Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
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12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
Python Data Science Handbook: full text in Jupyter Notebooks
Learn how to develop, deploy and iterate on production-grade ML applications.
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
10 Weeks, 20 Lessons, Data Science for All!
:zap: A Fast, Extensible Progress Bar for Python and CLI
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.
The fastai deep learning library
⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 or handson-mlp instead.
The fastai book, published as Jupyter Notebooks
🤖 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.
Data Apps & Dashboards for Python. No JavaScript Required.
Python programs, usually short, of considerable difficulty, to perfect particular skills.
✅（已完结）超级全面的 深度学习 笔记【土堆 Pytorch】【李沐 动手学深度学习】【吴恩达 深度学习】【大飞 大模型Agent】
100-Days-Of-ML-Code中文版
Best Practices on Recommendation Systems
End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment.
Interactive Data Visualization in the browser, from Python
The interactive graphing library for Python :sparkles:
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
💿 Free software that works great, and also happens to be open-source Python.
This open-source curriculum introduces the fundamentals of Model Context Protocol (MCP) through real-world, cross-language examples in .NET, Java, TypeScript, JavaScript, Rust and Python. Designed for developers, it focuses on practical techniques for building modular, scalable, and secure AI workflows from session setup to service orchestration.