Awesome Python Data Science

An Open Source Machine Learning Framework for Everyone

Open Source Computer Vision Library

Caffe: a fast open framework for deep learning.

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow

PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）

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.

A toolkit for making real world machine learning and data analysis applications in C++

cuDF - GPU DataFrame 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.

Fit interpretable models. Explain blackbox machine learning.

mlpack: a fast, header-only C++ machine learning library

C++ library for audio and music analysis, description and synthesis, including Python bindings

High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.

Shōgun

An efficient video loader for deep learning with smart shuffling that's super easy to digest

A Python package for manipulating 2-dimensional tabular data structures

ThunderSVM: A Fast SVM Library on GPUs and CPUs

C++-based high-performance parallel environment execution engine (vectorized env) for general RL environments.

Distributed machine learning platform

Universal model exchange and serialization format for decision tree forests

ThunderGBM: Fast GBDTs and Random Forests on GPUs

Framework and Library for Distributed Online Machine Learning

TensorFlow ROCm port

Marsyas - Music Analysis, Retrieval and Synthesis for Audio Signals

An engine for high performance multi-agent environments with very large numbers of agents, along with a set of reference environments

Audio features extraction

Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.