Automated Machine Learning: Methods, Systems, Challenges - Rilegato

Libro 5 di 8: The Springer Series on Challenges in Machine Learning
 
9783030053178: Automated Machine Learning: Methods, Systems, Challenges
Rilegato
ISBN 10: 3030053172 ISBN 13: 9783030053178
Casa editrice: Springer-Verlag GmbH, 2019

Sinossi

This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. 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. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This 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. 

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

Dalla quarta di copertina

This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. 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. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This 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.

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.

Editore
Springer-Verlag GmbH
Data di pubblicazione
2019
Lingua
Inglese
ISBN 10 
3030053172
ISBN 13 
9783030053178
Rilegatura
Copertina rigida
Numero edizione
1
Numero di pagine
236
Redattore
Hutter Frank, Vanschoren Joaquin, Kotthoff Lars
Contatto del produttore
Springer Nature Customer Service Center GmbH
ProductSafety@springernature.com

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Heidelberg
69115
Germania

Altre edizioni note dello stesso titolo

9783030053192: Automated Machine Learning: Methods, Systems, Challenges

Edizione in evidenza

ISBN 10:  3030053199 ISBN 13:  9783030053192
Casa editrice: Springer, 2019
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