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/