  Deep learning has achieved a great success in many areas, from computer
vision to natural language processing, to game playing, and much more. Yet,
what deep learning is really doing is still an open question. There are a lot
of works in this direction. For example, [5] tried to explain deep learning by
group renormalization, and [6] tried to explain deep learning from the view of
functional approximation. In order to address this very crucial question, here
we see deep learning from perspective of mechanical learning and learning
machine (see [1], [2]). From this particular angle, we can see deep learning
much better and answer with confidence: What deep learning is really doing? why
it works well, how it works, and how much data is necessary for learning. We
also will discuss advantages and disadvantages of deep learning at the end of
this work.
