1.rohitg00 / ai-engineering-from-scratch
Learn AI Engineering! Learn it. Build it. Ship it for others.
Ranked by Attention Score: stars gained over the last 7 complete days
47 repositories by Attention Score · Oct 27, 2026 – Nov 2, 2026 (UTC)
Learn AI Engineering! Learn it. Build it. Ship it for others.
List of Computer Science courses with video lectures.
Local UI to run and train LLMs and diffusion models. Supports GGUF, MLX, Qwen3.8, DeepSeek-V4.1, MiniMax-H3, Gemma 4, FLUX and more.
Open, source-backed catalog of computer-use agents: reviewed papers, GUI models, datasets, benchmarks, tools, and reusable JSONL/SQLite data.
Scikit-learn for Recursive Self-improvement (RSI). A modular framework for recursive harness evolution.
SGLang is a high-performance serving framework for large language models and multimodal models.
TavernGym and TavernBench: a faithful, full-content Hearthstone Battlegrounds simulator with an RL interface, and a 960-task decision benchmark. Code and data for "Build, Don't Play".
Code for Machine Learning for Trading, 3rd edition — from data sourcing to live execution.
🦆 Microduck 飞特 HD1910 机器人鸭复刻:单脚独立实机、现成动作模型、中文中控、自制 HAT/IMU 与三步教程。正常复刻无需训练。Feetech HD1910 Microduck replica: one-legged balance, ready-to-use models, Chinese console, custom HAT/IMU & 3-step build guides. No training required.
This is the homepage of a new book entitled "Mathematical Foundations of Reinforcement Learning."
Infrastructure for continually self‑improving agents
The absolute trainer to light up AI agents.
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
FinGPT: Open-Source Financial Large Language Models! Revolutionize 🔥 We release the trained model on HuggingFace.
A standard API for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)
🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
Free Course : Data, Machine Learning, Deep Learning and AI Engineering, the long way round: the math, the code, the systems and the security. 173 lessons, 166 runnable labs, no shortcuts. Free under CC BY-NC 4.0.
A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers.
Bullet Physics SDK: real-time collision detection and multi-physics simulation for VR, games, visual effects, robotics, machine learning etc.
PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
《李宏毅深度学习教程》(李宏毅老师推荐👍,苹果书🍎),PDF下载地址:https://github.com/datawhalechina/leedl-tutorial/releases
High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.
强化学习中文教程(蘑菇书🍄),在线阅读地址:https://datawhalechina.github.io/easy-rl/
Agent Reinforcement Trainer: train multi-step agents for real-world tasks using GRPO. Give your agents on-the-job training. Reinforcement learning for Qwen3.6, GPT-OSS, Llama, and more!
PyBullet Gymnasium environments for single and multi-agent reinforcement learning of quadcopter control
A standard API for multi-agent reinforcement learning environments, with popular reference environments and related utilities
Quantitative analysis, strategies and backtests
Python Implementation of Reinforcement Learning: An Introduction
A course in reinforcement learning in the wild
Reinforcement Learning environments based on the 1993 game Doom :godmode:
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
A collection of environments for autonomous driving and tactical decision-making tasks
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
PyBullet CartPole and Quadrotor environments—with CasADi symbolic a priori dynamics—for learning-based control and RL
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.
Debugging, monitoring and visualization for Python Machine Learning and Data Science
Trading and Backtesting environment for training reinforcement learning agent or simple rule base algo.
TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.
A ROS/ROS2 Multi-robot Simulator for Autonomous Vehicles
C++-based high-performance parallel environment execution engine (vectorized env) for general RL environments.
Modular Reinforcement Learning (RL) library (implemented in PyTorch, JAX, and NVIDIA Warp) with support for Gymnasium/Gym, NVIDIA Isaac Lab, MuJoCo Playground and other environments
Gibson Environments: Real-World Perception for Embodied Agents
👉 CARLA resources such as tutorial, blog, code and etc https://github.com/carla-simulator/carla