Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
- gpt
- large-language-models
- llm
- python
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18 Lessons to Get Started Building AI Agents
- agentic-ai
- agentic-framework
- agentic-rag
- ai-agents
A latent text-to-image diffusion model
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
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
- hacking
- penetration-testing
- hacking-series
- cybersecurity
A game theoretic approach to explain the output of any machine learning model.
- interpretability
- machine-learning
- deep-learning
- gradient-boosting
A simple screen parsing tool towards pure vision based GUI agent
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]
- book
- chinese
- computer-vision
- deep-learning
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.
- machine-learning
- trading
- investment
- finance
FinGPT: Open-Source Financial Large Language Models! Revolutionize 🔥 We release the trained model on HuggingFace.
The repository provides code for running inference with the Meta Segment Anything Model 2 (SAM 2), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
TranslatedThis project changes the MXNet implementations in the original book 《动手学深度学习》 (Dive into Deep Learning) into PyTorch implementations.
- deep-learning
- deep-learning-tutorial
- pytorch
- pytorch-tutorial
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.
Translated[🔥updating ...] AI automated quantitative trading robot (fully local deployment) AI-powered Quantitative Investment Research Platform. 📃 online docs: https://ufund-me.github.io/Qbot ✨ :news: qbot-mini: https://github.com/Charmve/iQuant
- funds
- machine-learning
- pytrade
- quantitative-finance
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
- generative-ai
- llm
- vertex-ai
- langchain
Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything
- open-vocabulary-detection
- open-vocabulary-segmentation
- data-generation
- automatic-labeling-system
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.
- csharp
- java
- javascript
- mcp