Deep Learning Summer School, Montreal 2015

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About

Deep neural networks that learn to represent data in multiple layers of increasing abstraction have dramatically improved the state-of-the-art for speech recognition, object recognition, object detection, predicting the activity of drug molecules, and many other tasks. Deep learning discovers intricate structure in large datasets by building distributed representations, either via supervised, unsupervised or reinforcement learning.

The Deep Learning Summer School 2015 is aimed at graduate students and industrial engineers and researchers who already have some basic knowledge of machine learning (and possibly but not necessarily of deep learning) and wish to learn more about this rapidly growing field of research.

Videos

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From Language Modelling to Machine Translation

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Learning to Compare

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Deep Learning

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On manifolds and autoencoders

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Variational Autoencoder and Extensions

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Stacks of Restricted Boltzmann Machines

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Undirected Graphical Models

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