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  3. Machine learning

Automated Machine Learning

- Methods, Systems, Challenges
Engelsk Hardback

Automated Machine Learning

- Methods, Systems, Challenges
Engelsk Hardback
Tjek vores konkurrenters priser

This open access book presents the first comprehensive overview of general methods in Automatic Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first international challenge of AutoML systems.  The book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.  The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. Many of the recent machine learning successes crucially rely on human experts, who select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters; however the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself.

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This open access book presents the first comprehensive overview of general methods in Automatic Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first international challenge of AutoML systems.  The book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.  The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. Many of the recent machine learning successes crucially rely on human experts, who select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters; however the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself.

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Produktdetaljer
Sprog: Engelsk
Sider: 219
ISBN-13: 9783030053178
Indbinding: Hardback
Udgave:
ISBN-10: 3030053172
Kategori: Machine learning
Udg. Dato: 28 maj 2019
Længde: 17mm
Bredde: 241mm
Højde: 162mm
Forlag: Springer Nature Switzerland AG
Oplagsdato: 28 maj 2019
Forfatter(e):
Forfatter(e)

Kategori Machine learning

ISBN-13 9783030053178

Sprog Engelsk

Indbinding Hardback

Sider 219

Udgave

Længde 17mm

Bredde 241mm

Højde 162mm

Udg. Dato 28 maj 2019

Oplagsdato 28 maj 2019

Forlag Springer Nature Switzerland AG

Kategori sammenhænge