C/C++ Machine Learning Library Top 10

원문 : https://analyticsindiamag.com/top-10-libraries-in-c-c-for-machine-learning/

 

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1. TensorFlow

2. Caffe

3. Microsoft Cognitive Toolkit

4. mlPack Library

5. DyNet

 

1| TensorFlow 146k

GitHub Stars: 146k

About: TensorFlow is a popular open-source software library for machine learning. This library has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers and developers build and deploy ML-powered applications easily.

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2| Caffe

GitHub Stars: 30.6k

About: Convolutional Architecture for Fast Feature Embedding or Caffe is a deep learning framework written in C++. The features of this library include expressive architecture, extensible code, speed and large community which fosters active development in research and industry deployments.

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3| Microsoft Cognitive Toolkit (CNTK)

GitHub Stars: 16.8k

About: Written in C++, Microsoft Cognitive Toolkit is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph. It implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers. CNTK allows users to easily realise and combine popular model types such as feed-forward DNNs, convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs). 

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4| mlpack Library

GitHub Stars: 3.3k

About: mlpack is a fast, flexible machine learning library, written in C++. The library aims to provide fast, extensible implementations of cutting-edge machine learning algorithms. It also provides simple command-line programs, Python bindings, Julia bindings, and C++ classes which can be integrated into larger-scale machine learning solutions.

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5| DyNet

GitHub Stars: 3.1k

About: Dynamic Neural Network Toolkit or DyNet is a neural network library written in C++ (with bindings in Python) and is designed to be efficient when running on either CPU or GPU. DyNet builds its computational graph on the fly, which makes variable-input and variable-output models simple to implement with high performance. The library is well-suited for techniques like natural language processing, graph structures, reinforcement learning, and other such.

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6| Shogun

GitHub Stars: 2.7k

About: Shogun is an open-source machine learning library that offers a wide range of efficient and unified machine learning methods. The library is implemented in C++ and offers automatically generated, unified interfaces to Python, Octave, Java/Scala, Ruby, C#, R, Lua. Shogun provides an easy combination of multiple data representations, algorithm classes and general-purpose tools for rapid prototyping of data pipelines. 

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7| FANN

GitHub Stars: 1216

About: Fast Artificial Neural Network (FANN) is an open-source neural network library written in C language. The library implements multilayer artificial neural networks in C with support for both fully connected and sparsely connected networks. It is easy to use, versatile, well documented, and fast. The features include backpropagation training, evolving topology training, cross-platform, and can use both floating-point and fixed-point numbers.

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8| OpenNN 

GitHub Stars: 721

About: Written in C++, Open Neural Networks (OpenNN) is an open-source neural networks library for advanced analytics. The library contains sophisticated algorithms and utilities to deal with the following artificial intelligence solutions such as classification, regression, forecasting, among others. The main advantage of this library is its high performance.

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9| SHARK Library

GitHub Stars: 334

About: Shark is a fast, modular, general open-source machine learning library written in C++ language. The library provides methods for linear and nonlinear optimisation, kernel-based learning algorithms, neural networks, and various other machine learning techniques. It serves as a powerful toolbox for real-world applications as well as for research.

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10| Armadillo

GitHub Stars: 3

About: Armadillo is a linear algebra library written in C++ language. The library provides high-level syntax and functionality deliberately similar to Matlab and is useful for algorithm development directly in C++ as well as the quick conversion of research code into production environments. The library can be used for machine learning, pattern recognition, computer vision, signal processing, bioinformatics, statistics, econometrics, among others.

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