I posted this on Software Engineering and was told that it might be better here and on AI. I'm currently reading Artificial Intelligence: A Guide to Intelligent Systems by Michael Negnevitsky (3rd edition) and came across this example of weight updates in a neural network. The equation for updating the weight in this network is: wi(p+1) = wi(p) + alpha * xi * e(p) where e(p) is the difference between the expected and calculated error, x is the input parameter, and alpha is the learning rate.The initial weights of the second epoch aren't different from the initial weights of the first. Aren't they supposed to be updated? What's happening between the final weights of an epoch and the initial weights of the next epoch?
Right, I think I see your confusion (I was confused too here for a minute). It seems that it is doing stochastic gradient descent (i.e, updating the weights after each input/observation).
So the way to read the table is row by row:
initial weights -> final weights (row 1)
then final weights of row 1 become initial weights of row 2 and so on so forth.