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Cover: 9783030053178 | Automated Machine Learning | Methods, Systems, Challenges | Buch | xiv
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Automated Machine Learning
Methods, Systems, Challenges
Buch von Frank Hutter (u. a.)
Sprache: Englisch

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Beschreibung
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.
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.
Zusammenfassung

Presents a tutorial-level overview of the methods underlying automatic machine learning, enabling readers to easily understand the key concepts behind AutoML

Offers a comprehensive collection of in-depth descriptions of AutoML systems, allowing readers to see how the key concepts have been implemented in the context of actual systems

Discusses an independent international competition of many different systems, providing an independent evaluation of pros and cons of different AutoML approaches

Inhaltsverzeichnis
1 Hyperparameter Optimization.- 2 Meta-Learning.- 3 Neural Architecture Search.- 4 Auto-WEKA.- 5 Hyperopt-Sklearn.- 6 Auto-sklearn.- 7 Towards Automatically-Tuned Deep Neural Networks.- 8 TPOT.- 9 The Automatic Statistician.- 10 AutoML Challenges.
Details
Erscheinungsjahr: 2019
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
Reihe: The Springer Series on Challenges in Machine Learning
Inhalt: xiv
219 S.
9 s/w Illustr.
45 farbige Illustr.
219 p. 54 illus.
45 illus. in color.
ISBN-13: 9783030053178
ISBN-10: 3030053172
Sprache: Englisch
Herstellernummer: 978-3-030-05317-8
Ausstattung / Beilage: HC runder Rücken kaschiert
Einband: Gebunden
Autor: Hutter, Frank
Kotthoff, Lars
Vanschoren, Joaquin
Redaktion: Hutter, Frank
Vanschoren, Joaquin
Kotthoff, Lars
Herausgeber: Frank Hutter/Lars Kotthoff/Joaquin Vanschoren
Auflage: 1st ed. 2019
Hersteller: Springer International Publishing
The Springer Series on Challenges in Machine Learning
Maße: 241 x 160 x 18 mm
Von/Mit: Frank Hutter (u. a.)
Erscheinungsdatum: 28.05.2019
Gewicht: 0,567 kg
Artikel-ID: 114890622
Zusammenfassung

Presents a tutorial-level overview of the methods underlying automatic machine learning, enabling readers to easily understand the key concepts behind AutoML

Offers a comprehensive collection of in-depth descriptions of AutoML systems, allowing readers to see how the key concepts have been implemented in the context of actual systems

Discusses an independent international competition of many different systems, providing an independent evaluation of pros and cons of different AutoML approaches

Inhaltsverzeichnis
1 Hyperparameter Optimization.- 2 Meta-Learning.- 3 Neural Architecture Search.- 4 Auto-WEKA.- 5 Hyperopt-Sklearn.- 6 Auto-sklearn.- 7 Towards Automatically-Tuned Deep Neural Networks.- 8 TPOT.- 9 The Automatic Statistician.- 10 AutoML Challenges.
Details
Erscheinungsjahr: 2019
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
Reihe: The Springer Series on Challenges in Machine Learning
Inhalt: xiv
219 S.
9 s/w Illustr.
45 farbige Illustr.
219 p. 54 illus.
45 illus. in color.
ISBN-13: 9783030053178
ISBN-10: 3030053172
Sprache: Englisch
Herstellernummer: 978-3-030-05317-8
Ausstattung / Beilage: HC runder Rücken kaschiert
Einband: Gebunden
Autor: Hutter, Frank
Kotthoff, Lars
Vanschoren, Joaquin
Redaktion: Hutter, Frank
Vanschoren, Joaquin
Kotthoff, Lars
Herausgeber: Frank Hutter/Lars Kotthoff/Joaquin Vanschoren
Auflage: 1st ed. 2019
Hersteller: Springer International Publishing
The Springer Series on Challenges in Machine Learning
Maße: 241 x 160 x 18 mm
Von/Mit: Frank Hutter (u. a.)
Erscheinungsdatum: 28.05.2019
Gewicht: 0,567 kg
Artikel-ID: 114890622
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