  We give a covering number bound for deep learning networks that is
independent of the size of the network. The key for the simple analysis is that
for linear classifiers, rotating the data doesn't affect the covering number.
Thus, we can ignore the rotation part of each layer's linear transformation,
and get the covering number bound by concentrating on the scaling part.
