Neural Networks and Deep Learning

Underlying theory, the range of applications, and learning from very large data sets. The following topics will be discussed: Deep hierarchies and learning mechanisms in humans; Artificial neural networks; Deep vs. shallow architectures; Representation learning; Principal and Independent component analysis; Sparse representations; Convolutional neural networks; Restricted Boltzmann Machines; Deep Recurrent Networks; Applications to pattern recognition, speech recognition, natural language processing, classification and clustering.

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