ai-engineering-from-scratch 发布 v2026.10：新增 MCPA 认证备考与多语言界面

Official release notes (https://github.com/rohitg00/ai-engineering-from-scratch/releases/tag/v2026.10) Edition 2026.10 adds exam prep for the Model Context Protocol Associate (MCPA) certification, translates the site interface around every lesson, and holds the course to stricter checks. The curriculum stays at **523 lessons across 20 phases**, with 58 lesson texts revised since Edition 2026.09. CI now runs the lessons' own tests, and a sweep repaired datasets, models, tools, and links that no longer resolve. ## Prepare for the MCPA exam The new [MCPA certification prep](https://aiengineeringfromscratch.com/certification?id=mcpa-f) covers the Model Context Protocol Associate exam from the Agentic AI Foundation, delivered through Linux Foundation Training. It is aligned to MCP 2026-07-28, the stateless revision of the protocol. - 34 lessons across the five exam domains, from the JSON-RPC envelope and capability negotiation to OAuth, consent, and audit trails, ending in a capstone that exercises every domain in one transcript. - Each lesson has a runnable standard-library lab with tests, a six-question quiz, a figure, and a reference artifact. - A 30-question diagnostic and three original 60-question mock exams, each following the blueprint's domain split. - CI checks every lab transcript against the 2026-07-28 wire shape, so no lesson presents a legacy method such as `initialize` as current. Figures in both certification programs now animate. MCPA diagrams play each message in order, and Claude labs show their inputs flowing into the result. On phones, wide diagrams scroll inside their card instead of shrinking. Certification cards now show only the exam facts each program has published. Together, the Claude and MCPA programs hold 67 lessons, 12 assessments, and 505 original questions. Study with an AI tutor: ```bash npx skills add rohitg00/ai-engineering-from-scratch ``` Then ask it to use `mcpa-certification` or `claude-certification`. This is independent study material. It is not affiliated with the Agentic AI Foundation, the Linux Foundation, or Anthropic, does not reproduce live exam questions, and cannot guarantee a passing score. ## The site interface follows your language Lesson bodies were already translated. Now the interface around them follows the selected language too: header navigation, the lesson sidebar, "On this page", checkpoints, quiz headings, the code and output panels, and lesson navigation. It covers the eight CI languages: Simplified Chinese, Hindi, Spanish, Arabic, French, Portuguese, Turkish, and Vietnamese. All 523 lessons have machine translations in those eight languages. Lessons revised in this edition are re-translated automatically after each merge, and code and other protected technical content stay in their original form. ## Lessons that run, references that resolve - CI now runs every lesson's own tests on each change. Lessons that need scientific libraries such as PyTorch are skipped there. - A new check keeps quiz answers from being guessable by length. It fails if the share of questions whose correct option is clearly the longest goes up. - References that stopped working were repaired: the Wikipedia dataset configuration, the MMAU-Pro benchmark, Depth Anything, NLI and speculative-decoding draft models, vLLM's speculative decoding and FP8 flags, GGUF conversion with llama.cpp, the current Gemini SDK and models, and GPTQModel and LLM Compressor in place of the archived AutoGPTQ and AutoAWQ. - 46 dead links now point to moved pages, publisher records, or archived copies. Citations of pages that never existed were replaced with their real sources or removed. - Two lesson claims now match their sources: vLLM supports speculative decoding together with chunked prefill, and the 2025 alignment-faking follow-up tested interrogation training, scratchpad length penalties, and process supervision. - Lesson bugs fixed: MCP lessons 13/06 to 13/09 could not import their code