Published by Saint Philip Street Press, 2020
ISBN 10: 1013271661 ISBN 13: 9781013271663
Language: English
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Published by Saint Philip Street Press, 2020
ISBN 10: 1013271661 ISBN 13: 9781013271663
Language: English
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Published by Saint Philip Street Press, 2020
ISBN 10: 101327167X ISBN 13: 9781013271670
Language: English
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Published by Saint Philip Street Press, 2020
ISBN 10: 101327167X ISBN 13: 9781013271670
Language: English
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Published by Saint Philip Street Press, 2020
ISBN 10: 1013271661 ISBN 13: 9781013271663
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Published by Saint Philip Street Press, 2020
ISBN 10: 1013271661 ISBN 13: 9781013271663
Language: English
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Published by Saint Philip Street Press, 2020
ISBN 10: 101327167X ISBN 13: 9781013271670
Language: English
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Published by Saint Philip Street Press, 2020
ISBN 10: 101327167X ISBN 13: 9781013271670
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Add to basketHardcover. Condition: Brand New. 219 pages. 9.75x6.50x0.75 inches. In Stock.
Published by Saint Philip Street Press, 2020
ISBN 10: 1013271661 ISBN 13: 9781013271663
Language: English
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Add to basketKartoniert / Broschiert. Condition: New. KlappentextrnrnThis 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 interna.
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Published by Saint Philip Street Press, 2020
ISBN 10: 101327167X ISBN 13: 9781013271670
Language: English
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Add to basketGebunden. Condition: New. KlappentextrnrnThis 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 interna.
Published by Springer International Publishing, Springer International Publishing Mai 2019, 2019
ISBN 10: 3030053172 ISBN 13: 9783030053178
Language: English
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Add to basketBuch. Condition: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 236 pp. Englisch.
Published by Springer International Publishing, 2019
ISBN 10: 3030053172 ISBN 13: 9783030053178
Language: English
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Add to basketBuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.
Published by Saint Philip Street Press, 2020
ISBN 10: 1013271661 ISBN 13: 9781013271663
Language: English
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Published by Saint Philip Street Press, 2020
ISBN 10: 1013271661 ISBN 13: 9781013271663
Language: English
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Add to basketPAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Published by Saint Philip Street Press, 2020
ISBN 10: 101327167X ISBN 13: 9781013271670
Language: English
Seller: PBShop.store US, Wood Dale, IL, U.S.A.
HRD. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Published by Saint Philip Street Press, 2020
ISBN 10: 101327167X ISBN 13: 9781013271670
Language: English
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Add to basketHRD. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Published by Saint Philip Street Press, 2020
ISBN 10: 1013271661 ISBN 13: 9781013271663
Language: English
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Published by Saint Philip Street Press, 2020
ISBN 10: 1013271661 ISBN 13: 9781013271663
Language: English
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Published by Springer International Publishing Mai 2019, 2019
ISBN 10: 3030053172 ISBN 13: 9783030053178
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Add to basketBuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 236 pp. Englisch.