The convolution stage, where we apply different filters \( \boldsymbol{W} \) in order to reduce the dimensionality of an image, adds, in addition to the weights and biases (to be trained by the back propagation algorithm) that define the filters, two new hyperparameters, the so-called padding \( P \) and the stride \( S \).