[MRG] Generic multi layer perceptron

Currently I am implementing `layers_coef_` to allow for any number of hidden layers.

This pull request is to implement the generic Multi-layer perceptron as part of the GSoC 2014 proposal. 

The expected time to finish this pull request is June 15

The goal is to extend Multi-layer Perceptron to support more than one hidden layer and to support having a pre-training phase (initializing weights through Restricted Boltzmann Machines) and a fine-tuning phase; and write its documentation.

This directly follows from this pull-request: https://github.com/scikit-learn/scikit-learn/pull/2120
This is not doing memory preallocation, just building python list with 0 as a place hold. The memory usage does not come from the numpy arrays with shape `(n_hidden[layer_idx], batch_size)` that are going to populate that list later.