Deep Learning: A Tutorial
Nick Polson Vadim Sokolov
Our goal is to provide a review of deep learning methods which provide
insight into structured high-dimensional data. Rather than using shallow
additive architectures common to most statistical models, deep learning uses
layers of semi-affine input transformations to provide a predictive rule.
Applying these layers of transformations leads to a set of attributes (or,
features) to which probabilistic statistical methods can be applied. Thus, the
best of both worlds can be achieved: scalable prediction rules fortified with
uncertainty quantification, where sparse regularization finds the features.