ML at Twitter : a deep dive into Twitter's timeline /

ML at Twitter : a deep dive into Twitter's timeline /

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Bibliographic Details
Author / Creator:Montez Halasz, Cibele, on-screen presenter.
Imprint:[Place of publication not identified] : O'Reilly, 2019.
Description:1 online resource (1 streaming video file (40 min., 44 sec.))
Language:English
Subject:
Twitter (Firm)
Twitter (Firm)
Machine learning.
Online social networks -- Technological innovations.
Machine learning.
Format: E-Resource Video Streaming Video
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/13677708
Hidden Bibliographic Details
Varying Form of Title:Machine Learning at Twitter
Other authors / contributors:Banerjee, Satanjeev, on-screen presenter.
O'Reilly Artificial Intelligence Conference (2019 : New York, N.Y.) issuing body.
Notes:Title from title screen (viewed November 14, 2019).
Presenters, Cibele Montez Halasz, Satanjeev Banerjee.
Recorded at the 2019 O'Reilly Artificial Intelligence Conference in New York.
Summary:"Machine learning has allowed Twitter to drive engagement, promote healthier conversations, and deliver catered advertisements. Cibele Montez Halasz and Satanjeev Banerjee describe one of those use cases: timeline ranking. They share some of the optimizations that the team has made--from modeling to infrastructure--in order to have models that are both expressive and efficient. You'll explore the feature pipeline, modeling decisions, platform improvements, hyperparameter tuning, and architecture (alongside discretization and isotonic calibration) as well as some of the challenges Twitter faced by working with heavily text-based (sparse) data and some of the improvements the team made in its TensorFlow-based platform to deal with these use cases. Join in to gain a holistic view of one of Twitter's most prominent machine learning use cases."--Resource description page

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