Home » Source Code » Deep Learning Matlab Toolbox

Deep Learning Matlab Toolbox

2014-04-26 12:14:37
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Category Category:
Machine LearningMachine Learning MatlabMatlab


A Matlab toolbox for Deep Learning. 
Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data. It is inspired by the human brain's apparent deep (layered, hierarchical) architecture. A good overview of the theory of Deep Learning theory is Learning Deep Architectures for AI

For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.

The Next Generation of Neural Networks (Hinton, 2007) 
Recent Developments in Deep Learning (Hinton, 2010) 
Unsupervised Feature Learning and Deep Learning (Ng, 2011) 
If you use this toolbox in your research please cite:

Prediction as a candidate for learning deep hierarchical models of data (Palm, 2012)

Directories included in the toolbox 
NN/ - A library for Feedforward Backpropagation Neural Networks

CNN/ - A library for Convolutional Neural Networks

DBN/ - A library for Deep Belief Networks

SAE/ - A library for Stacked Auto-Encoders

CAE/ - A library for Convolutional Auto-Encoders

util/ - Utility functions used by the libraries

data/ - Data used by the examples

tests/ - unit tests to verify toolbox is working

For references on each library check REFS.md

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File list

Tips: You can preview the content of files by clicking file names^_^
Name Size Date
01.97 kB
.travis.yml249.00 B2014-01-12 06:39
01.97 kB
caeapplygrads.m1.19 kB2014-01-12 06:39
caebbp.m917.00 B2014-01-12 06:39
caebp.m1,011.00 B2014-01-12 06:39
caedown.m259.00 B2014-01-12 06:39
caeexamples.m754.00 B2014-01-12 06:39
caenumgradcheck.m3.53 kB2014-01-12 06:39
caesdlm.m845.00 B2014-01-12 06:39
caetrain.m1.12 kB2014-01-12 06:39
caeup.m489.00 B2014-01-12 06:39
max3d.m173.00 B2014-01-12 06:39
scaesetup.m1.89 kB2014-01-12 06:39
scaetrain.m270.00 B2014-01-12 06:39
01.97 kB
cnnapplygrads.m575.00 B2014-01-12 06:39
cnnbp.m2.09 kB2014-01-12 06:39
cnnff.m1.73 kB2014-01-12 06:39
cnnnumgradcheck.m3.35 kB2014-01-12 06:39
cnnsetup.m1.97 kB2014-01-12 06:39
cnntest.m193.00 B2014-01-12 06:39
cnntrain.m845.00 B2014-01-12 06:39
CONTRIBUTING.md544.00 B2014-01-12 06:39
01.97 kB
dbnsetup.m557.00 B2014-01-12 06:39
dbntrain.m232.00 B2014-01-12 06:39
dbnunfoldtonn.m425.00 B2014-01-12 06:39
rbmdown.m90.00 B2014-01-12 06:39
rbmtrain.m1.37 kB2014-01-12 06:39
rbmup.m89.00 B2014-01-12 06:39
LICENSE1.28 kB2014-01-12 06:39
01.97 kB
nnapplygrads.m628.00 B2014-01-12 06:39
nnbp.m1.60 kB2014-01-12 06:39
nnchecknumgrad.m704.00 B2014-01-12 06:39
nneval.m772.00 B2014-01-12 06:39
nnff.m1.81 kB2014-01-12 06:39
nnpredict.m188.00 B2014-01-12 06:39
nnsetup.m1.80 kB2014-01-12 06:39
nntest.m180.00 B2014-01-12 06:39
nntrain.m2.36 kB2014-01-12 06:39
nnupdatefigures.m1.81 kB2014-01-12 06:39
README.md8.53 kB2014-01-12 06:39
README_header.md2.20 kB2014-01-12 06:39
REFS.md950.00 B2014-01-12 06:39
01.97 kB
saesetup.m132.00 B2014-01-12 06:39
saetrain.m308.00 B2014-01-12 06:39
create_readme.sh744.00 B2014-01-12 06:39
01.97 kB
mnist_uint8.mat14.05 MB2014-01-12 06:39
01.97 kB
runalltests.m165.00 B2014-01-12 06:39
test_cnn_gradients_are_numerically_correct.m552.00 B2014-01-12 06:39
test_example_CNN.m981.00 B2014-01-12 06:39
test_example_DBN.m1.01 kB2014-01-12 06:39
test_example_NN.m3.17 kB2014-01-12 06:39
test_example_SAE.m934.00 B2014-01-12 06:39
test_nn_gradients_are_numerically_correct.m749.00 B2014-01-12 06:39
01.97 kB
allcomb.m2.56 kB2014-01-12 06:39
expand.m1.91 kB2014-01-12 06:39
flicker.m208.00 B2014-01-12 06:39
flipall.m80.00 B2014-01-12 06:39
fliplrf.m543.00 B2014-01-12 06:39
flipudf.m576.00 B2014-01-12 06:39
im2patches.m313.00 B2014-01-12 06:39
isOctave.m108.00 B2014-01-12 06:39
makeLMfilters.m1.85 kB2014-01-12 06:39
myOctaveVersion.m169.00 B2014-01-12 06:39
normalize.m97.00 B2014-01-12 06:39
patches2im.m242.00 B2014-01-12 06:39
randcorr.m283.00 B2014-01-12 06:39
randp.m2.03 kB2014-01-12 06:39
rnd.m49.00 B2014-01-12 06:39
sigm.m48.00 B2014-01-12 06:39
sigmrnd.m126.00 B2014-01-12 06:39
softmax.m256.00 B2014-01-12 06:39
tanh_opt.m54.00 B2014-01-12 06:39
visualize.m1.05 kB2014-01-12 06:39
whiten.m183.00 B2014-01-12 06:39
zscore.m137.00 B2014-01-12 06:39
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it is very usefull

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Deep Learning Matlab Toolbox (14.06 MB)

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