To expand on this a bit: You need a baseline feature representation for structure data for "traditional" problems. What it DOES is shifts the problem to layer

To expand on this a bit: You need a baseline feature representation for structure data for "traditional" problems. What it DOES is shifts the problem to layer architectures which is a hard problem in and of itself: What vonnik is talking about and what's already been addressed in a sibling comment was images. "Features" are the pixels which handles generating the "baseline representation" needed for feature engineering. Another thing of note here is it just highlights that ETL is important. Eg: image sizes, window lengths for time series etc. Normalization is also still hugely important for many neural network problems. Smerity says it best: http://smerity.com/articles/2016/architectures_are_the_new_feature_engineering.html http://smerity.com/articles/2016/architectures_are_the_new_f...