Paddle Design Compiler
PaddlePaddle/Paddle
A skill your agent uses when working with Paddle 3.0 compiler full pipeline: SOT (Symbolic Opcode Translator) for bytecode-level dy2st graph capture, PIR (Paddle IR) for SSA-based intermediate…
Study companion and working knowledge base for the Deep Learning textbook by Goodfellow, Bengio & Courville (MIT Press, 2016), read free at deeplearningbook.org.
Install Deep Learning Book
$ npx skills add alirezarezvani/claude-skills --skill deep-learning-book -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install alirezarezvani/claude-skills deep-learning-book --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/engineering/deep-learning-book/skills/deep-learning-book .claude/skills/deep-learning-book && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "deep-learning-book" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/engineering/deep-learning-book/skills/deep-learning-book into .claude/skills/deep-learning-book/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deep-learning-book", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/alirezarezvani/claude-skills/tree/main/engineering/deep-learning-book/skills/deep-learning-bookType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add alirezarezvani/claude-skills --skill deep-learning-book -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install alirezarezvani/claude-skills deep-learning-book --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/engineering/deep-learning-book/skills/deep-learning-book .agents/skills/deep-learning-book && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "deep-learning-book" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/engineering/deep-learning-book/skills/deep-learning-book into .agents/skills/deep-learning-book/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deep-learning-book", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add alirezarezvani/claude-skills --skill deep-learning-book -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install alirezarezvani/claude-skills deep-learning-book --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/engineering/deep-learning-book/skills/deep-learning-book .cursor/skills/deep-learning-book && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "deep-learning-book" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/engineering/deep-learning-book/skills/deep-learning-book into .cursor/skills/deep-learning-book/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deep-learning-book", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/alirezarezvani/claude-skills.git --path engineering/deep-learning-book/skills/deep-learning-book--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add alirezarezvani/claude-skills --skill deep-learning-book -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install alirezarezvani/claude-skills deep-learning-book --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/engineering/deep-learning-book/skills/deep-learning-book .gemini/skills/deep-learning-book && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "deep-learning-book" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/engineering/deep-learning-book/skills/deep-learning-book into .gemini/skills/deep-learning-book/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deep-learning-book", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install alirezarezvani/claude-skills deep-learning-bookInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add alirezarezvani/claude-skills --skill deep-learning-book -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/engineering/deep-learning-book/skills/deep-learning-book .github/skills/deep-learning-book && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "deep-learning-book" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/engineering/deep-learning-book/skills/deep-learning-book into .github/skills/deep-learning-book/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deep-learning-book", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add alirezarezvani/claude-skills --skill deep-learning-book -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install alirezarezvani/claude-skills deep-learning-book --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/engineering/deep-learning-book/skills/deep-learning-book .opencode/skills/deep-learning-book && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "deep-learning-book" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/engineering/deep-learning-book/skills/deep-learning-book into .opencode/skills/deep-learning-book/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deep-learning-book", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
deep-learning-book- GitHub stars
- 28k
- Token cost
- ~2.9k tokens
- SKILL.md length
- 1,126 words
- Files
- 37 (incl. scripts, references, assets)
- Skills in repo
- 342
- Repo updated
- First seen
- Licence
- MIT
At a glance
Study companion and working knowledge base for the Deep Learning textbook by Goodfellow, Bengio & Courville (MIT Press, 2016), read free at deeplearningbook.org.
- Teaching this book
- SKILL.md covers How to Use This Skill, Core Frameworks & Mental Models, Chapter Index and Topic Index, plus 3 more sections
- Calls python3
- Planning a route through it
What it does
Deep Learning Book is an agent skill from alirezarezvani/claude-skills. 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…
Its SKILL.md is about 2.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 38 other files, including scripts, reference files and assets (for example `assets/chapter_worksheet.md`, `assets/example_layer_spec.json` and `assets/study_log_template.md`).
It sits in AI & LLM Engineering, covering Deep learning and Translation. The repository describes itself as: 380 Claude Code skills & agent skills & plugins (30+ Agents, 70+ custom commands, 380+ skills, customizable references, scripts)for Claude Code, Codex, Gemini CLI, Cursor, and 8… The licence is MIT.
When your agent uses it
- Teaching this book
- Planning a route through it
- Deciding whether a chapters advice is still current
- Translating its math into a training decision
Example prompts
- “/deep-learning-book”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit 19392f7. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 1 file in scripts/, which the agent can run.
Shell commands in SKILL.md call:
python3
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
deeplearningbook.org
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Deep Learning Book loads about 2.9k tokens when it runs, and up to ~8.3k if it reads all its reference files. Until then it costs about 200 tokens; SKILL.md has 1,126 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from alirezarezvani/claude-skills at commit 19392f7, republished under its MIT licence (© alirezarezvani). 1,126 words, ~2,863 tokens.
Download SKILL.mdSave it as
.claude/skills/deep-learning-book/SKILL.md (or your agent's skills folder). This skill also uses 36 other files; get the full folder from GitHub.- name
- deep-learning-book
- description
- 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.
- license
- MIT
- metadata.version
- 1.0.0
- metadata.author
- Alireza Rezvani
- metadata.category
- engineering
- metadata.updated
- 2026-08-25
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.
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.
