深度学习资料整理（源码）

近这两年里deep learning技术在图像识别和跟踪等方面有很大的突破，是一大研究热点，里面涉及的数学理论和应用技术很值得深入研究，这系列博客总结了深度学习的博客，原理等资料，供大家学习讨论。

一、深度学习资料整理（博客类）

二、深度学习资料整理（文章类）

三、深度学习资料整理（软件资源）

四、深度学习原理（关键技术总结，斯坦福大学UFLDL教程）

五、深度学习资料整理（深度神经网络理解）

六、深度学习资料整理（压缩感知）

七、深度学习资料整理（从感知机到深度网络）

八、深度学习资料整理（源码）

八、深度学习资料整理（源码）

收集了一些Deep Learning的源代码。主要是Matlab和C++的，当然也有python的。放在这里，后续遇到新的会持续更新。下表没有的也欢迎大家提供，以便大家使用和交流。谢谢。

 

最近一次更新：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 Networks of stacked RBM'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手写字符识别C++代码

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++ 11 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 problems.

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 Video

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/

参考：http://blog.csdn.net/zouxy09

未完，持续更新中，欢迎补充！更多关注：http://blog.csdn.net/tiandijun，欢迎交流！