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Fraud detection without feature engineering /
"Pamela Vagata (Stripe) explains how Stripe has applied deep learning techniques to predict fraud from raw behavioral data. Since fraud detection is a critical business problem for Stripe, the company already had a well-tuned feature-engineered model for comparison. Stripe found that the deep l...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor Corporativo: | |
| Formato: | Electrónico Congresos, conferencias Video |
| Idioma: | Inglés |
| Publicado: |
[Place of publication not identified] :
O'Reilly,
2019.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 001 | OR_on1127651167 | ||
| 003 | OCoLC | ||
| 005 | 20231017213018.0 | ||
| 006 | m o c | ||
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| 035 | |a (OCoLC)1127651167 | ||
| 037 | |a CL0501000081 |b Safari Books Online | ||
| 050 | 4 | |a QA76.9.A25 | |
| 049 | |a UAMI | ||
| 100 | 1 | |a Vagata, Pamela, |e on-screen presenter. | |
| 245 | 1 | 0 | |a Fraud detection without feature engineering / |c Pamela Vagata. |
| 264 | 1 | |a [Place of publication not identified] : |b O'Reilly, |c 2019. | |
| 300 | |a 1 online resource (1 streaming video file (40 min., 11 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 | ||
| 511 | 0 | |a Presenter, Pamela Vagata. | |
| 500 | |a Title from title screen (viewed November 14, 2019). | ||
| 518 | |a Recorded at the 2019 O'Reilly Artificial Intelligence Conference in New York. | ||
| 520 | |a "Pamela Vagata (Stripe) explains how Stripe has applied deep learning techniques to predict fraud from raw behavioral data. Since fraud detection is a critical business problem for Stripe, the company already had a well-tuned feature-engineered model for comparison. Stripe found that the deep learning model outperforms the feature-engineered model both on predictive performance and in the effort spent on data engineering, model construction, tuning, and maintenance. Join in to discover how common industry practice could shift toward deeper models trained end to end and away from labor-intensive feature engineering."--Resource description page | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Computer security. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Computer crimes |x Investigation. | |
| 650 | 0 | |a Computer networks |x Security measures. | |
| 650 | 2 | |a Computer Security | |
| 650 | 6 | |a Sécurité informatique. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Criminalité informatique |x Enquêtes. | |
| 650 | 6 | |a Réseaux d'ordinateurs |x Sécurité |x Mesures. | |
| 650 | 7 | |a Computer crimes |x Investigation. |2 fast |0 (OCoLC)fst00872065 | |
| 650 | 7 | |a Computer networks |x Security measures. |2 fast |0 (OCoLC)fst00872341 | |
| 650 | 7 | |a Computer security. |2 fast |0 (OCoLC)fst00872484 | |
| 650 | 7 | |a Machine learning. |2 fast |0 (OCoLC)fst01004795 | |
| 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/~/0636920339540/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 994 | |a 92 |b IZTAP | ||