Data Science Study Session @Blue Apron

You are invited to join us for a data science study session. On Monday, February 13, Bruno Gonçalves, Moore-Sloan Fellow at NYU's Center for Data Science, will lead a practical introduction on the architecture, training, advantages, and pitfalls of neural networks.

The venue this month is provided by our friends at Blue Apron. We hope to see you there!

Machine(s) Learning with Neural Networks

The data deluge we are currently witnessing presents both opportunities and challenges. Never before have so many aspects of our world been so thoroughly quantified as now. One of the main beneficiaries of the current data glut has been data intensive approaches, such as Neural Networks in general and Deep Learning in particular, with many important new practical applications arising in the last couple of years.

In this tutorial we provide a practical introduction to the most important concepts of neural networks, as we gradually implement simple neural networks from scratch to perform simple tasks such as character recognition. Our emphasis will be on understanding the architecture, training, advantages and pitfalls of practical neural networks.

Participants are expected to have a basic familiarity of calculus and linear algebra, as well as working proficiency with Python.

Biography:

Bruno Gonçalves is currently a Moore-Sloan Fellow at NYU's Center for Data Science. With a background in Physics and Computer Science his career has revolved around the use of datasets from sources as diverse as Apache web logs, Wikipedia edits, Twitter posts epidemiological reports and Census data to analyze and model Human Behavior and Mobility. More recently he has focused on the application of machine learning and neural network techniques to analyze large geolocated datasets. He is the editor of "Social Phenomena: From Data Analysis to Models" (Springer, 2015) and a co-author of the forthcoming "Twitterology: The Social Science of Twitter" (Springer, 2018).