深度学习源码搜集
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推荐四个Deep learning的matlab代码,方便大家做实验。
1. Hinton DBN code
这个不用说了,开山鼻祖Hinton的代码,下载链接:Hinton DBN coce。代码可读性一般,后面我会把我修改后的代码公布出来。
2. Deep learning toolbox
Mathworks上的代码,纯正Matlab代码,CNN部分写的不错,下载链接:DL Toolbox matlab 。
3. MEDAL
UC Berkely的一位大神写的,全部matlab代码,github项目主页:MEDAL 。 值得一提的是,这是一个用OO风格写成的matlab代码。
4. Covolutional DBN
Github项目主页:CDBN 。
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/
=============================================================
hinton Matlab: http://www.cs.toronto.edu/~hinton/MatlabForSciencePaper.html
Hinton 学生改写的CUDA版本: https://code.google.com/p/cuda-convnet/
Matlab toolbox版本: https://github.com/rasmusbergpalm/DeepLearnToolbox
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