# S. Mandayam/ ANN/ECE Dept./Rowan University Artificial Neural Networks 0909.560.01/0909.454.01 Fall 2004 Shreekanth Mandayam ECE Department Rowan University.

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S. Mandayam/ ANN/ECE Dept./Rowan University Artificial Neural Networks 0909.560.01/0909.454.01 Fall 2004 Shreekanth Mandayam ECE Department Rowan University http://engineering.rowan.edu/~shreek/spring04/ann/ Lecture 4 October 4, 2004

S. Mandayam/ ANN/ECE Dept./Rowan UniversityPlan Multilayer Perceptron Recall: Architecture Signal Flow Learning rule - Backpropagation Final Project Assignments Lab Project 2

S. Mandayam/ ANN/ECE Dept./Rowan University Multilayer Perceptron (MLP): Architecture 1 1 1         x1x1 x2x2 x3x3 y1y1 y2y2   w ji w kj w lk Input Layer Hidden Layers Output Layer Inputs Outputs

S. Mandayam/ ANN/ECE Dept./Rowan University MLP: Signal Flow Function signal Error signal    Computations at each node, j Neuron output, y j Gradient vector, dE/dw ji Forward propagation Backward propagation

S. Mandayam/ ANN/ECE Dept./Rowan UniversityBackpropagation i j k Left Right Notation At a node j, y i (n) w ji (n) v j (n)  (.) y j (n) d j (n) e j (n)

S. Mandayam/ ANN/ECE Dept./Rowan University Backprop. (contd) i j k Left Right Notation If node j is a hidden node, y i (n) w ji (n) v j (n)  (.) y j (n) w kj (n) v k (n)  (.) y k (n) d k (n) e k (n)

S. Mandayam/ ANN/ECE Dept./Rowan University MLP Training Forward Pass Fix w ji (n) Compute y j (n) Backward Pass Calculate  j (n) Update weights w ji (n+1) i j k Left Right i j k Left Right x y

S. Mandayam/ ANN/ECE Dept./Rowan University MLP’s in Matlab http://engineering.rowan.edu/~shreek/spring04/ann/demos/mlp.m

S. Mandayam/ ANN/ECE Dept./Rowan University Lab Project 2 http://engineering.rowan.edu/~shreek /spring04/ann/lab2.htmlhttp://engineering.rowan.edu/~shreek /spring04/ann/lab2.html UCI Machine Learning Repository: http://www.ics.uci.edu/~mlearn/MLRepository.html Face Recognition: Generate images

S. Mandayam/ ANN/ECE Dept./Rowan UniversitySummary

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