Abstract

PART 1: Introduction to Deep Learning & Deep Belief Nets (PDF Parts 1 & 2)

Geoffrey Hinton
University of Toronto
Canadian Institute for Advanced Research

Overview of the tutorial
• A brief history of deep learning.
• How to learn multi-layer generative models of unlabelled
data by learning one layer of features at a time.
– What is really going on when we stack RBMs to form a
deep belief net.
• How to use generative models to make discriminative
training methods work much better for classification and
regression.
• How to modify RBMs to deal with real-valued input.