Introduction to Neural Networks
by Nic Schraudolph
and Fred Cummins
Our goal is to introduce students to a powerful class of model, the
Neural Network. In fact, this is a broad term which includes many
diverse models and approaches. We will first motivate networks by
analogy to the brain. The analogy is loose, but serves to introduce
the idea of parallel and distributed computation.
We then introduce one kind of network in detail: the feedforward
network trained by backpropagation of error. We discuss model
architectures, training methods and data representation issues. We
hope to cover everything you need to know to get backpropagation
working for you. A range of applications and extensions to the basic
model will be presented in the final section of the module.
Lecture 1: Introduction
Lecture 2: The Backprop Toolbox
Lecture 3: Advanced Topics
By popular demand: Lectures 1 & 2 as a ZIP file;
Lecture 3 as a ZIP file.
© 1998-2006 Nic Schraudolph, Fred Cummins, and Jenny Orr.
All rights reserved.