under the documented test command; phase 19 lessons 29, 39, 59, and 86 had broken fixtures, tests, and YAML parsing; perplexity changed on every run in 10/10 (#358); the ACP audit trail in 16/03 was always empty (#357); and the few-shot code in 11/02 now matches the lesson text (#436). ## Security - Checkpoints in 19/47 load with `weights_only=True`, so restoring an untrusted file cannot run code. - The code guard in 11/09 parses snippets instead of matching substrings, which closes an escape through `__globals__`. - The sandbox path jail no longer lets a slashless symlink escape (#249). ## Fixes around the course - The homepage and About page load even when the browser blocks storage. - The Docker image builds on Apple Silicon, and the setup lesson installs `unzip` for fnm. - Mermaid diagrams keep readable fills in dark mode. - Requesting Markdown at the site root now returns Markdown for agents. - The README banner lists every phase once, and its artifact icons no longer collide with their labels. - Smaller fixes: shared check defaults in 00/01 (#361), an unused import in 00/03 (#286), and quiz de-biasing that gives the same result on every operating system (#466). ## Newsletter Subscribe to AI Engineering Newsletter from the homepage, after any lesson, or on the About page. The form takes you to Substack with your email filled in. ## Books All six English volumes are rebuilt as EPUB and PDF files from this edition's source and attached below: | Volume | Title | Chapters | | --- | --- | ---: | | 1 | Foundations | 52 | | 2 | Deep Learning | 58 | | 3 | Language | 45 | | 4 | Large Language Models | 68 | | 5 | Agents | 157 | | 6 | Production | 143 | | | Total | 523 | Interactive figures, graded quizzes, and runnable labs remain accessible through each chapter's online links. The website and README book links continue to use the latest release assets. ## Contributors Thank you to everyone who contributed to this edition: - @Mubashirrrr closed the sandbox path jail escape through a slashless symlink (#249) - @thejesh23 made perplexity deterministic in 10/10 (#358) and fixed the empty ACP audit trail in 16/03 (#357) - @FabienDaniel aligned the few-shot code in 11/02 with the lesson text (#436) - @dajiaohuang made quiz de-biasing give the same result on every operating system (#466) - @rushil-b-patel centralized the check defaults in 00/01 (#361) - @ismet removed an unused import in 00/03 (#286) ## Sponsors Thank you to [SerpApi](https://serpapi.com/ai-engineering-from-scratch) for supporting the curriculum. SerpApi provides a Web Search API for AI apps, available in Markdown and JSON. [Full changes since Edition 2026.09](https://github.com/rohitg00/ai-engineering-from-scratch/compare/v2026.09...v2026.10) Repository README (current repository documentation, not release changes) Read in your language: Español · Français · Português · Deutsch · Italiano · 简体中文 · 日本語 · 한국어 · हिन्दी · العربية · Русский · Türkçe Translated landing pages, committed to the repo. English is canonical; lesson pages are machine-translated on the translations branch. See docs/i18n.md. ## From the creator of [Agent Memory - #1 Persistent memory ⭐](https://github.com/rohitg00/agentmemory) which naturally works with any agents or chat assistants. ```text ░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒ ``` > **84% of students already use AI tools. Only 18% feel prepared to use them > professionally.** This curriculum closes that gap. > > 523 lessons. 