  Deep learning has sparked a network of mutual interactions between different
disciplines and AI. Naturally, each discipline focuses and interprets the
workings of deep learning in different ways. This diversity of perspectives on
deep learning, from neuroscience to statistical physics, is a rich source of
inspiration that fuels novel developments in the theory and applications of
machine learning. In this perspective, we collect and synthesize different
intuitions scattered across several communities as for how deep learning works.
In particular, we will briefly discuss the different perspectives that
disciplines across mathematics, physics, computation, and neuroscience take on
how deep learning does its tricks. Our discussion on each perspective is
necessarily shallow due to the multiple views that had to be covered. The
deepness in this case should come from putting all these faces of deep learning
together in the reader's mind, so that one can look at the same problem from
different angles.
