ML at Twitter : a deep dive into Twitter's timeline / Cibele Montez Halasz, Satanjeev Banerjee.

Format:

Conference/Event

Video

Author/Creator:

Montez Halasz, Cibele, on-screen presenter.

Contributor:

Banerjee, Satanjeev, on-screen presenter.

Conference Name:

O'Reilly Artificial Intelligence Conference (2019 : New York, New York)., issuing body.

Language:

English

Subjects (All):

Machine learning.

Online social networks--Technological innovations.

Online social networks.

Twitter (Firm).

Physical Description:

1 online resource (1 streaming video file (40 min., 44 sec.)) : digital, sound, color

Other Title:

Machine Learning at Twitter

Place of Publication:

[Place of publication not identified] : O'Reilly, 2019.

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.

Participant:

Presenters, Cibele Montez Halasz, Satanjeev Banerjee.

Notes:

Title from title screen (viewed November 14, 2019).

Recorded at the 2019 O'Reilly Artificial Intelligence Conference in New York.

OCLC:

1127651168