I've read that it's beneficial to apply certain common feature transformations on datasets before they hit machine learning models. These based on the distributions of the dataset's features; eg, applying log transforms to skewed normally-distributed features. Some examples here.
Now as I understand, a main boon of deep learning is "automatic feature engineering" (aka, "feature learning"). I know that includes feature combinations; but my hunch says that also includes learned feature transformations per the above? So when using deep networks with well-tuned hypers, can feature-transformations safely be removed from the human's responsibilities - that is, throw all this log/square/box-cox stuff away?
[Edit] Extra: does this also handle "feature selection" (deciding which inputs not to include) for you?