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Deep Learning源代码收集

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。
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/

转载注明来源:CV视觉网 » Deep Learning源代码收集

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