I have read that in deep networks you can engineer each layer for a particular purpose with regards to feature learning. I'm wondering how that is actually done and how it is trained?

In addition doesn't this conflict with the idea of deep-networks having "automatic" feature extraction?

For example consider this:

Lets say you want to detect stop signs. How would you teach a deep network to do this in a layer-wise fashion?

People write about one layer of a Deep Network does edge detection, but how?


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