深度网络学习软件资源

Theano – CPU/GPU symbolic expression compiler in python (from LISA lab at University of Montreal)

Pylearn2 - Pylearn2 is a library designed to make machine learning research easy.

Torch – provides a Matlab-like environment for state-of-the-art machine learning algorithms in lua (from Ronan Collobert, Clement Farabet
and Koray Kavukcuoglu)

Deep Learning Tutorials – examples of how to do Deep Learning with Theano (from LISA lab at University of Montreal)

DeepLearnToolbox – A Matlab toolbox for Deep Learning (from Rasmus Berg Palm)

Cuda-Convnet – 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 will do. Training is done using the back-propagation algorithm.

Deep Belief Networks. Matlab code for learning Deep Belief Networks (from Ruslan Salakhutdinov).

RNNLM- Tomas Mikolov’s Recurrent Neural Network based Language models Toolkit.

RNNLIB-RNNLIB is a recurrent neural network library for sequence learning problems. Applicable to most types of spatiotemporal
data, it has proven particularly effective for speech and handwriting recognition.

matrbm. Simplified version of Ruslan Salakhutdinov’s code, by Andrej Karpathy (Matlab).

这个使用了一下附带的Demo，刚开始学习，可视使用这个。

deepmat- Deepmat, Matlab based deep learning algorithms.

Estimating Partition Functions of RBM’s. Matlab code for estimating partition functions of Restricted Boltzmann
Machines using Annealed Importance Sampling (from Ruslan Salakhutdinov).

Learning Deep Boltzmann Machines Matlab code for training and fine-tuning Deep Boltzmann Machines (from Ruslan Salakhutdinov).

The LUSH programming language and development environment, which is used @ NYU for deep convolutional networks

Eblearn.lsh is a LUSH-based machine learning library for doing Energy-Based Learning. It includes code for “Predictive
Sparse Decomposition” and other sparse auto-encoder methods for unsupervised learning. Koray Kavukcuoglu provides Eblearn code
for several deep learning papers on this page.

Nengo-Nengo is a graphical and scripting based software package for simulating large-scale neural systems.

Eblearn is a C++ machine learning library with a BSD license for energy-based learning, convolutional networks, vision/recognition
applications, etc. EBLearn is primarily maintained by Pierre Sermanet at NYU.

cudamat is a GPU-based matrix library for Python. Example code for training Neural Networks and Restricted Boltzmann Machines
is included.

Gnumpy is a Python module that interfaces in a way almost identical to numpy, but does its computations on your
computer’s GPU. It runs on top of cudamat.

The CUV Library (github link)
is a C++ framework with python bindings for easy use of Nvidia CUDA functions on matrices. It contains an RBM implementation, as well as annealed importance sampling code and code to calculate the partition function exactly (from AIS
lab at University of Bonn).

3-way factored RBM and mcRBM is
python code calling CUDAMat to train models of natural images (from Marc’Aurelio Ranzato).

Matlab code for training conditional RBMs/DBNs and factored
conditional RBMs (from Graham Taylor).

CXXNET – An implementation of deep convolution neural network in C++.

mPoT is python code using CUDAMat and gnumpy to train models of natural images (from Marc’Aurelio
Ranzato).

neuralnetworks is a java based gpu library for deep learning algorithms.

If your software belongs here, email us and let us know.

来源：http://deeplearning.net/software_links/