20 phases. ~342 hours. Python, TypeScript, Rust, Julia. Every lesson ships > a reusable artifact: a prompt, a skill, an agent, an MCP server. Free, open source, MIT. > > You don't just learn AI. You build it. End-to-end. By hand. 114,584 readers  ·  181,995 page views in the last 30 days  ·  as of 2026-08-29 ## Start here: choose what you want to build You do not need to scan 523 lessons before beginning. Pick one goal. Each link opens the same curriculum on GitHub or the website, and both versions use the same lesson code. | Your goal | Learn on GitHub | Learn on the website | |---|---|---| | I am new and want the complete foundation | [Phase 0: Setup and Tooling](phases/00-setup-and-tooling/) | [Dev Environment](https://aiengineeringfromscratch.com/lesson?path=phases/00-setup-and-tooling/01-dev-environment) | | I know Python and want math plus ML foundations | [Phase 1: Math Foundations](phases/01-math-foundations/) | [Linear Algebra Intuition](https://aiengineeringfromscratch.com/lesson?path=phases/01-math-foundations/01-linear-algebra-intuition) | | I want to build production LLM applications | [Phase 11: LLM Engineering](phases/11-llm-engineering/) | [Prompt Engineering](https://aiengineeringfromscratch.com/lesson?path=phases/11-llm-engineering/01-prompt-engineering) | | I want to build agents | [Phase 14: Agent Engineering](phases/14-agent-engineering/) | [The Agent Loop](https://aiengineeringfromscratch.com/lesson?path=phases/14-agent-engineering/01-the-agent-loop) | | I want to use coding agents on real repositories | [Agent-Assisted Engineering path](learning-paths/using-coding-agents.json) | [Agent-Assisted Engineering](https://aiengineeringfromscratch.com/lesson?path=phases/14-agent-engineering/31-agent-workbench-why-models-fail&learningPath=using-coding-agents) | | I want to shape the right build before implementation | [Product Judgment and Delivery path](learning-paths/shaping-the-build.json) | [Product Judgment and Delivery](https://aiengineeringfromscratch.com/lesson?path=phases/14-agent-engineering/47-outcomes-before-output&learningPath=shaping-the-build) | | I want to build with Model Context Protocol (MCP) | [Model Context Protocol (MCP) route](phases/13-tools-and-protocols/README.md#model-context-protocol-mcp-path) | [Model Context Protocol (MCP) path](https://aiengineeringfromscratch.com/lesson?path=phases/13-tools-and-protocols/06-mcp-fundamentals&learningPath=model-context-protocol) | | I want to write and ship Agent Skills | [Focused Agent Skills route](phases/13-tools-and-protocols/README.md#agent-skills-fast-path) | [Agent Skills path](https://aiengineeringfromscratch.com/lesson?path=phases/13-tools-and-protocols/22-skills-and-agent-sdks&learningPath=agent-skills) | | I want to prepare for a Claude certification | [Certification onboarding](certifications/claude/GETTING_STARTED.md) | [Certification Academy](https://aiengineeringfromscratch.com/certifications.html) | | I want to prepare for the MCP Associate (MCPA) | [MCPA onboarding](certifications/mcpa/GETTING_STARTED.md) | [MCPA track](https://aiengineeringfromscratch.com/certification?id=mcpa-f) | Not sure where you fit? Use the [`start-learning` placement tutor](skills/start-learning/SKILL.md) or the [website prerequisites guide](https://aiengineeringfromscratch.com/prereqs.html). Compare four core domains and six career routes in the [AI Engineering Learning Paths](https://aiengineeringfromscratch.com/learning-paths.html). ### Sponsors Thank you to our sponsors. Your support keeps every lesson free and open source. See all supporters Become a sponsor ### Use every lesson the same way 1. **Read** `docs/en.md` and explain the core idea in your own words. 2. **Type and build** the important code instead of treating the code block as decoration. 3. **Run** the lesson command from the repository root, the directory containing `README.md` and `phases/`. 4. **Keep evidence**: the command, working directory, exit code, meaningful output, and the artifact you changed or produced. 