Tweeted By @fchollet

DL is applicable when you're doing *pattern recognition*: when you have data that lies on a smooth manifold, along which samples can be interpolated. And you're going to need a dense sampling of your manifold as training data in order to fit a parametric approximation of it

— François Chollet (@fchollet) March 3, 2020