I think the whole "you don't need to engineer features" thing can be a bit oversold, in that you don't need to build explicit feature extractors, but you do sti

I think the whole "you don't need to engineer features" thing can be a bit oversold, in that you don't need to build explicit feature extractors, but you do still need to design your architecture to fit your problem domain. It's more general than traditional feature extraction, but if you keep working on a single problem for long enough you may find that while deep learning can be a great starting point, you eventually want to add more hand-coded features if you really have some insight that the neural net isn't fully capturing. I'm personally quite interested to see if we find meaningful ways of constructing differentiable feature extractors that we can train conjointly with a neural net.