Reasoning behind using Deep Learning on non-local data I understand the using of deep learning for data that have &quot;local&quot; structure, for example, images/videos/texts, as the convolutional layers reduce the amount of dimensions.
However, I saw that some people use it on non-local data, as on databases for example, here or here on the titanic database.
My question is: as just one hidden layer with enough neurons within can theoretically creates as many dimensions as we want, why would one use several hidden layers/deep learning instead of just using a single bigger hidden layer?
It appear that I forgot one point of the ANN, at least forgot one of its effects : the activation function.

It is true that for linear activation, multi-layer can be reduce to a single-one, but with a non-linear function, 


  a two-layer neural network can be proven to be a universal function approximator.


Sources

However, it is true that I dont understand now why to use more than two hidden layers...