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下面这些都是比较优质的深度学习的open source，和大家一起分享下。
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 will do. Training is done using the back-propagation algorithm.
Multi-GPU training support implementing data parallelism, model
parallelism, and the hybrid approach described in One weird trick for parallelizing convolutional
neural networks
三、caffe
Caffe: a fast open framework for deep learning.
http://caffe.berkeleyvision.org/
四、Theano
Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation.
http://www.deeplearning.net/software/theano
五、pylearn2
A Machine Learning library based on Theano
Deep Learning for Java, Scala & Clojure on Hadoop, Spark & GPUs
http://deeplearning4j.org
七、purine2
purine version 2. This framework is described in Purine: A bi-graph based deep learning framework
八、petuum
Petuum is a distributed machine learning framework. It aims to provide a generic algorithmic and systems interface to large scale machine learning, and takes care of difficult systems "plumbing work" and algorithmic acceleration, while simplifying the distributed
implementation of ML programs - allowing you to focus on model perfection and Big Data Analytics. Petuum runs efficiently at scale on research clusters and cloud compute like Amazon EC2 and Google GCE.
九、dmlc
A Community of Awesome Distributed Machine Learning C++ Projects
There‘s lots of treasure of DL.
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原文地址：http://blog.csdn.net/sunbaigui/article/details/46374633
