Embeddings from the ground up by Matt Ranger
Channel: PyData Montreal
Date: 03/02/2020

Abstract:
Embedding methods gained notoriety when they launched NLP into a new golden age in the last decade. But embedding methods are useful for more than just text data: they can turn most discrete data structures into a continuous representation. In this talk, we'll go over the basics of embeddings, show why they work so well with text data. We'll see how to apply embedding to other problems: platform user accounts, networks, higher-level embeddings created from different sub-embeddings. We'll also go over some common pitfalls and useful linear algebra tricks that help workflows with embeddings.

About Matt:
Matt Ranger is a data scientist at Acquisio where he works on the algorithm and optimization infrastructure. Prior to that, he worked in game analytics and as a professional poker player for half a decade. His academic background is in economics.