Popular GitHub Repositories for Boosting Algorithms

100 Days of ML Coding

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.

A game theoretic approach to explain the output of any machine learning model.

? 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.

Machine learning, in numpy

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

FinRL: Financial Reinforcement Learning. ?

PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.

Minimal and clean examples of machine learning algorithms implementations

Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.