Deep learning is a buzz phrase that refers to a branches of machine learning that involve multiple, interactive layers of nonlinear data transformations. It’s deep because there are multiple layers, and it’s deep learning because the layers can communicate with one another. Deep learning techniques have been growing in accuracy and success and are interesting to study in themselves.
As a data scientist, I’m drawn to deep learning because it involves some of my favorite things: multidimensional math, linear algebra in particular; cool algorithms; the chain rule; and surprises – which in the applied math world means “nonlinear effects”.
So my goal is to document adventures in deep learning in the next months, and make this blog a place where interested people can learn along with me.