Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
- gpt
- large-language-models
- llm
- python
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Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
18 Lessons to Get Started Building AI Agents
In-depth tutorials on LLMs, RAGs and real-world AI agent applications.
Translated📚 Building large models from scratch
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.
This repository is maintained by Omar Santos (@santosomar) and includes thousands of resources related to ethical hacking, bug bounties, digital forensics and incident response (DFIR), AI security, vulnerability research, exploit development, reverse engineering, and more. 🔥 Also check: https://hackertraining.org
50+ tutorials and implementations for Generative AI Agent techniques, from basic conversational bots to complex multi-agent systems.
Translated✅ (Completed) Super comprehensive deep learning notes [Tudui PyTorch] [Li Mu Dive into Deep Learning] [Andrew Ng Deep Learning] [Dafei LLM Agent]
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.
Code for Machine Learning for Trading, 3rd edition — from data sourcing to live execution.
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.
Get started with building Fullstack Agents using Gemini 2.5 and LangGraph
Sample code and notebooks for Generative AI on Google Cloud, with Gemini platform
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.
Translated"Lee Hung-yi's Deep Learning Tutorial" (recommended by Professor Hung-yi Lee 👍, the Apple Book 🍎), PDF download address: https://github.com/datawhalechina/leedl-tutorial/releases
Free MLOps course from DataTalks.Club. Register here 👇🏼 to get notified about the next cohort
llama3 implementation one matrix multiplication at a time
Your new Mentor for Data Science E-Learning.
Learn ML engineering for free in 4 months! Register here 👇🏼
Natural Language Processing Tutorial for Deep Learning Researchers
TranslatedChinese tutorial on reinforcement learning (Mushroom Book 🍄), read online at: https://datawhalechina.github.io/easy-rl/
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.