Deep Learning and Modern NLP

In this talk, Zak will cover the fundamental building blocks of neural network architectures and how they are utilized to tackle problems in modern natural language processing. Topics covered will include an overview of language vector representations, text classification, named entity recognition, and sequence to sequence modeling approaches. An emphasis will be placed on the shape of these types of problems from the perspective of deep learning architectures. This will help to develop an intuition for identifying which neural network techniques are the most applicable to new problems that practitioners might encounter.

About our speaker:
Zachary S. Brown is currently a Lead Data Scientist at S&P Global Market Intelligence, where he leads a small team with a focus on modern natural language processing and its application to content classification and data extraction. Zak received his PhD in Computational Physics from The College of William & Mary in 2014, where he calculated features of the strong force using simulations on high performance computing clusters. He has a passion for education, and has led and contributed to data science initiatives at Capital One, Cloudera, and most recently at S&P Global. In his free time, he helps to organize our Data Science Community Meetup, and occasionally teaches college physics courses in RVA.