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ML at Twitter : a deep dive into Twitter's timeline /
"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 modeli...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor Corporativo: | |
| Formato: | Electrónico Congresos, conferencias Video |
| Idioma: | Inglés |
| Publicado: |
[Place of publication not identified] :
O'Reilly,
2019.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Montez Halasz, Cibele, |e on-screen presenter. | |
| 245 | 1 | 0 | |a ML at Twitter : |b a deep dive into Twitter's timeline / |c Cibele Montez Halasz, Satanjeev Banerjee. |
| 246 | 3 | |a Machine Learning at Twitter | |
| 264 | 1 | |a [Place of publication not identified] : |b O'Reilly, |c 2019. | |
| 300 | |a 1 online resource (1 streaming video file (40 min., 44 sec.)) | ||
| 336 | |a two-dimensional moving image |b tdi |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 337 | |a video |b v |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 500 | |a Title from title screen (viewed November 14, 2019). | ||
| 518 | |a Recorded at the 2019 O'Reilly Artificial Intelligence Conference in New York. | ||
| 511 | 0 | |a Presenters, Cibele Montez Halasz, Satanjeev Banerjee. | |
| 520 | |a "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 | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 610 | 2 | 0 | |a Twitter (Firm) |
| 610 | 2 | 7 | |a Twitter (Firm) |2 fast |0 (OCoLC)fst01794912 |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Online social networks |x Technological innovations. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Réseaux sociaux (Internet) |x Innovations. | |
| 650 | 7 | |a Machine learning. |2 fast |0 (OCoLC)fst01004795 | |
| 700 | 1 | |a Banerjee, Satanjeev, |e on-screen presenter. | |
| 711 | 2 | |a O'Reilly Artificial Intelligence Conference |d (2019 : |c New York, N.Y.) |j issuing body. | |
| 856 | 4 | 0 | |u https://learning.oreilly.com/videos/~/0636920339571/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 994 | |a 92 |b IZTAP | ||