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Ch. 20 – Neural Networks

Ch. 20 – Neural Networks. Supplemental slides for CSE 327 Prof. Jeff Heflin. A Neuron. Perceptron Learning. function PERCEPTRON-LEARNING( examples,network) returns a perceptron hypothesis

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Ch. 20 – Neural Networks

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  1. 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 einexamples doErr y[e] – g(in)Wj  Wj +   Err  g’(in)  xj[e]until some stopping criteria is satisfiedreturn NEURAL-NET-HYPOTHESIS(network) From Figure 20.21, p. 742

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