21 Lessons, Get Started Building with Generative AI

Examples and guides for using the OpenAI API

12 Lessons to Get Started Building AI Agents

Learn how to develop, deploy and iterate on production-grade ML applications.

A collection of notebooks/recipes showcasing some fun and effective ways of using Claude.

aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)

Learn OpenCV : C++ and Python Examples

A guidance language for controlling large language models.

This repository provides tutorials and implementations for various Generative AI Agent techniques, from basic to advanced. It serves as a comprehensive guide for building intelligent, interactive A…

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

This repository delivers end-to-end, code-first tutorials covering every layer of production-grade GenAI agents, guiding you from spark to scale with proven patterns and reusable blueprints for re…

Welcome to the Llama Cookbook! This is your go to guide for Building with Llama: Getting started with Inference, Fine-Tuning, RAG. We also show you how to solve end to end problems using Llama mode…

Get started with building Fullstack Agents using Gemini 2.5 and LangGraph

Sample code and notebooks for Generative AI on Google Cloud, with Gemini on Vertex AI

A multi-voice TTS system trained with an emphasis on quality

This repository contains implementations and illustrative code to accompany DeepMind publications

[WIP] Resources for AI engineers. Also contains supporting materials for the book AI Engineering (Chip Huyen, 2025)

The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery 🧑‍🔬

Practice your pandas skills!

Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.

Official inference library for Mistral models

AI Observability & Evaluation

This repo is meant to serve as a guide for Machine Learning/AI technical interviews.

A curated list of applied machine learning and data science notebooks and libraries across different industries (by @firmai)

Data augmentation for NLP

Python code for common Machine Learning Algorithms

Neo4j graph construction from unstructured data using LLMs

Notebooks using the Hugging Face libraries 🤗

GenAI Cookbook