Deep Learning Library finishing in various programming languages

Source: http://www.teglor.com/b/deep-learning-libraries-language-cm569

Python

1. Theano is a Python library for defining and evaluating mathematical expressions with numerical arrays. It makes it easy-to-write deep learning algorithms in Python. The top of the Theano many more libraries is built.

   1. keras is a minimalist, highly modular neural network library in the spirit of Torch, Written in Python, which uses Theano under the hood for optimized tensor manipulation on the GPU and CPU.

   2. pylearn2 is a library that wraps a lot of models and training algorithms such as Stochasti C Gradient descent that is commonly used in deep learning. Its functional libraries is built on top of Theano.

   3. lasagne is a lightweight library to build and train neural networks in Theano. It is governed by simplicity, transparency, modularity, pragmatism, focus and restraint principles.

   4. blocks a framework that helps your build neural network models on top of Theano.

3. Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors. Google's Deepdream is the based on Caffe Framework. This framework is a bsd-licensed C + + library with Python Interface.

5. Nolearn contains a number of wrappers and abstractions around existing neural network libraries, most notably Las Agne, along with a few machine learning utility modules.

7. Gensim is deep learning toolkit implemented in Python programming language intended for handling large text collections, U Sing efficient algorithms.

9. Chainer Bridge the gap between algorithms and implementations of deep learning. Its powerful, flexible and intuitive and are considered as the flexible framework for deep learning.

11. Deepnet is a gpu-based python implementation of deep learning algorithms like Feed-forward neural Nets, Restricted Boltzma nn machines, deep belief Nets, autoencoders, deep Boltzmann machines and convolutional neural Nets.

13. Hebel is a library for deep learning with neural networks in Python using GPUs acceleration with CUDA through Pycuda. It implements the most important types of neural network models and offers a variety of different activation functions and Training methods such as momentum, Nesterov momentum, dropout, and early stopping.

15. Cxxnet is fast, concise, distributed deep learning framework based on Mshadow. It is a lightweight and easy extensible C++/cuda Neural Network Toolkit with friendly Python/matlab interface for training and prediction.

17. Deeppy is a pythonic deep learning framework built on top of NumPy.

19. Deeplearning is the deep learning library, developed with C + + and Python.

21. Neon is Nervana's Python based deep learning framework.

Matlab

1. convnet convolutional Neural net is a type of deep learning classification algorithm s, that can learn useful features from raw data by themselves and are performed by tuning its weighs.

2. deeplearntoolbox is a matlab/octave toolbox for deep learning and includes deep belief Ne TS, stacked autoencoders, convolutional neural nets.

3. cuda-convnet is a fast C++/cuda implementation of convolutional (or more generally, Feed-forward) Neural networks. It can model arbitrary layer connectivity and network depth. Any directed acyclic graph of layers would do. Training is done using the backpropagation algorithm.

4. matconvnet  is a MATLAB Toolbox implementing convolutional neural Networks (CNNs) For computer vision applications. It is simple, efficient, and can run and learn State-of-the-art CNNs

Cpp

2. Eblearn is an open-source C + + Library of machine learning by New York University's machine learning labs, led by Yann LeCun . In particular, implementations of convolutional neural networks with energy-based models along with a GUI, demos and tutor IALs.

4. Singa is designed to being general to implement the distributed training algorithms of existing systems. It is supported by Apache software Foundation.

6. NVIDIA DIGITS is a new system for developing, training and visualizing deep neural networks. It puts the power of deep learning to an intuitive browser-based interface, so that data scientists and researchers can Quickly design the best DNN for their data using real-time network behavior visualization.

8. Intel®deep Learning Framework provides a unified framework for intel®platforms accelerating deep convolutional neural Ne Tworks.

Java

1. N-dimensional Arrays for Java (ND4J) are scientific computing libraries for the JVM. They is meant to being used in production environments, which means routines is designed to run fast with minimum RAM Requi Rements.

2. Deeplearning4j is the first Commercial-grade, Open-source, distributed deep-learning Library written for Java and Scala. It is designed to being used in business environments, rather than as a of the tool.

3. ENCOG is a advanced machine learning framework which supports support Vector machines,artificial neural Networks, genetic Programming, Bayesian Networks, Hidden Markov Models, genetic programming and genetic algorithms are supported.

Javascript

1. Convnet.js is a Javascript library for training deep learning models (mainly neural Networks) entirely in a browser. No software requirements, no compilers, no installations, no GPUs, no sweat.

Lua

1. Torch is a scientific computing the framework with wide support for machine learning algorithms. It's easy-to-use and efficient, fast scripting language, Luajit, and an underlying C/CUDA implementation. Torch is based on Lua programming language.

Julia

1. Mocha is a deep learning framework for Julia, inspired by the C + + framework Caffe. Efficient implementations of general stochastic gradient solvers and common layers in Mocha could being used to train deep/ Shallow (convolutional) neural networks, with (optional) unsupervised pre-training via (stacked) auto-encoders. Its best feature include Modular architecture, high-level Interface, portability with speed, compatibility and many more.

Lisp

1. Lush (Lisp Universal Shell) is a object-oriented programming language designed for researchers, experimenters, and Enginee Rs interested in large-scale numerical and graphic applications. It comes with the rich set of deep learning libraries as a part of the machine learning libraries.

Haskell

1. Dnngraph is a deep neural network model generation DSL in Haskell.

. NET

1. Accord.net is a. NET machine learning framework combined with audio and image processing libraries completely written in C #. It is a complete framework for building Production-grade Computer vision, computer audition, signal processing and statist ICS Applications

R

1. Darch package can is used for generating neural networks with many layers (deep architectures). Training methods includes a pre Training with the contrastive divergence method and a fine tuning with common known Traini ng algorithms like backpropagation or conjugate gradient.
2. Deepnet implements some deep learning architectures and neural network algorithms, including Bp,rbm,dbn,deep Autoencoder a nd so on.

Reference: http://www.csdn.net/article/2015-09-15/2825714

Deep Learning Library finishing in various programming languages