Sep 23–26, 2019

Deep Learning from Scratch

Bruno Goncalves (Data For Science, Inc)
9:00am12:30pm Tuesday, September 24, 2019
Location: 1E 08
Secondary topics:  Deep Learning

Who is this presentation for?

Data Scientists, Business Analysts, Data Engineers, Machine Learning Researchers

Level

Intermediate

Prerequisite knowledge

Experience with Python, numpy and Scipy

Materials or downloads needed in advance

Laptop with a scientific python distribution (such as ananconda) installed.

What you'll learn

I will guide the audience through the implementation of a subset of the functionality of Keras so that the attendants will be prepared to move on to working directly with state of the art deep learning libraries in more advanced applications.

Description

Over the past few years we have seen a convergence of two large scale trends: Big Data and Big Compute. The resulting combination of large amounts of data and abundant CPU (and GPU) cycles has brought to the forefront and highlighted the power of neural network techniques and approaches that were once thought to be too impractical.

Deep Learning, as this new wave of interest has come to be known, has made impressive and unprecedented progress on applications as diverse as Natural Language Processing, Machine Translation, Computer Vision, Robotics, etc. In this lecture, students will learn, in a hands-on way, the theoretical foundations and principal ideas underlying this burgeoning field. The code structure of the implementations provided is meant to closely resemble the way the state of the art deep learning libraries Keras is structured so that by the end of the course, students will be prepared to dive deeper into the deep learning applications of their choice.

Photo of Bruno Goncalves

Bruno Goncalves

Data For Science, Inc

Bruno Gonçalves is currently a Vice President in Data Science and Finance at JPMorgan Chase. Previously, we was a Data Science fellow at NYU’s Center for Data Science while on leave from a tenured faculty position at Aix-Marseille Université. Since completing his PhD in the Physics of Complex Systems in 2008 he has been pursuing the use of Data Science and Machine Learning to study Human Behavior. Using large datasets from Twitter, Wikipedia, web access logs, and Yahoo! Meme he studied how we can observe both large scale and individual human behavior in an obtrusive and widespread manner. The main applications have been to the study of Computational Linguistics, Information Diffusion, Behavioral Change and Epidemic Spreading.

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