3 practical thoughts on why deep learning performs so well

The superior performance of deep learning relative to other machine learning methodologies has been commented in several forums and magazines in recent times. I would like to post today on three reasons that, in my opinion, are the basis of this commented superiority. I am not the first to comment on this issue [1], and for sure I won't be the last. But I would like to extend the discussion by taking into account the practical reasons behind the success of deep learning. Hence if you are looking for its theoretical background you would do better to look for it in the deep learning literature, where the "Hamiltonian of the spin glass model [2]", the exploitation of compositional functions to cope with the curse of dimensionality [3], their capability to best represent the simplicity of physics-based functions [4], and the flattening of the data manifolds [5] have been proposed.