Deep learning, a not-so-deep overview

So, what is this deep learning that is grabbing our attention and headlines? Let's turn to Wikipedia again for a working definition: Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using model architectures, with complex structures or otherwise, composed of multiple non-linear transformations. That sounds as if a lawyer wrote it. The characteristics of deep learning are that it is based on ANNs where the machine learning techniques, primarily unsupervised learning, are used to create new features from the input variables. We will dig into some unsupervised learning techniques in the next couple of chapters, but one can think of it as finding structure in data where no response variable is available. A simple way to think of it is the Periodic Table of Elements, which is a classic case of finding structure where no response is specified. Pull up this table online...