Deep Learning

Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence.

Website of organization dedicated to all things deep learning:

http://deeplearning.net/

Multiple tutorials and a wealth of other info:

http://deeplearning.net/tutorial/intro.html


Peer review paper:

Theoretical results suggest that in order to learn the kind of complicated functions that can represent high- level abstractions (e.g. in vision, language, and other AI-level tasks), one may need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers or in complicated propositional formulae re-using many sub-formulae. Searching the parameter space of deep architectures is a difficult task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable success, beating the state-of-the-art in certain areas. This paper discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks.

http://www.iro.umontreal.ca/~bengioy/papers/ftml.pdf


Deep learning implementations in many languages:

http://deeplearning.net/software_links/

Restricted Boltzmann machine

Learning to use RBM's is on my todo list...I'll update when I get around to it.  RBM's are just one technique for deep learning.

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