Show full SKILL.md (472 more words)Show less
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 | Convolutional Networks | sparse interactions, sharing, equivariance, pooling |
| ch10 | Sequence Modeling | BPTT, vanishing gradients, LSTM/GRU, attention |
| ch11 | Practical Methodology | metrics, baselines, the data-vs-capacity rule, debugging |
| ch12 | Applications | scaling, compression, vision, speech, NLP (dated) |
| ch13 | Linear Factor Models | PPCA, factor analysis, ICA, sparse coding |
| ch14 | Autoencoders | undercomplete, sparse, denoising, contractive |
| ch15 | Representation Learning | transfer, distributed codes, disentanglement |
| ch16 | Structured Probabilistic Models | directed/undirected, energy-based, d-separation |
| ch17 | Monte Carlo Methods | importance sampling, MCMC, Gibbs, mixing |
| ch18 | Confronting the Partition Function | CD/PCD, pseudolikelihood, score matching, NCE, AIS |
| ch19 | Approximate Inference | ELBO, EM, mean field, amortization |
| ch20 | Deep Generative Models | Boltzmann machines, VAE, GAN, autoregressive |
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-sampleEvery tool supports --help, --sample and --output json, uses the standard library only, and
returns typed exit codes.
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.
© alirezarezvani, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 36 other files (scripts, references, assets) in engineering/deep-learning-book/skills/deep-learning-book of alirezarezvani/claude-skills.
- SKILL.md
- assets/chapter_worksheet.md
- assets/example_layer_spec.json
- assets/study_log_template.md
- chapters/ch01-introduction.md
- chapters/ch02-linear-algebra.md
- chapters/ch03-probability-information-theory.md
- chapters/ch04-numerical-computation.md
- chapters/ch05-machine-learning-basics.md
- chapters/ch06-deep-feedforward-networks.md
- chapters/ch07-regularization.md
- chapters/ch08-optimization.md
- chapters/ch09-convolutional-networks.md
- chapters/ch10-sequence-modeling.md
- chapters/ch11-practical-methodology.md
- chapters/ch12-applications.md
- chapters/ch13-linear-factor-models.md
- chapters/ch14-autoencoders.md
- chapters/ch15-representation-learning.md
- … and 18 more
Open the folder on GitHubat commit 19392f7
Compare with similar skills
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|---|---|---|---|---|---|---|
| Deep Learning Book this skillalirezarezvani/claude-skills | 28k | — | ~2.9k | Automated safety check: Pass | MIT | |
| Paddle Design CompilerPaddlePaddle/Paddle | 24k | — | ~3.6k | Automated safety check: Pass | Apache-2.0 | |
| Neuron Nki Writinguw-syfi/vibesys | 103 | — | ~5k | Automated safety check: Pass | MIT | |
| Add Uint Supportpytorch/pytorch | 104k | 2 repos | ~2.3k | Automated safety check: Pass | Custom licence | |
| Segment Anything Model GuideOrchestra-Research/AI-Research-SKILLs | 13k | 9 repos | ~3.3k | Automated safety check: Pass | MIT | |
| Add Oponnx/onnx | 22k | — | ~1.2k | Automated safety check: Pass | Apache-2.0 |
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Questions about Deep Learning Book
What does Deep Learning Book do?
Study companion and working knowledge base for the Deep Learning textbook by Goodfellow, Bengio & Courville (MIT Press, 2016), read free at deeplearningbook.org. Deep Learning Book is an agent skill from alirezarezvani/claude-skills.org.
When should I use Deep Learning Book?
Deep Learning Book fits situations like: teaching this book; planning a route through it; deciding whether a chapters advice is still current; translating its math into a training decision.
How do I install Deep Learning Book in Claude Code?
Run `npx skills add alirezarezvani/claude-skills --skill deep-learning-book -a claude-code`. Or copy the skill folder (engineering/deep-learning-book/skills/deep-learning-book in alirezarezvani/claude-skills) into .claude/skills/deep-learning-book in your project. Claude Code loads it when a task matches its description.
How do I install Deep Learning Book in Codex?
Run `npx skills add alirezarezvani/claude-skills --skill deep-learning-book -a codex`. Or copy the skill folder (engineering/deep-learning-book/skills/deep-learning-book in alirezarezvani/claude-skills) into .agents/skills/deep-learning-book in your project. Codex loads it when a task matches its description.
Can I use Deep Learning Book in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add alirezarezvani/claude-skills --skill deep-learning-book -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/deep-learning-book, .gemini/skills/deep-learning-book, .github/skills/deep-learning-book and .opencode/skills/deep-learning-book in your project.
What does Deep Learning Book need to run?
Going by SKILL.md and its folder, Deep Learning Book needs the command-line tools its instructions call (python3). Our summary lists: Python 3.
Does Deep Learning Book access the network?
SKILL.md names 1 domain. As links in the text: deeplearningbook.org. This is read from the text; nothing was executed.
Is Deep Learning Book safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Deep Learning Book use?
Deep Learning Book is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Deep Learning Book use?
About 2.9k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5.4k tokens, read only when the agent opens those files.
What are the alternatives to Deep Learning Book?
Skills that share tags, products or a category with Deep Learning Book: Paddle Design Compiler (PaddlePaddle/Paddle, 24k stars), Neuron Nki Writing (uw-syfi/vibesys, 103 stars), Add Uint Support (pytorch/pytorch, 104k stars) and Segment Anything Model Guide (Orchestra-Research/AI-Research-SKILLs, 13k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Deep Learning Book?
alirezarezvani (a GitHub user) maintains it in alirezarezvani/claude-skills, which has 27,788 GitHub stars. The repository holds 342 skills in this directory. The repository was last updated on August 30, 2026.
Source: alirezarezvani/claude-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.