5. **Continue** only when you can explain the output and make one small change without guessing. Commands in lesson pages are paths from the repository root unless the lesson explicitly says to change directories. If a lesson offers several languages, run the implementation for the language you are learning. ### Clone it and produce your first evidence ```bash git clone https://github.com/rohitg00/ai-engineering-from-scratch.git cd ai-engineering-from-scratch python3 phases/00-setup-and-tooling/01-dev-environment/code/verify.py --route beginner python3 phases/01-math-foundations/01-linear-algebra-intuition/code/vectors.py ``` The preflight separates requirements needed now from tools needed later. Every required failure includes the detected reason and a corrective command. The second command is a dependency-free lesson and ends by showing that a matrix times a vector is the operation inside a neural network layer. Save that terminal output as your first evidence. ## Add the AI tutor in 30 seconds If Node.js, `npx`, and a skill-capable coding agent are already installed, your coding agent can become your tutor in two commands. A repository clone is not needed to install or read the tutor. Runnable focused-path labs need `python3`. Agent Skills host labs also need a selected host and a writable user or project skill scope. Check the local requirements first: ```bash node --version npx --version python3 --version ``` Then install the curriculum skills and choose the host and scope you intend to use when the installer asks: ```bash npx skills add rohitg00/ai-engineering-from-scratch ``` Invocation syntax belongs to the host, not to the portable `SKILL.md` format: | Host | Start the course | Start Model Context Protocol (MCP) | Start Agent Skills | Run a phase quiz | |---|---|---|---|---| | Codex | `start-learning`, or choose it from `/skills` | `learn-mcp`, or choose it from `/skills` | `learn-agent-skills`, or choose it from `/skills` | `check-understanding 13`, or choose it from `/skills` | | Claude Code | `/start-learning` | `/learn-mcp` | `/learn-agent-skills` | `/check-understanding 13` | | Other compatible hosts | `Use start-learning to begin the course.` | `Use learn-mcp to start the Model Context Protocol (MCP) path.` | `Use learn-agent-skills to start the Agent Skills Engineering path.` | `Use check-understanding to quiz me on Phase 13.` | A ten-question placement quiz maps what you already know to a starting phase and saves a personalized study plan to `LEARNING.md`. From there, the `learn` skill teaches one lesson per session: concept, math, code, quiz. It streams lessons straight from this repo, and the `course-guide` skill jumps you to the exact lesson that covers anything you are stuck on. In Codex, invoke these skills with `learn` and `course-guide`; in Claude Code, use `/learn` and `/course-guide`; in other compatible hosts, ask to use the skill by name. Only want Model Context Protocol (MCP)? Use the MCP invocation for your host. It creates `MCP-LEARNING.md` and follows one 17-lesson route through stateless requests, transports, bidirectional work, security, reliability, registry governance, and conformance evidence. The exact order and checkpoints live in the [Model Context Protocol (MCP) manifest](learning-paths/model-context-protocol.json). Only want Agent Skills? Use the Agent Skills invocation for your host. It creates `AGENT-SKILLS-LEARNING.md` and follows one coherent five-lesson route: contract, discovery, invocation, sandbox boundaries, then release evals and real-host portability. Start on the web with the [Agent Skills path](https://aiengineeringfromscratch.com/lesson?path=phases/13-tools-and-protocols/22-skills-and-agent-sdks&learningPath=agent-skills). The installer lists the hosts it can configure and asks where to install. If you do not have Node.js, `npx`, `python3`, a supported host, or a writable scope yet, use the website or read `docs/en.md` manually. That path teaches the concepts, but real-host discovery, invocation, script, and uninstall evidence remains pending until the preflight is available. Read the lessons at [aiengineeringfromscratch.com](https://aiengineeringfromscratch.com). ## How this works Most AI material teaches in scattered pieces. A paper here, a fine-tuning post there, a flashy agent demo somewhere else. The pieces rarely line up. You ship a chatbot but can't explain its loss curve. You hook a function to an agent but can't say what attention does inside the model that's calling it. This curriculum is the spine. 