Presented By
O’Reilly + Intel AI
Put AI to Work
April 15-18, 2019
New York, NY

Recurrent neural networks for time series analysis

Bruno Goncalves (JPMorgan Chase & Co.)
1:45pm5:15pm Tuesday, April 16, 2019
Implementing AI
Location: Sutton South
Secondary topics:  Models and Methods, Temporal data and time-series

Who is this presentation for?

data scientists

Level

Intermediate

Prerequisite knowledge

Basic Python programming

Materials or downloads needed in advance

Laptop with Keras and Python 3 installed.

What you'll learn

Recurrent neural networks - Review of feed-forward networks - Introduction to recursion - Types of recurrent neural networks - Your first recurrent network Gated recurrent units - Advantages of recursion -Controlling information flow - Gates and internal logic Long short-term memory - Remembering the past - Avoiding vanishing gradients - Memory cells All code and slides presented during the tutorial will be made available in the course GitHub repository https://github.com/bmtgoncalves/RNN

Description

From the closing price of the stock market to the number of clicks per second on a web page or the sequence of venues visited by a tourist exploring a new city, time series and temporal sequences of discrete events are everywhere around us. Understanding them requires taking into account the sequence of values seen in previous steps and even long-term temporal correlations.

Join Bruno Gonçalves to learn how to use recurrent neural networks, a technique originally developed for natural language processing, to model and forecast time series. You’ll also discover the advantages and disadvantages of recurrent neural networks with respect to more traditional approaches.

Photo of Bruno Goncalves

Bruno Goncalves

JPMorgan Chase & Co.

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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