deep learning 开源库链接

Software links

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).

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

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.

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).

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

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

Last modified on February 4, 2013, at 10:52 am by Caglar Gulcehre

转载：http://deeplearning.net/deep-learning-research-groups-and-labs/