@gadolinis there are several other related threads that you can find with search that might add to your understanding of this concept. Here some examples:
TMosh and AI_curious
Thx for both responses. I totally get that filter parameter values are learned - that is the foundation of deep learning. Based on the “known” filters, such as edge detection, my initial impression was that somehow the deep learning also figured out those same known filters. But now I am realizing that the learned filters have no direct correlation with those “known” filters at all. Those value work in that model, and that is it. Bottom line - understanding known filters …
In week1 of the Deep Learning course 4, the edge detectors are explained. When implement the code in assignment, we just give how many filters and kernel size we will assign in each layer.
This look like, to me, the values (weights) in the kernel are not specific for edge filtering. Why the edges filters are not used? or when do I need to enforce edge filters?
Best
Try searching on Edge detector or Edge filter
https://community.deeplearning.ai/search?q=Edge%20filter