rohitg00 / ai-engineering-from-scratch
git clone https://github.com/rohitg00/ai-engineering-from-scratch.git
v2026.10.4 · 1d ago
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Learn AI Engineering! Learn it. Build it. Ship it for others.
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LanguagePython
LicenseMIT
Created2026-03-18 (7mo ago)
Last push1d ago
Watchers450
OwnerUser
Seen on GitHub Trending
- 2026-10-20 — #6 monthly in python · +16,662 ★
- 2026-10-20 — #14 monthly · +16,662 ★
GitHub topics
agentsaiai-agentsai-engineeringcomputer-visioncoursedeep-learningdshfrom-scratchgenerative-aillmmachine-learningmcppythonreareinforcement-learningrusttransformerstutorialtypescript
Links
- GitHub: github.com/rohitg00/ai-engineering-from-scratch
- Homepage: https://aiengineeringfromscratch.com
- Releases · Issues · Hacker News mentions
README excerpt
Implement model internals, retrieval pipelines, and agent runtimes. Test them, inspect failures, and keep the code and evaluation results. **Start learning** · **Choose a path** · **Try a lab** · **Build a project** · **Browse the curriculum** Free, open source, MIT. Learn on the website, with a coding agent, or by running local code. > 542 lessons. 20 phases. Python, TypeScript, Rust, Julia. Read in your language 🇬🇧 English · 🇨🇳 简体中文 · 🇹🇼 繁體中文(台灣) · 🇯🇵 日本語 · 🇰🇷 한국어 · 🇵🇹 Português · 🇧🇷 Português (Brasil) · 🇪🇸 Español · 🇩🇪 Deutsch · 🇫🇷 Français · 🇮🇹 Italiano · 🇳🇱 Nederlands · 🇵🇱 Polski · 🇨🇿 Čeština · 🇷🇴 Română · 🇭🇺 Magyar · 🇬🇷 Ελληνικά · 🇸🇪 Svenska · 🇩🇰 Dansk · 🇳🇴 Norsk · 🇫🇮 Suomi · 🇷🇺 Русский · 🇺🇦 Українська · 🇹🇷 Türkçe · 🇮🇱 עברית · 🇸🇦 العربية · 🇮🇷 فارسی · 🇮🇳 हिन्दी · 🇧🇩 বাংলা · 🇵🇰 اردو · 🇹🇭 ไทย · 🇻🇳 Tiếng Việt · 🇮🇩 Bahasa Indonesia · 🇵🇭 Tagalog ### Sponsors Your support keeps every lesson free and open source. See all supporters · Become a sponsor ## Learning paths | Route | Starting lesson | |---|---| | Model foundations | Setup and tooling | | LLM systems | Prompt engineering | | Agents and delivery | The agent loop | Compare career paths · Prerequisites and study time ### Gradient descent Twenty starting points follow gradient descent on a quadratic loss. The graph shows their positions and mean loss after each update. Adjust the learning rate in the lesson · Compare GD, momentum, and Adam in code ### Projects Three projects with staged starters, reference implementations, and local graders. Run commands from the repository root after setup. Starters fail until you implement the stages. 01 · Retrieval Evaluation Lab · Python · Ranking metrics and regression checks A candidate improves mean NDCG while one query ranks its most relevant evidence lower. Build a query-by-query comparison that reports the regression and can fail a release check. Use Python 3.10+. Review RAG and model evaluation. Implement ranking validation, precision and recall, rank-sensitive metrics, then system comparison. [code] **Keep:** a reproducible comparison with per-query deltas and the judgments used to score them. Metrics describe those judgments; they do not establish answer correctness. Start the project · Inspect the reference · Run on your own inputs 02 · Agent Trace Debugger · TypeScript · Trace parsing and timing A supplied trace still takes 100 ms, but total token usage rises by 200 and one span starts failing. Separate overlapping child work from parent execution time, then produce a report that exposes the change. Use Node.js 22.18+ and Python 3 for the grader. Implement JSONL parsing, parent validation, interval arithmetic, then an inspectable timeline. [code] **Keep:** the input trace, an HTML timeline, and a JSON regression report. Preserve exclusive per-span token counts so parent and child usage are not counted twice. Start the project · Inspect the reference · Explore timing interactively 03 · Tool Call Fi