Is feature engineer an important step for a deep learning approach? I'd like to ask you if feature engineering is an important step for a deep learning approach.
By feature engineering I mean some advanced preprocessing steps, such as looking at histogram distributions and try to make it look like a normal distribution or, in the case of time series, make it stationary first (not filling missing values or normalizing the data).
I feel like with enough regularization, the deep learning models don't need feature engineering compared to some machine learning models (SVMs, random forests, etc.), but I'm not sure.
From what I believe, feature engineering is important, it's a part of the job of ML network designer.
Network designing involves

Feature engineering: What should be in the input to the network, as processed from similar or totally different data
Deciding network shape, layer shapes, types of neurons in layers, etc.
Feature engineering again (but labels), in the output, what should the output be, either regression values or classes

And possibly also tasks rather simple as mentioned in the question: filling missing values, normalising data, create pre-feeding normalisation steps in code, etc.