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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 ;)
⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 or handson-mlp instead.
The fastai book, published as Jupyter Notebooks
Neural Networks: Zero to Hero
Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media
TranslatedAn introductory LLM tutorial for developers, the Chinese version of Andrew Ng's large model course series
🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
50+ tutorials and implementations for Generative AI Agent techniques, from basic conversational bots to complex multi-agent systems.
Python programs, usually short, of considerable difficulty, to perfect particular skills.
Translated✅ (Completed) Super comprehensive deep learning notes [Tudui PyTorch] [Li Mu Dive into Deep Learning] [Andrew Ng Deep Learning] [Dafei LLM Agent]
Learn OpenCV : C++ and Python Examples
Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.
End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment.
TranslatedThe pytorch handbook is an open-source book that aims to help friends who want to use PyTorch for deep learning development and research get started quickly. All of the PyTorch tutorials it contains have been tested to ensure they run successfully.
Code for Machine Learning for Trading, 3rd edition — from data sourcing to live execution.
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
TranslatedCode implementation of 《统计学习方法》 (Statistical Learning Methods)
TranslatedThis project changes the MXNet implementations in the original book 《动手学深度学习》 (Dive into Deep Learning) into PyTorch implementations.
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.
Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.
One has no future if one couldn't teach themself.
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 model family and using them on various provider services
Get started with building Fullstack Agents using Gemini 2.5 and LangGraph
[WIP] Resources for AI engineers. Also contains supporting materials for the book AI Engineering (Chip Huyen, 2025)