Stanford University's deep learning source code can be used for pattern recognition and prediction, and is relatively stable.

Filename	Size	Update
cnnConvolve.m	3702	2013-04-12
cnnExercise.m	9289	2013-04-12
sparseAutoencoderLinearCost.m	4261	2013-04-11
softmaxExercise.m	4796	2013-04-10
checkStackedAECost.m	1745	2013-04-10
softmaxCost.m	1281	2013-04-10
softmaxPredict.m	849	2013-04-10
sparseAutoencoderCost.m	3865	2013-04-09
stlExercise.m	5514	2013-04-07
feedForwardAutoencoder.m	1304	2013-04-07
loadMNISTImages.m	835	2013-04-03
pca_gen.m	6377	2013-04-03
pca_2d.m	3426	2013-04-01
trainMNIST.m	1830	2013-03-11
train.m	5796	2013-03-11
sparseAutoencoderCost - 副本.m	4104	2013-03-11
computeNumericalGradient.m	1115	2013-03-11
sampleIMAGES.m	2120	2013-03-10
linearDecoderExercise.m	5346	2011-07-16
displayColorNetwork.m	1233	2011-05-22
stackedAEExercise.m	7412	2011-05-13
softmaxTrain.m	1891	2011-05-10
stackedAEPredict.m	1296	2011-05-09
stackedAECost.m	3100	2011-05-09
params2stack.m	1174	2011-05-09
stack2params.m	1653	2011-05-09
display_network.m	2647	2011-04-29
sampleIMAGESRAW.m	696	2011-04-29
loadMNISTLabels.m	516	2011-04-25
checkNumericalGradient.m	1982	2011-01-05
initializeParameters.m	622	2011-01-05
cnnPool.m	1641	2013-04-12

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