# AI Engineering from Scratch — Hands-On AI Systems Course

> A comprehensive open-source curriculum that walks developers through building AI systems from first principles, covering agents, transformers, computer vision, and deployment.

## Install

Save as a script file and run:

# AI Engineering from Scratch

## Quick Use
```bash
git clone https://github.com/rohitg00/ai-engineering-from-scratch.git
cd ai-engineering-from-scratch
pip install -r requirements.txt
# Follow module-by-module from Module 1
```

## Introduction
AI Engineering from Scratch is a structured, open-source curriculum designed to teach developers how to build production AI systems from the ground up. Rather than relying on high-level abstractions, it guides you through implementing core components yourself so you understand what happens under the hood.

## What AI Engineering from Scratch Does
- Provides 20+ hands-on modules covering transformers, agents, RAG, computer vision, and reinforcement learning
- Teaches building AI pipelines from raw math through to deployment
- Includes runnable Python notebooks for every concept
- Covers MCP integration, agent orchestration, and tool use
- Offers exercises that progress from basics to shipping production systems

## Architecture Overview
The repository is organized as a series of standalone modules, each in its own directory with a README, Jupyter notebooks, and supporting code. Modules are designed to be followed sequentially but can also be used as standalone references. Dependencies are minimal and Python-based.

## Self-Hosting & Configuration
- Clone the repo and install Python 3.10+ with pip
- Each module has its own requirements file for isolated dependencies
- GPU optional for most modules; recommended for vision and training sections
- Works on Linux, macOS, and Windows with WSL
- No external API keys required for core modules

## Key Features
- Covers the full AI stack from linear algebra to multi-agent systems
- Emphasizes building from scratch rather than calling libraries blindly
- Includes real-world deployment patterns (Docker, cloud, edge)
- Community-maintained with regular updates for new techniques
- Rust and TypeScript supplementary tracks for systems-level AI

## Comparison with Similar Tools
- **Fast.ai** — top-down approach vs this bottom-up from-scratch methodology
- **Coursera/Udacity** — paid and time-locked vs open and self-paced
- **Andrej Karpathy tutorials** — focused on neural nets vs full-stack AI engineering
- **LangChain docs** — framework-specific vs framework-agnostic principles
- **Papers with Code** — research-oriented vs engineering-oriented

## FAQ
**Q: Do I need a GPU to follow the course?**
A: Most modules run on CPU. GPU is recommended only for the training and vision modules.

**Q: What prior knowledge is assumed?**
A: Basic Python programming and high-school math. Each module builds on the previous one.

**Q: Can I use this for a university course?**
A: Yes, it is Apache-2.0 licensed and designed for educational use.

**Q: How often is the content updated?**
A: The community contributes regularly, with major updates roughly monthly.

## Sources
- https://github.com/rohitg00/ai-engineering-from-scratch
- https://github.com/rohitg00/ai-engineering-from-scratch#readme

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Source: https://tokrepo.com/en/workflows/asset-8adf250b
Author: Script Depot