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In this talk  John Myles White surveys some basic methods for analyzing data in a streaming manner. He focuses on using stochastic gradient descent (SGD) to fit models to data sets that arrive in small chunks, discussing some basic implementation issues and demonstrating the effectiveness of SGD for problems like linear and logistic regression as well as matrix factorization.

He also describes how these methods allow ML systems to adapt to user data in real-time. This talk was recorded at the New York Open Statistical Programming meetup at Knewton.  Source: G33KTalks 

Also see SlideShare: http://www.slideshare.net/g33ktalk/john-myles-white-keynote

ABOUT THE SPEAKER:  231314234 john myles whiteJohn is one of the primary developers of Julia, a new language for technical computing. He is currently developing the statistical and machine learning infrastructure for Julia.  In addition, he is one of the residents at Hacker School’s Summer 2013 program. 

John recently finished his Ph.D. at Princeton, where he developed models of human decision-making. During grad school, John co-wrote Machine Learning for Hackers and Bandit Algorithms for Website. Starting in the fall, John will be a research scientist at Facebook.

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Blog Publisher / Head of Data Science Search

Founder & Head of Data Science Search at Starbridge Partners, LLC.