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Executive briefing : usable machine learning - lessons from Stanford and beyond /
"Despite a meteoric rise in data volumes within modern enterprises, enabling nontechnical users to put this data to work in diagnostic and predictive tasks remains a fundamental challenge. Peter Bailis (Sisu | Stanford University) details the lessons learned in building new systems and interfac...
| 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) Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 246 | 3 | 0 | |a Usable machine learning - lessons from Stanford and beyond |
| 264 | 1 | |a [Place of publication not identified] : |b O'Reilly Media, |c 2019. | |
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| 511 | 0 | |a Presenter, Peter Bailis. | |
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| 520 | |a "Despite a meteoric rise in data volumes within modern enterprises, enabling nontechnical users to put this data to work in diagnostic and predictive tasks remains a fundamental challenge. Peter Bailis (Sisu | Stanford University) details the lessons learned in building new systems and interfaces to help users quickly and easily leverage the data at their disposal with production experience from Facebook, Microsoft, and the Stanford DAWN project. Drawing on his research and startup experience, Peter examines why deep networks aren't a panacea for most organizations' data; how usability and speed are the best path to better models; why Facebook, Apple, Amazon, Netflix, and Google (FAANG) likely won't (and can't) dominate every vertical; and why automating feature selection is more practical than AutoML. This session is from the 2019 O'Reilly Artificial Intelligence Conference in San Jose, CA."--Resource description page | ||
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