20 phases, 523 lessons, four languages: Python, TypeScript, Rust, Julia. Linear algebra at one end, autonomous swarms at the other. Every algorithm gets built from raw math first. Backprop. Tokenizer. Attention. Agent loop. By the time PyTorch shows up, you already know what it's doing under the hood. Each lesson runs the same loop: read the problem, derive the math, write the code, run the test, keep the artifact. No five-minute videos, no copy-paste deploys, no hand-holding. Free, open source, and built to run on your own laptop. ```text ░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒ ``` ## The shape of the curriculum Twenty phases stack on top of each other. Math is the floor. Agents and production are the roof. Skip ahead if you already know the lower layers, but don't skip and then wonder why something at the top is breaking. ```mermaid %%{init: {'theme':'base','themeVariables':{'primaryColor':'#fafaf5','primaryTextColor':'#1a1a1a','primaryBorderColor':'#3553ff','lineColor':'#3553ff','fontFamily':'JetBrains Mono','fontSize':'12px'}}}%% flowchart TB P0["Phase 0 — Setup & Tooling"] --> P1["Phase 1 — Math Foundations"] P1 --> P2["Phase 2 — ML Fundamentals"] P2 --> P3["Phase 3 — Deep Learning Core"] P3 --> P4["Phase 4 — Vision"] P3 --> P5["Phase 5 — NLP"] P3 --> P6["Phase 6 — Speech & Audio"] P3 --> P9["Phase 9 — RL"] P5 --> P7["Phase 7 — Transformers"] P7 --> P8["Phase 8 — GenAI"] P7 --> P10["Phase 10 — LLMs from Scratch"] P10 --> P11["Phase 11 — LLM Engineering"] P10 --> P12["Phase 12 — Multimodal"] P11 --> P13["Phase 13 — Tools & Protocols"] P13 --> P14["Phase 14 — Agent Engineering"] P14 --> P15["Phase 15 — Autonomous Systems"] P15 --> P16["Phase 16 — Multi-Agent & Swarms"] P14 --> P17["Phase 17 — Infrastructure & Production"] P15 --> P18["Phase 18 — Ethics & Alignment"] P16 --> P19["Phase 19 — Capstone Projects"] P17 --> P19 P18 --> P19 ``` ```text ░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒ ``` ## The shape of a lesson Each lesson lives in its own folder, with the same structure across the entire curriculum: ```text phases/-/-/ ├── code/ runnable implementations (Python, TypeScript, Rust, Julia) ├── docs/ │ └── en.md lesson narrative └── outputs/ prompts, skills, agents, or MCP servers this lesson produces ``` Every lesson follows six beats. The *Build It / Use It* split is the spine — you implement the algorithm from scratch first, then run the same thing through the production library. You understand what the framework is doing because you wrote the smaller version yourself. ```mermaid %%{init: {'theme':'base','themeVariables':{'primaryColor':'#fafaf5','primaryTextColor':'#1a1a1a','primaryBorderColor':'#3553ff','lineColor':'#3553ff','fontFamily':'JetBrains Mono','fontSize':'13px'}}}%% flowchart LR M["MOTTO one-line core idea"] --> Pr["PROBLEM concrete pain"] Pr --> C["CONCEPT diagrams & intuition"] C --> B["BUILD IT raw math, no frameworks"] B --> U["USE IT same thing in PyTorch / sklearn"] U --> S["SHIP IT prompt · skill · agent · MCP"] ``` ## Getting started Three ways in. Pick one. **Option A — learn in your terminal *(recommended)*.** After the Node.js, `npx`, host, and scope preflight above, install the learning skills into a compatible agent and let the course drive itself: ```bash npx skills add rohitg00/ai-engineering-from-scratch ``` Use the host-specific invocation table above. The installed skills provide `start-learning`, `learn`, `course-guide`, and the focused `learn-mcp` and `learn-agent-skills` routes. Lesson prose can stream from this repository without a clone. A local clone is required for copied repository code commands and executable MCP or Agent Skills labs. Progress lives in `LEARNING.md`, `MCP-LEARNING.md`, or `AGENT-SKILLS-LEARNING.md` in your project, so every session can resume. **Option B — read.** Open any completed lesson on [aiengineeringfromscratch.com](https://aiengineeringfromscratch.com) or expand a phase under [Contents](#contents). No setup, no cloning. **Option C — clone and run.