps tooling, or fairness and safety\nevaluation — none of which the book treats. For production ML engineering use\n`engineering-team\u002Fsenior-ml-engineer`; for LLM cost work use `engineering\u002Fllm-cost-optimizer`.\n\nWhen a question lands outside the book, say the book does not cover it and cite the delta\nreference for what replaced its position. A companion that quietly extrapolates is worse than one\nthat names its boundary.\n",{"name":5,"license":14,"metadata":43,"description":7},{"author":44,"updated":45,"version":46,"category":47},"Alireza Rezvani",1787616000000,"1.0.0","engineering","claude\u002Fai-personal-assistant-main","fb33f7f5b3a206eb019c788e3c48e91c02ccb1f9",".claude\u002Fskills\u002Fclaude-skills\u002Fdeep-learning-book",0,"https:\u002F\u002Fgithub.com\u002Fmarshadgani\u002FArshad.AI","https:\u002F\u002Farshad-ai-seven.vercel.app","https:\u002F\u002Fgithub.com\u002Fmarshadgani","2026-11-02 00:51:35",{"source":8,"skillName":5,"commands":57,"prompt":67,"zipAvailable":68},[58,61,64],{"tool":59,"command":60},"npx","npx skills add marshadgani\u002FArshad.AI --skill deep-learning-book",{"tool":62,"command":63},"bunx","bunx skills add marshadgani\u002FArshad.AI --skill deep-learning-book",{"tool":65,"command":66},"pnpm","pnpm dlx skills add marshadgani\u002FArshad.AI --skill 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Platforms: Facebook, Twitter, LinkedIn, YouTube, Instagram, Pinterest, TikTok, Threads, Google Ads.","https:\u002F\u002Fgithub.com\u002Fmarshadgani\u002FArshad.AI\u002Fblob\u002Fclaude\u002Fai-personal-assistant-main\u002F.claude\u002Fskills\u002Fui-ux-pro-max\u002Fdesign\u002FSKILL.md",[309,312,313,316],{"categoryNo":310,"categoryName":311,"taxonomyType":223,"taxonomyTypeDesc":224},"01JBSEEDCAT000000000000003","演示文稿",{"categoryNo":281,"categoryName":282,"taxonomyType":223,"taxonomyTypeDesc":224},{"categoryNo":314,"categoryName":315,"taxonomyType":223,"taxonomyTypeDesc":224},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":284,"categoryName":285,"taxonomyType":286,"taxonomyTypeDesc":287},[294,318,319,320,289,321,322,323,324,299,325,326,327,328,329,330,331,332,244,333],"Media","tiktok","actions","logo","logo generation","chart","multi-platform","ads","youtube","generate","presentations","google","atlas cloud","screenshot","muapi","identity",{"skillNo":335,"name":336,"nameZh":6,"description":337,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":338,"categories":339,"keywords":342,"publishedAt":246},"01M71MSH9T7S227MN2YSYB2EP5","design-system","Token architecture, component specifications, and slide generation. Three-layer tokens (primitive→semantic→component), CSS variables, spacing\u002Ftypography scales, component specs, strategic slide creation. Use for design tokens, systematic design, brand-compliant presentations.","https:\u002F\u002Fgithub.com\u002Fmarshadgani\u002FArshad.AI\u002Fblob\u002Fclaude\u002Fai-personal-assistant-main\u002F.claude\u002Fskills\u002Fui-ux-pro-max\u002Fdesign-system\u002FSKILL.md",[340,341],{"categoryNo":281,"categoryName":282,"taxonomyType":223,"taxonomyTypeDesc":224},{"categoryNo":284,"categoryName":285,"taxonomyType":286,"taxonomyTypeDesc":287},[294,343,344,261,345,346,347,348,300,328,349,350,351,352,353,354,355,232,356,357],"design system","system","css variables","creation","Variables","architecture","systematic","token","spacing","layer","typography","brand","three","tokens","slide generation",{"skillNo":359,"name":360,"nameZh":6,"description":361,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":362,"categories":363,"keywords":366,"publishedAt":246},"01M71MSCFZ1HFMNBDMAFXDZV92","banner-design","Design banners for social media, ads, website heroes, creative assets, and print. Multiple art direction options with optional generated or supplied visuals. Actions: design, create, generate banner. Platforms: Facebook, Twitter\u002FX, LinkedIn, YouTube, Instagram, Google Display, website hero, print. Styles: minimalist, gradient, bold typography, photo-based, illustrated, geometric, retro, glassmorphism, 3D, neon, duotone, editorial, collage.","https:\u002F\u002Fgithub.com\u002Fmarshadgani\u002FArshad.AI\u002Fblob\u002Fclaude\u002Fai-personal-assistant-main\u002F.claude\u002Fskills\u002Fui-ux-pro-max\u002Fbanner-design\u002FSKILL.md",[364,365],{"categoryNo":281,"categoryName":282,"taxonomyType":223,"taxonomyTypeDesc":224},{"categoryNo":284,"categoryName":285,"taxonomyType":286,"taxonomyTypeDesc":287},[294,367,368,318,369,370,371,320,372,373,374,375,376,377,299,325,326,327,378,379],"banner","banner design","3D","direction","neon","Art","social","generated","hero","dimensions","display","options","website"]