Awesome-Machine-Learning P1

List of awesome Machine Learning frameworks, libraries and software.

General-Purpose Machine Learning

• BanditLib - A simple Multi-armed Bandit library.
• Caffe - A deep learning framework developed with cleanliness, readability, and speed in mind. [DEEP LEARNING]
• CatBoost - General purpose gradient boosting on decision trees library with categorical features support out of the box. It is easy to install, contains fast inference implementation and supports CPU and GPU (even multi-GPU) computation.
• CNTK - The Computational Network Toolkit (CNTK) by Microsoft Research, is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph.
• CUDA - This is a fast C++/CUDA implementation of convolutional [DEEP LEARNING]
• CXXNET - Yet another deep learning framework with less than 1000 lines core code [DEEP LEARNING]
• DeepDetect - A machine learning API and server written in C++11. It makes state of the art machine learning easy to work with and integrate into existing applications.
• Distributed Machine learning Tool Kit (DMTK) - A distributed machine learning (parameter server) framework by Microsoft. Enables training models on large data sets across multiple machines. Current tools bundled with it include: LightLDA and Distributed (Multisense) Word Embedding.
• DLib - A suite of ML tools designed to be easy to imbed in other applications.
• DSSTNE - A software library created by Amazon for training and deploying deep neural networks using GPUs which emphasizes speed and scale over experimental flexibility.
• DyNet - A dynamic neural network library working well with networks that have dynamic structures that change for every training instance. Written in C++ with bindings in Python.
• encog-cpp
• Fido - A highly-modular C++ machine learning library for embedded electronics and robotics.
• igraph - General purpose graph library.
• Intel(R) DAAL - A high performance software library developed by Intel and optimized for Intel's architectures. Library provides algorithmic building blocks for all stages of data analytics and allows to process data in batch, online and distributed modes.
• LightGBM - Microsoft's fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
• libfm - A generic approach that allows to mimic most factorization models by feature engineering.
• MLDB - The Machine Learning Database is a database designed for machine learning. Send it commands over a RESTful API to store data, explore it using SQL, then train machine learning models and expose them as APIs.
• mlpack - A scalable C++ machine learning library.
• proNet-core - A general-purpose network embedding framework: pair-wise representations optimization Network Edit.
• ROOT - A modular scientific software framework. It provides all the functionalities needed to deal with big data processing, statistical analysis, visualization and storage.
• shark - A fast, modular, feature-rich open-source C++ machine learning library.
• Shogun - The Shogun Machine Learning Toolbox.
• sofia-ml - Suite of fast incremental algorithms.
• Stan - A probabilistic programming language implementing full Bayesian statistical inference with Hamiltonian Monte Carlo sampling.
• Timbl - A software package/C++ library implementing several memory-based learning algorithms, among which IB1-IG, an implementation of k-nearest neighbor classification, and IGTree, a decision-tree approximation of IB1-IG. Commonly used for NLP.
• Vowpal Wabbit (VW) - A fast out-of-core learning system.
• Warp-CTC - A fast parallel implementation of Connectionist Temporal Classification (CTC), on both CPU and GPU.
• XGBoost - A parallelized optimized general purpose gradient boosting library.
• LKYDeepNN - A header-only C++11 Neural Network library. Low dependency, native traditional chinese document.