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Online evaluation of machine learning models /
"Ted Dunning (MapR) offers a survey of useful ways to evaluate models in the real world, breaking the problem of evaluation apart into operational and function evaluation and demonstrating how to do each without unnecessary pain and suffering. You'll learn about decoy and canary models, no...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor Corporativo: | |
| Formato: | Electrónico Congresos, conferencias Video |
| Idioma: | Inglés |
| Publicado: |
[Place of publication not identified] :
O'Reilly Media,
[2019]
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Dunning, Ted, |d 1956- |e on-screen presenter. | |
| 245 | 1 | 0 | |a Online evaluation of machine learning models / |c Ted Dunning. |
| 264 | 1 | |a [Place of publication not identified] : |b O'Reilly Media, |c [2019] | |
| 264 | 4 | |c ©2019 | |
| 300 | |a 1 online resource (1 streaming video file (40 min., 31 sec.)) | ||
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| 337 | |a video |b v |2 rdamedia | ||
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| 511 | 0 | |a Presenter, Ted Dunning. | |
| 500 | |a Title from title screen (viewed February 21, 2020). | ||
| 520 | |a "Ted Dunning (MapR) offers a survey of useful ways to evaluate models in the real world, breaking the problem of evaluation apart into operational and function evaluation and demonstrating how to do each without unnecessary pain and suffering. You'll learn about decoy and canary models, nonlinear latency histogramming, model-delta diagrams, and more. These techniques may sound arcane, but each is simple at heart and doesn't require any advanced mathematics to understand. Along the way, he shares exciting visualization techniques that will help make differences strikingly apparent. This session was recorded at the 2019 O'Reilly Strata Data Conference in San Francisco."--Resource description page | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Data structures (Computer science) | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Electronic data processing. | |
| 650 | 0 | |a Cloud computing. | |
| 650 | 2 | |a Artificial Intelligence | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Structures de données (Informatique) | |
| 650 | 6 | |a Intelligence artificielle. | |
| 650 | 6 | |a Infonuagique. | |
| 650 | 7 | |a artificial intelligence. |2 aat | |
| 650 | 7 | |a Artificial intelligence. |2 fast |0 (OCoLC)fst00817247 | |
| 650 | 7 | |a Cloud computing. |2 fast |0 (OCoLC)fst01745899 | |
| 650 | 7 | |a Data structures (Computer science) |2 fast |0 (OCoLC)fst00887978 | |
| 650 | 7 | |a Electronic data processing. |2 fast |0 (OCoLC)fst00906956 | |
| 650 | 7 | |a Machine learning. |2 fast |0 (OCoLC)fst01004795 | |
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