CSE599 Lecture6: Neural networks
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Table of Contents
Neural networks
Networks types
History
McCulloch–Pitts “neuron”
Associative memories
wij for a single memory
Associative-memory example
Multiple memories
Hopfield networks
Learning networks
Perceptrons
Linear separability
Linear separability (con’t)
Perceptron learning
Multilayer perceptrons
Gradient-descent learning
Backpropagation of errors
Backpropagation of errors (con’t)
Other feedforward networks
Other feedforward networks (con’t)
Recurrent networks
Recurrent networks (con’t)
Unsupervised networks
Biology and neural networks
Author:
Chris Diorio
Email:
diorio@cs.washington.edu
Home Page:
http://www.cs.washington.edu/education/courses/599/99sp/