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Practical feature engineering /
"Feature engineering is generally the section that gets left out of machine learning books, but it's also the most important part of successful models, even in today's world of deep learning. While academic courses on machine learning focus on gradients and the latest flavor of recurr...
| Clasificación: | Libro Electrónico |
|---|---|
| Formato: | Electrónico 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, |e on-screen presenter. | |
| 245 | 1 | 0 | |a Practical feature engineering / |c Ted Dunning. |
| 264 | 1 | |a [Place of publication not identified] : |b O'Reilly Media, |c 2019. | |
| 300 | |a 1 online resource (1 streaming video file (38 min., 49 sec.)) | ||
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| 511 | 0 | |a Presenter, Ted Dunning. | |
| 500 | |a Title from title screen (viewed July 23, 2020). | ||
| 520 | |a "Feature engineering is generally the section that gets left out of machine learning books, but it's also the most important part of successful models, even in today's world of deep learning. While academic courses on machine learning focus on gradients and the latest flavor of recurrent network, Ted Dunning (MapR) explores the techniques that practitioners in the real world are seeking out better features and figuring out how to extract value using a variety of time-honored (and occasionally exceptionally clever) heuristics. In a sense, feature engineering is the Rodney Dangerfield of machine learning, never getting any respect. It is, however, the task that will get you the most value for time spent in terms of model performance. This work is not just the work of the data scientist. Good features encode business realities as well and are the cross-product of good business sense and good data engineering. This session is from the 2019 O'Reilly Strata Conference in New York, NY."--Resource description page | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 611 | 2 | 0 | |a O'Reilly Strata Data Conference |d (2019 : |c New York, N.Y.) |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Computer engineering |x Data processing. | |
| 650 | 0 | |a Business enterprises |x Data processing. | |
| 650 | 0 | |a Information technology |x Management. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Entreprises |x Informatique. | |
| 650 | 6 | |a Technologie de l'information |x Gestion. | |
| 650 | 7 | |a Business enterprises |x Data processing |2 fast |0 (OCoLC)fst00842543 | |
| 650 | 7 | |a Computer engineering |x Data processing |2 fast |0 (OCoLC)fst00872080 | |
| 650 | 7 | |a Information technology |x Management |2 fast |0 (OCoLC)fst00973112 | |
| 650 | 7 | |a Machine learning |2 fast |0 (OCoLC)fst01004795 | |
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