1. Supplemental slides for CSE 327 Prof. Jeff Heflin
Ch. 20 – Neural Networks
Supplemental slides for CSE 327
Prof. Jeff Heflin

2. A Neuron

3. Perceptron Learning
function PERCEPTRON-LEARNING(examples,network) returns a perceptron hypothesis
inputs: examples, a set of examples with input x and output y network, a perceptron with weights Wj and activation function g
repeat for each e in examples do Err  y[e] – g(in) Wj  Wj +   Err  g’(in)  xj[e] until some stopping criteria is satisfied return NEURAL-NET-HYPOTHESIS(network)
From Figure 20.21, p. 742