Conceptual question on CNN and any multi layer neural network (Part 2)
There's an amazing book called "Deep Learning with Python" by Francois Chollet that you could refer to. To answer your question:


You usually add high number of layers and check where the validation accuracy halts and training loss decreases rapidly. This means your network starts to overfit and it's easy to fine tune your network from there on with the help of regularization. 
You determine the layers of your network depending upon the complexity of the problem and the resolution of the images as well. Bigger pictures need larger networks. The filters need to be increasing as the network increases and the size of the feature map should decrease in an ideal network. I don't know much about strides though. Max_Pooling is the most common solution instead of strides.


I could go on about Max_Pooling but that won't answer your question.