Deep Learning Libraries by Language

最新推荐文章于 2024-03-27 19:46:15 发布

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最新推荐文章于 2024-03-27 19:46:15 发布 · 1k 阅读

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#deep #learning #library #languages

本文列举了不同编程语言中用于深度学习的库，包括Python的Theano、Keras、Pylearn2等，Matlab的ConvNet、DeepLearnToolBox，C++的eblearn、SINGA，Java的Deeplearning4j，JavaScript的Convnet.js，Lua的Torch，Julia的Mocha，Lisp的Lush，Haskell的DNNGraph以及.NET的Accord.NET等。这些框架涵盖了从表达式速度到模块化的各种特性，旨在加速和简化深度学习的实现。

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. On the top of the Theano many more libraries are built.

1.1 Keras is a minimalist, highly modular neural network library in the spirit of Torch, written in Python, that uses Theano under the hood for optimized tensor manipulation on GPU and CPU.

1.2 Pylearn2 is a library that wraps a lot of models and training algorithms such as Stochastic Gradient Descent that are commonly used in Deep Learning. Its functional libraries are built on top of Theano.

1.3 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.

1.4 Blocks is a framework that helps you build neural network models on top of Theano.

2. 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 based on Caffe Framework. This framework is a BSD-licensed C++ library with Python Interface.

3. nolearn contains a number of wrappers and abstractions around existing neural network libraries, most notably Lasagne, along with a few machine learning utility modules.

4. Gensim is deep learning toolkit implemented in python programming language intend