> The huge triumph of DL has been figuring out that as long as you can pose a problem in a differentiable way and you can obtain a sufficient amount of data, yo

> The huge triumph of DL has been figuring out that as long as you can pose a problem in a differentiable way and you can obtain a sufficient amount of data, you can efficiently tackle it with a function approximator that can be optimized with first order methods - from that, flows everything. But this only defines under which circumstances Deep Learning will produce a solution, this doesn't tell us why DL has been so effective. There is little point in an algorithm guaranteed to converge if that algorithm has little applicability. To me, DL's triumph was being able to make advances in fields that were at a standstill with traditional methods (CV is an obvious one, but natural language processing is a good one as well). This in turn has attracted enough attention that DL is now being considered for a very wide variety of problems. Obviously, DL won't be successful on all of them but that's science as usual.