I've already seen many articles about this topic and Backpropagation In Convolutional Neural Networks by Jefkine (5 September 2016) seems to be the best. Although, as author said,
For the purposes of simplicity we shall use the case where the input image is grayscale i.e single channel C = 1.
Also, he uses stride = 1 and assumes only 1 filter, for the same purpose.
These are the final equations for backpropagation (taken from the article):
The autor's notation explained:
I figured out how to do the forward pass with stride, depth and more filters, but couldn't do the same with the backpropagation. Do you know where to put the 3rd dimension, stride and filters number in those equations?
Also, how to backpropagate the bias (assuming there's 1 bias per filter)?
Thanks in advance.