The Restricted Boltzmann Machine (RBM) has become increasingly popular of late after its success in the Netflix prize competition and other competitions. Most of the inventive work behind RBMs was done by Geoffrey Hinton. In particular the training of RBMs using an algorithm called "Contrastive Divergence" (CD). CD is very similar to gradient descent. A good consequence of the CD is its ability to "dream". Of the various machine learning methods out there, the RBM is the only one which has this capacity baked in implicitly.
http://bayesianthink.blogspot.com/2013/05/the-restricted-boltzmann-machine-rbm.html#.VCnWzikijjI
This is some Matlab code a guy made of a class he was taking. It is probably not great but if you are working in Matlab it is probably better than starting from scratch:
https://code.google.com/p/matrbm/
RBM tutorial:
http://deeplearning.net/tutorial/rbm.html#rbm
RBM in scikit-learn:
http://scikit-learn.org/stable/modules/neural_networks.html
A Practical Guide to Training Restricted Boltzmann Machines:
http://www.cs.toronto.edu/~hinton/absps/guideTR.pdf
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