Deep Learning is amazing. But why is Deep Learning so successful? Is Deep Learning just old-school Neural Networks on modern hardware? Is it just that we have so much data now the methods work better? Is Deep Learning just a really good at finding features. Researchers are working hard to sort this out.

Recently it has been shown that [1]

*Unsupervised Deep Learning implements the Kadanoff Real Space Variational Renormalization Group (1975)*

This means the success of Deep Learning is intimately related to some very deep and subtle ideas from Theoretical Physics. In this post we examine this.

#### Unsupervised Deep Learning: AutoEncoder Flow Map

An AutoEncoder is a Unsupervised Deep Learning algorithm that learns how to represent an complex image or other data structure $latex X $. There are several kinds of AutoEncoders; we care about so-called Neural Encoders–those using Deep Learning techniques to reconstruct the data:

The simplest Neural Encoder…

View original post 2,232 more words