** ```bash git clone https://github.com/rohitg00/ai-engineering-from-scratch.git cd ai-engineering-from-scratch python3 phases/01-math-foundations/01-linear-algebra-intuition/code/vectors.py ``` Cloning also auto-loads the learning skills in Claude Code, and gives every lesson's code to the `learn` tutor for real execution instead of read-along. ### Prerequisites - You can write code (any language; Python helps). - You want to understand how AI **actually works**, not just call APIs. ### Prepare for Claude certifications The [Claude Certification Academy](certifications/claude/README.md) is a free, open-source preparation program for all four official Claude certification tracks: Associate Foundations, Developer Foundations, Architect Foundations, and Architect Professional. Each route combines blueprint-mapped lessons, runnable labs, a diagnostic, capstone work, and a full-length original practice exam. Use the [AI-native GitHub onboarding guide](certifications/claude/GETTING_STARTED.md) with Claude Code, Codex, ChatGPT, Cursor, or another agent. Run `claude-certification` in Codex, `/claude-certification` in Claude Code, or ask another host to use `claude-certification`. It chooses a track, creates a persistent route in `CLAUDE-CERTIFICATION.md`, teaches one step at a time, runs the real labs, and gives artifact-based feedback. The same curriculum remains available on the [certification website](https://aiengineeringfromscratch.com/certifications.html). The academy is independent study material based on public exam objectives. It is not affiliated with Anthropic, does not reproduce live exam questions, and cannot guarantee a passing score. ### Prepare for the MCP Associate (MCPA) certification The [MCPA Certification Curriculum](certifications/mcpa/README.md) is a free, open-source preparation program for the Model Context Protocol Associate exam from the Agentic AI Foundation, delivered through Linux Foundation Training. Its 34 lessons teach the stateless 2026-07-28 protocol across the five exam domains: per-request `_meta` and `server/discover` in place of the old handshake, multi round-trip requests, subscriptions, caching, the tasks and MCP Apps extensions, OAuth authorization, and the registry and SDK tiers. Every lesson ships a runnable standard-library lab whose transcript is checked for the current wire shape, and the track adds a diagnostic, a capstone, and three full-length original practice exams whose question mix follows the published blueprint weights. Use the [AI-native GitHub onboarding guide](certifications/mcpa/GETTING_STARTED.md) with Claude Code, Codex, ChatGPT, Cursor, or another agent. Run `mcpa-certification` in Codex, `/mcpa-certification` in Claude Code, or ask another host to use `mcpa-certification`. It creates a persistent route in `MCPA-CERTIFICATION.md`, teaches one step at a time, runs the real labs, and gives artifact-based feedback. The same curriculum is available on the [MCPA track page](https://aiengineeringfromscratch.com/certification?id=mcpa-f). This curriculum is independent study material based on public exam objectives. It is not affiliated with the Agentic AI Foundation or the Linux Foundation, does not reproduce live exam questions, and cannot guarantee a passing score. ### The learning skills | Skill | What it does | |---|---| | [`start-learning`](skills/start-learning/SKILL.md) | One-time onboarding: why you're learning, placement quiz, personalized plan saved to `LEARNING.md`. | | [`learn`](skills/learn/SKILL.md) | The tutor loop. Warm-up recall, then the next lesson taught interactively, then its quiz; records progress and a review queue. | | [`course-guide`](skills/course-guide/SKILL.md) | Topic router. "Where do I learn attention?" or "my loss is NaN" → the exact lessons, with links. | | [`learn-mcp`](skills/learn-mcp/SKILL.md) | Focused Model Context Protocol (MCP) tutor. Creates `MCP-LEARNING.md`, follows the 17-lesson manifest, and records wire, security, reliability, and conformance evidence. | | [`learn-agent-skills`](skills/learn-agent-skills/SKILL.md) | Focused