文件列表:

code (0, 2013-05-09)
code\CRBM (0, 2013-05-09)
code\CRBM\crbmGradients.m (2576, 2013-05-09)
code\CRBM\crbmHExpectation.m (592, 2013-05-09)
code\CRBM\crbmSampleK.m (230, 2013-05-09)
code\CRBM\crbmVExpectation.m (643, 2013-05-09)
code\CRBM\crbmVisualize.m (377, 2013-05-09)
code\CRBM\createCRBM.m (796, 2013-05-09)
code\RBM (0, 2013-05-09)
code\RBM\createRBM.m (1118, 2013-05-09)
code\RBM\rbmGradients.m (2070, 2013-05-09)
code\RBM\rbmHExpectation.m (597, 2013-05-09)
code\RBM\rbmSampleK.m (993, 2013-05-09)
code\RBM\rbmVExpectation.m (692, 2013-05-09)
code\RBM\rbmVisualize.m (538, 2013-05-09)
code\RBM\trainRBM.m (1071, 2013-05-09)
code\examples (0, 2013-05-09)
code\examples\trainCRBMMNIST.m (1092, 2013-05-09)
code\examples\trainRBMMNIST.m (941, 2013-05-09)
code\util (0, 2013-05-09)
code\util\bernoulli_sample.m (81, 2013-05-09)
code\util\convcut.m (460, 2013-05-09)
code\util\kthMap.m (113, 2013-05-09)
code\util\sigmoid.m (99, 2013-05-09)
data (0, 2013-05-09)
data\mnist (0, 2013-05-09)
doc (0, 2013-05-09)
doc\dbn_tutorial.pdf (153512, 2013-05-09)
doc\supplementary_reading_list.pdf (128497, 2013-05-09)

Deep Learning in Matlab To run demonstration: Download the MNIST dataset at http://www.cs.nyu.edu/~roweis/data/mnist_all.mat Then run rbm = trainRBMMNIST(location) with the location of the data as input.

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