Overview

The Deep Learning Summer School 2015 was held in Montreal in August 2015.  For the 2016 edition, please follow this link.

Organizers: Yoshua Bengio, Roland Memisevic, Yann LeCun


Videos of the lectures are available at: http://videolectures.net/deeplearning2015_montreal/



Summary:
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.

This summer schools 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.

The summer school is organized within the Thematic Program on Statistical Inference, Learning, and Models for Big Data at Fields Institute.

Contact: rolandDOTmemisevic AT umontrealDOTca