Popular GitHub Repositories for Gradient Descent

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

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

强化学习中文教程（蘑菇书?），在线阅读地址：https://datawhalechina.github.io/easy-rl/

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学

An elegant PyTorch deep reinforcement learning library.

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Pytorch implementation of convolutional neural network visualization techniques

High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

Fit interpretable models. Explain blackbox machine learning.

Gorgonia is a library that helps facilitate machine learning in Go.

PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....

A PyTorch Library for Meta-learning Research

A collection of research papers on decision, classification and regression trees with implementations.

Fast and Easy Infinite Neural Networks in Python

Minimal implementation of clipped objective Proximal Policy Optimization (PPO) in PyTorch

Optax is a gradient processing and optimization library for JAX.

A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).