So im reading through this link on Neural Nets and regression and on this page the part about back propagation which reads:
To perform backpropagation and make the network learn, you simply compare ŷ to the ground-truth value of y and adjust the weights and biases of the network until error is minimized, much as you would with a classifier. Root-means-squared-error (RMSE) could be the loss function.
My question is how much do i adjust the weights and biases by?
And in what order?
Now I know the second part of my question obvious answer is 'backwards' but what i mean is
if im changing the weights from back to front. how can i know that if i change a weight near the input so it wont drastically mess up the Y? So I would have to go back to the weight near the output and change that so the weight near the input dosent make such a drastic change.
Dose this make sense? if not I will clarify.