Deep Learning Source Code Collection-Continuous update ... __ depth study

Deep Learning Source code Collection-Continuous update ...

Zouxy09@qq.com

Http://blog.csdn.net/zouxy09

Collected some source code for deep learning. The main is MATLAB and C + +, of course, there are python. Put it here and follow up with new updates that will continue. The table below is also welcome to be available for everyone to use and communicate. Thank you.

Last update: 2013-9-22

Theano

http://deeplearning.net/software/theano/

Code from:http://deeplearning.net/

Deep Learning Tutorial Notes and code

Https://github.com/lisa-lab/DeepLearningTutorials

Code From:lisa-lab

A Matlab Toolbox for Deep Learning

Https://github.com/rasmusbergpalm/DeepLearnToolbox

Code From:rasmusberg Palm

Deepmat

Matlab Code for Restricted/deep Boltzmannmachines and Autoencoder

Https://github.com/kyunghyuncho/deepmat

Code From:kyunghyun Cho http://users.ics.aalto.fi/kcho/

Training a deep autoencoder or a classifieron mnist digits

Http://www.cs.toronto.edu/~hinton/MatlabForSciencePaper.html

Code From:ruslan Salakhutdinov and Geoffhinton

Cnn-convolutional Neural Network class

http://www.mathworks.cn/matlabcentral/fileexchange/24291

Code From:matlab

Neural Network for recognition Ofhandwritten Digits (CNN)

Http://www.codeproject.com/Articles/16650/Neural-Network-for-Recognition-of-Handwritten-Digi

Cuda-convnet

A Fast C++/cuda implementation ofconvolutional Neural networks

http://code.google.com/p/cuda-convnet/

Matrbm

A small library that can train Restrictedboltzmann machines, and also Deep belief the Networks of stacked ' s.

http://code.google.com/p/matrbm/

Code From:andrej karpathy

Exercise from UFLDL Tutorial:

Http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial

And Tornadomeet ' s bolg:http://www.cnblogs.com/tornadomeet/tag/deep%20learning/

and Https://github.com/dkyang/UFLDL-Tutorial-Exercise

Conditional Restricted Boltzmann Machines

Http://www.cs.nyu.edu/~gwtaylor/publications/nips2006mhmublv/code.html

From Graham Taylor http://www.cs.nyu.edu/~gwtaylor/

Factored Conditional restricted boltzmannmachines

Http://www.cs.nyu.edu/~gwtaylor/publications/icml2009/code/index.html

From Graham Taylor http://www.cs.nyu.edu/~gwtaylor/

Marginalized stacked denoising autoencodersfor Domain adaptation

Http://www1.cse.wustl.edu/~mchen/code/mSDA.tar

Code from:http://www.cse.wustl.edu/~kilian/code/code.html

Tiled convolutional Neural Networks

Http://cs.stanford.edu/~quocle/TCNNweb/pretraining.tar.gz

Http://cs.stanford.edu/~pangwei/projects.html

TINY-CNN:

A C++11 Implementation of convolutionalneural networks

Https://github.com/nyanp/tiny-cnn

Mycnn

https://github.com/aurofable/18551_Project/tree/master/server/2009-09-30-14-33-myCNN-0.07

Adaptive deconvolutional Network Toolbox

Http://www.matthewzeiler.com/software/DeconvNetToolbox2/DeconvNetToolbox.zip

http://www.matthewzeiler.com/

Deep Learning handwritten character recognition C + + code

http://download.csdn.net/detail/lucky_greenegg/5413211

from:http://blog.csdn.net/lucky_greenegg/article/details/8949578

Convolutionalrbm.m

A Matlab/mex/cuda-mex implementation ofconvolutional restricted Boltzmann machines.

Https://github.com/qipeng/convolutionalRBM.m

From:http://qipeng.me/software/convolutional-rbm.html

Rbm-mnist

C + + implementation of Geoff Hinton ' sdeep Learning matlab code

Https://github.com/jdeng/rbm-mnist

Learning Deep Boltzmann Machines

Http://web.mit.edu/~rsalakhu/www/code_DBM/code_DBM.tar

Http://web.mit.edu/~rsalakhu/www/DBM.html

Code provided by Ruslan Salakhutdinov

Efficient sparse coding algorithms

Http://web.eecs.umich.edu/~honglak/softwares/fast_sc.tgz

Http://web.eecs.umich.edu/~honglak/softwares/nips06-sparsecoding.htm

Linear Spatial Pyramid Matching usingsparse coding for Image classification

Http://www.ifp.illinois.edu/~jyang29/codes/CVPR09-ScSPM.rar

Http://www.ifp.illinois.edu/~jyang29/ScSPM.htm

Spams

(SPArse modeling Software) is anoptimization Toolbox for solving various SPArse estimation.

http://spams-devel.gforge.inria.fr/

Sparsenet

Sparse Coding simulation Software

http://redwood.berkeley.edu/bruno/sparsenet/

Fast Dropout Training

Https://github.com/sidaw/fastdropout

Http://nlp.stanford.edu/~sidaw/home/start

Deep Learning of invariant Features viasimulated fixations in

Http://ai.stanford.edu/~wzou/deepslow_release.tar.gz

http://ai.stanford.edu/~wzou/

Sparse filtering

Http://cs.stanford.edu/~jngiam/papers/NgiamKohChenBhaskarNg2011_Supplementary.pdf

K-means

Http://www.stanford.edu/~acoates/papers/kmeans_demo.tgz

Others:

http://deeplearning.net/software_links/

Original address: http://blog.csdn.net/zouxy09/article/details/11910527