January 2019
Intermediate to advanced
294 pages
6h 43m
English
Content preview from Machine Learning Quick Reference
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Backward propagation
The purpose of backpropagation is to update each of the weights in the network so that they cause the actual output to be closer to the target output, thereby minimizing the error for each output neuron and the network as a whole.
Let's focus on an output layer first. We are supposed to find out the impact of change in w5 on the total error.
This will be decided by 
. It is the partial derivative of Etotal with respect to w5.
Let's apply the chain rule here:
= 0.690966 – 0.9 = -0.209034
= 0.213532
InputOL1 = w5*OutputHL1 ...
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