Agent Skills tutor. Creates `AGENT-SKILLS-LEARNING.md`, teaches lessons 22, 24, 25, 26, and 27, and records real-host evidence. | | [`claude-certification`](skills/claude-certification/SKILL.md) | Certification tutor. Chooses CCAO-F, CCDV-F, CCAR-F, or CCAR-P; teaches each lesson; runs labs; reviews artifacts; administers diagnostics and mocks; saves progress. | | [`mcpa-certification`](skills/mcpa-certification/SKILL.md) | MCPA tutor. Follows the 34-lesson `mcpa-f` route on the 2026-07-28 protocol; teaches each lesson; runs labs and the wire checker; administers the diagnostic and three mocks; saves progress. | | [`find-your-level`](skills/find-your-level/SKILL.md) | Ten-question placement quiz. Maps your knowledge to a starting phase and produces a personalized path with hour estimates. | | [`check-understanding `](skills/check-understanding/SKILL.md) | Per-phase quiz, eight questions, with feedback and specific lessons to review. Use the Codex, Claude Code, or natural-language form in the invocation table above. | ```text ░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒ ``` ## Read the core curriculum as a book The 20-phase core curriculum under `phases/` compiles into a six-volume book series. EPUB and PDF are built by CI from the same core lesson sources and attached to every [GitHub release](https://github.com/rohitg00/ai-engineering-from-scratch/releases); the links below always resolve to the newest release. Volume numbers index the series, not versions: each copy carries a dated edition stamp, and older editions stay downloadable from their release. Certification curricula are intentionally not converted into the books. Their AI tutor state, runnable labs, interactive figures, diagnostics, and timed mocks remain first-class on GitHub and the website. | Vol | Title | Phases | Download | |-----|-------|--------|----------| | 1 | Foundations · Math, Tooling, and Classical Machine Learning | 00-02 | [EPUB](https://github.com/rohitg00/ai-engineering-from-scratch/releases/latest/download/aiefs-vol1-foundations.epub) · [PDF](https://github.com/rohitg00/ai-engineering-from-scratch/releases/latest/download/aiefs-vol1-foundations.pdf) | | 2 | Deep Learning · Networks, Vision, and Speech | 03, 04, 06 | [EPUB](https://github.com/rohitg00/ai-engineering-from-scratch/releases/latest/download/aiefs-vol2-deep-learning.epub) · [PDF](https://github.com/rohitg00/ai-engineering-from-scratch/releases/latest/download/aiefs-vol2-deep-learning.pdf) | | 3 | Language · NLP Foundations and the Transformer | 05, 07 | [EPUB](https://github.com/rohitg00/ai-engineering-from-scratch/releases/latest/download/aiefs-vol3-language.epub) · [PDF](https://github.com/rohitg00/ai-engineering-from-scratch/releases/latest/download/aiefs-vol3-language.pdf) | | 4 | Large Language Models · Generation, Reinforcement, Pretraining, and Engineering | 08-11 | [EPUB](https://github.com/rohitg00/ai-engineering-from-scratch/releases/latest/download/aiefs-vol4-llms.epub) · [PDF](https://github.com/rohitg00/ai-engineering-from-scratch/releases/latest/download/aiefs-vol4-llms.pdf) | | 5 | Agents · Multimodality, Protocols, Autonomy, and Swarms | 12-16 | [EPUB](https://github.com/rohitg00/ai-engineering-from-scratch/releases/latest/download/aiefs-vol5-agents.epub) · [PDF](https://github.com/rohitg00/ai-engineering-from-scratch/releases/latest/download/aiefs-vol5-agents.pdf) | | 6 | Production · Infrastructure, Safety, and Capstones | 17-19 | [EPUB](https://github.com/rohitg00/ai-engineering-from-scratch/releases/latest/download/aiefs-vol6-production.epub) · [PDF](https://github.com/rohitg00/ai-engineering-from-scratch/releases/latest/download/aiefs-vol6-production.pdf) | The book is the snapshot; this repository is the living edition. Every chapter ends with links back to the lesson's animated figures, quiz, and runnable code. Build locally with `python3 scripts/build_book.py` (pandoc required); pipeline details in [book/README.md](book/README.md). ```text ░░░▒▒▒░░░▒▒