I agree with spaced-out. A neural net can capture all those smaller eigenvectors in the signal that are routinely thrown away during traditional feature engineering, like what you describe. When the number of training samples grows big enough, those factors with marginal contribution become significant and allow higher levels of accuracy in prediction or classification than are possible when curating features manually.

Deep nets are here to stay. They're just not magic bullets that solve all problems equally well, especially those when training data is minimal.