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A tutorial on Deep Learning

A tutorial on Deep Learning

Complex probabilistic models of unlabeled data can be created by combining simpler models. Mixture models are obtained by averaging the densities of simpler models and "products of experts" are obtained by multiplying the densities together and renormalizing. A far more powerful type of combination is to form a "composition of experts" by treating the values of the latent variables of one model as the data for learning the next model. The first half of the tutorial will show how deep belief nets -- directed generative models with many layers of hidden variables -- can be learned one layer at a time by composing simple, undirected, product of expert models that only have one hidden layer. It will also explain why composing directed models does not work. Deep belief nets are trained as... Show More

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