This document discusses backpropagation in a multi-layer neural network. It provides an example calculation, showing the input values, output values, expected values, and error at each layer as backpropagation is applied to reduce the error between the network's predictions and expected values. The error is reduced with each pass through the network.

1. 1 Multi Layer Neural NetworkErrorBackpropogation-
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Backpropogation Example

2. 2 Multi Layer Neural NetworkErrorBackpropogation-
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3. 3 Multi Layer Neural NetworkErrorBackpropogation-
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4. 4 Multi Layer Neural NetworkErrorBackpropogation-
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V1=0.17 -> v1=0.1715 V2=0.36 -> v2=0.3622 V3=0.4851 -> v3=0.5127
Y1=0.542 -> y1=0.5428 Y2=0.589 -> y2=0.5896 Y3=0.619 -> y3=0.6254
E=d3-y3=0.9-0.619=0.281 -> e=d3-y3=0.9-.6245=0.2746
Error is getting reduced after each passed