[D] What's the intuition behind certain CNN architectures?

My knowledge about CNNs is relatively basic. I know what they do, but I have not enough experience to understand the different choices in architectures (besides the obvious improvements, like a residual block). For what I do I never needed to rely on CNNs, so whenever I worked with them I just used some existing CNN structures, but now I would like to learn more about it, so that I can optimize my current models.

So for example looking at …

cnn intuition machinelearning