In addition to Python, machine learning programs written in these languages are also very

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Python has become one of the most commonly used languages in artificial intelligence and other related sciences due to its ease of use and its powerful library of tools. Especially in machine learning, is already the most favored language of major projects.

In fact, in addition to Python, there is no shortage of developers in other languages to write excellent machine learning programs. Here are some of the open-source machine learning projects that some of these individuals consider worthy of attention. Due to limited space, consideration is given to multiple periods of consolidation.

1, C

darknet--Neural Network Framework

Darknet is an open-source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computing.

CCV --Computer Vision Library

CCV is the abbreviation of C-based/cached/core Computer Vision Library, which is a modern computer vision library.

CCV is an application-driven algorithm library, such as a fast detection algorithm for static objects (such as a human face), an accurate detection algorithm for some objects that are not easily positioned (such as a cat), an algorithm for detecting art text, a tracking algorithm for long-term targets, and a feature point detection algorithm.

2. C + +

cntk-- Deep Learning Toolkit

Microsoft's Open source Deep learning toolkit, which describes neural networks as a graph-based structure, leaves nodes representing input or network parameters, and other node calculation steps.

The CNTK not only makes it easy to implement deep neural networks (DNN), convolutional neural Networks (CNN), cyclic neural networks (RNN), and short and long memory units (LSTM), but also supports multiple GPU combinations, server auto-differentiation, and parallel random gradient descent (SGD) learning.

Caffe -- deep learning Framework

Caffe is a clear and efficient deep learning framework, and the model and corresponding optimization are given in textual form rather than code, and the definition, optimization and pre-training weights of the model are given, which makes it easy to get started immediately. At the same time, it can run the best models and massive amounts of data, it is also easy to extend to new tasks and settings.

kaldi--Speech Recognition Toolkit

Kaldi is a language recognition toolkit written in C + + designed for use by speech recognition researchers and is easy to modify and extend. It provides algorithms in the most general form possible at the beginning of the design to ensure their scalability.

3. Go

cloudforest-- Decision tree Combination algorithm

A fast, flexible, multi-threaded decision tree for pure Go, allowing some relevant algorithms to be used for classification, regression, feature selection, and structural analysis of heterogeneous data with missing values. It allows for faster training times and is ideal for modern processors to learn binary.

4. Java

corenlp-- Natural Language Processing tool

CORENLP is a set of tools developed by Stanford University on Natural language processing that is powerful and simple to use. It can enter the original text, give the basic form of the word, their part of speech, company, the name of the person, explain the date, time and quantity and so on. It was originally developed for English, but it is now supported in Chinese.

h2o--machine learning and predictive analytics framework

H2O is a distributed, memory-based, extensible machine learning and predictive analytics framework for building large-scale machine learning models in an enterprise environment. It works seamlessly with large data technologies such as Hadoop and Spark, using a familiar interface with developers. It also provides implementations of many popular algorithms, such as GBM, Random Forest, deep neural Networks, Word2vec, and so on.

deeplearning4j--Distributed Neural Network library

DEEPLEARNING4J is a distributed neural network library written in Java and Scala that integrates Hadoop and Spark and is designed for use in a business environment that runs on distributed GPUs and CPUs. It's plug and play, allowing developers to quickly integrate deep learning features into the APP

DEEPLEARNING4J includes a distributed, multi-threaded deep learning framework, and a common single-threaded deep learning framework.

5. Javascript

natural--Natural Language Processing tool

The Natural language processing tool used by node. JS supports lexical analysis, stemming, classification, speech, inverse document frequency weight evaluation, WordNet, string similarity, and so on.

convnetjs--Deep Learning Library

Convnetjs is a JavaScript-based deep learning library that allows you to train deep networks in your browser. It can help deep learning beginners to understand the algorithm faster and more intuitively, through some simple Demo to the user's most intuitive explanation.