AI Lab

A personal learning lab for exploring machine learning, deep learning, and AI. This repository is organized by concept (regression, classification, neural networks, LLMs, etc.) to mirror how these topics interconnect.

Quick Start

1. Set up Python environment

/opt/homebrew/bin/python3.12 -m venv .env
source .env/bin/activate
pip install -r requirements.txt

2. Explore topics

Navigate to any topic folder and explore:

cd foundations              # Start here: math & Python basics
cd machine_learning         # Linear models, trees, sklearn
cd neural_networks          # Neural networks & deep learning
cd generative_ai            # GANs, diffusion, transformers
cd llm                      # Large language models
cd rag                      # Retrieval-augmented generation
cd agents                   # Autonomous agents

Each topic folder has:

• labs/ — runnable scripts and examples
• notebooks/ — interactive Jupyter explorations
• README.md — topic overview and prerequisites
• common/ — shared utilities for that topic
• data/ — example datasets

3. Run examples

# Run a script
python machine_learning/labs/1_gradient_descent_1d.py

# Run a notebook
jupyter notebook foundations/python/1_oop.ipynb

Learning Path (Suggested)

1. Foundations — Math & Python fundamentals
2. Machine Learning — Linear models, trees, classical algorithms
3. Neural Networks — MLPs, backprop, first deep learning models
4. Generative AI — VAE, GAN, diffusion, transformers
5. LLMs — Large language models, attention, transformers at scale
6. RAG — Retrieval-augmented generation with vector databases
7. Agents — Autonomous agents, reasoning, tool use

Key Conventions

• Scripts in labs/ are numbered (e.g., 1_gradient_descent_1d.py) and runnable directly
• Notebooks in notebooks/ are for exploration and interactive learning
• Shared utilities live in each topic's labs/common/ folder
• Data files in labs/data/ use relative paths for reproducibility
• Each topic has a README.md explaining learning goals and prerequisites

Resources & References

See each topic's README.md for curated resources. Popular ones:

• AI roadmap
• Google ML Crash Course
• deeplearning.ai Specializations
• Hands-On Machine Learning (Aurélien Géron)
• The Hundred-Page ML Book