---
title: "ai-engineering-from-scratch"
type: "tool"
slug: "rohitg00-ai-engineering-from-scratch"
canonical_url: "https://www.graphcanon.com/tools/rohitg00-ai-engineering-from-scratch"
github_url: "https://github.com/rohitg00/ai-engineering-from-scratch"
homepage_url: "https://aiengineeringfromscratch.com"
stars: 37583
forks: 6259
primary_language: "Python"
license: "MIT"
categories: ["inference-serving", "developer-tools", "evaluation-observability", "speech-audio", "computer-vision", "data-retrieval", "model-training", "llm-frameworks", "ai-agents"]
tags: ["agents", "llm", "nlp", "tutorial", "transformers", "ai-agents"]
updated_at: "2026-07-07T19:42:09.556147+00:00"
---

# ai-engineering-from-scratch

> Curriculum for building AI systems from scratch

A comprehensive curriculum covering over 500 lessons and 20 phases to build AI systems from foundational concepts to complex projects using Python, TypeScript, Rust, and Julia. Focuses on learning by doing, with a range of topics including machine learning, deep learning, NLP, computer vision, and more.

## Facts

- Repository: https://github.com/rohitg00/ai-engineering-from-scratch
- Homepage: https://aiengineeringfromscratch.com
- Stars: 37,583 · Forks: 6,259 · Open issues: 94 · Watchers: 233
- Primary language: Python
- License: MIT
- Last pushed: 2026-06-25T19:43:11+00:00

## Categories

- [Inference & Serving](/categories/inference-serving.md)
- [Developer Tools](/categories/developer-tools.md)
- [Evaluation & Observability](/categories/evaluation-observability.md)
- [Speech & Audio](/categories/speech-audio.md)
- [Computer Vision](/categories/computer-vision.md)
- [Data & Retrieval](/categories/data-retrieval.md)
- [Model Training](/categories/model-training.md)
- [LLM Frameworks](/categories/llm-frameworks.md)
- [AI Agents](/categories/ai-agents.md)

## Tags

agents, llm, nlp, tutorial, transformers, ai-agents

## Related tools

- [ECC](/tools/affaan-m-ecc.md) - The agent harness performance optimization system (★ 226,991)
- [hermes-agent](/tools/nousresearch-hermes-agent.md) - The self-improving AI agent built by Nous Research (★ 210,911)
- [AutoGPT](/tools/significant-gravitas-autogpt.md) - AutoGPT: Build, Deploy, and Run AI Agents (★ 185,420)
- [ollama](/tools/ollama-ollama.md) - Get up and running with Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models. (★ 175,664)
- [prompts.chat](/tools/f-prompts-chat.md) - The world's largest open-source prompt library for AI (★ 165,025)
- [transformers](/tools/huggingface-transformers.md) - 🤗 Transformers: the model-definition framework for state-of-the-art machine learning models (★ 162,350)
- [JavaGuide](/tools/snailclimb-javaguide.md) - Snailclimb/JavaGuide: 面试 & 后端通用面试指南，覆盖计算机基础、数据库、分布式、高并发、系统设计与 AI 应用开发 (★ 156,863)
- [langflow](/tools/langflow-ai-langflow.md) - Langflow is a powerful platform for building and deploying AI-powered agents and workflows. (★ 151,311)

## README (excerpt)

```text
<p align="center">
  <img src="assets/banner.svg" alt="AI Engineering from Scratch — reference manual banner" width="100%">
</p>

<p align="center">
  <a href="LICENSE"><img src="https://img.shields.io/badge/license-MIT-1a1a1a?style=flat-square&labelColor=fafaf5" alt="MIT License"></a>
  <a href="ROADMAP.md"><img src="https://img.shields.io/badge/lessons-503-3553ff?style=flat-square&labelColor=fafaf5" alt="503 lessons"></a>
  <a href="#contents"><img src="https://img.shields.io/badge/phases-20-3553ff?style=flat-square&labelColor=fafaf5" alt="20 phases"></a>
  <a href="https://github.com/rohitg00/ai-engineering-from-scratch/stargazers"><img src="https://img.shields.io/github/stars/rohitg00/ai-engineering-from-scratch?style=flat-square&labelColor=fafaf5&color=3553ff" alt="GitHub stars"></a>
  <a href="https://aiengineeringfromscratch.com"><img src="https://img.shields.io/badge/web-aiengineeringfromscratch.com-3553ff?style=flat-square&labelColor=fafaf5" alt="Website"></a>
</p>

## From the creator of [Agent Memory - #1 Persistent memory ⭐](https://github.com/rohitg00/agentmemory) <a href="https://github.com/rohitg00/agentmemory/stargazers"><img src="https://img.shields.io/github/stars/rohitg00/agentmemory?style=flat-square&labelColor=fafaf5&color=3553ff" alt="GitHub stars"></a> which naturally works with any agents or chat assistants.

```
░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒
```

> **84% of students already use AI tools. Only 18% feel prepared to use them
> professionally.** This curriculum closes that gap.
>
> 503 lessons. 20 phases. ~320 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.


<p align="center"><sub><b>150,639</b> readers &nbsp;·&nbsp; <b>241,669</b> page views in the last 30 days &nbsp;·&nbsp; as of 2026-06-07</sub></p>


## 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, 503 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.

```
░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒
```

## 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 &amp; 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 &amp; 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 &amp; Protocols"]
  P13 --
```

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/tools/rohitg00-ai-engineering-from-scratch`](/api/graphcanon/tools/rohitg00-ai-engineering-from-scratch)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_