Popular GitHub Repositories for Policy Optimization Algorithms

100 Days of ML Coding

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.

Collection of various algorithms in mathematics, machine learning, computer science and physics implemented in C++ for educational purposes.

Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.

Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG.

A complete daily plan for studying to become a machine learning engineer.

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

? Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained

Collection of various algorithms in mathematics, machine learning, computer science, physics, etc implemented in C for educational purposes.

Turns Data and AI algorithms into production-ready web applications in no time.

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

Python SDK for Agent AI Observability, Monitoring and Evaluation Framework. Includes features like agent, llm and tools tracing, debugging multi-agentic system, self-hosted dashboard and advanced analytics with timeline and execution graph view

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

The "Python Machine Learning (1st edition)" book code repository and info resource

A hyperparameter optimization framework

FinRL: Financial Reinforcement Learning. ?