Automated Machine Learning

Frank Hutter

ISBN 10: 3030053172 / ISBN 13: 9783030053178

Verlag: Springer International Publishing, Springer International Publishing Mai 2019, 2019

Beschreibung

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.

Bestandsnummer des Verkäufers 9783030053178

Bibliografische Details

• Titel: Automated Machine Learning
• Verlag: Springer International Publishing, Springer International Publishing Mai 2019
• Erscheinungsjahr: 2019
• Sprache: Englisch
• ISBN-10: 3030053172
• ISBN-13: 9783030053178
• Einband: Buch
• Zustand: Neu
• Abmessungen: 241B x 160 H x 18 T Millimeter Breite: 241 mm, Höhe: 160 mm, Tiefe: 18 mm
• Gewicht: 567 Gramm

Über diesen Titel

Inhaltsangabe

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.