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Each function includes description. Please check it!
It provides deep learning tools of deep belief networks (DBNs) of stacked restricted Boltzmann machines (RBMs). It includes the Bernoulli-Bernoulli RBM, the Gaussian-Bernoulli RBM, the contrastive divergence learning for unsupervised pre-training, the sparse constraint, the back projection for supervised training, and the dropout technique.
The sample codes with the MNIST dataset are included in the mnist folder. Please, see readme.txt in the mnist folder.
Hinton et al, Improving neural networks by preventing co-adaptation of feature detectors, 2012.
Lee et al, Sparse deep belief net model for visual area V2, NIPS 2008.
http://read.pudn.com/downloads103/sourcecode/math/421402/drtoolbox/techniques/train_rbm.m__.htm |