Items related to Hyperparameter Tuning for Machine and Deep Learning...
- PublisherSpringer
- Publication date2022
- ISBN 10 9811951721
- ISBN 13 9789811951725
- BindingPaperback
- LanguageEnglish
- Edition number1
- Number of pages344
- EditorBartz Eva, Bartz-Beielstein Thomas, Zaefferer Martin, Mersmann Olaf
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Hyperparameter Tuning for Machine and Deep Learning with R: A Practical Guide (Paperback or Softback)
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Springer 12/19/2022, 2022
ISBN 10: 9811951721
ISBN 13: 9789811951725
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Seller: BargainBookStores, Grand Rapids, MI, U.S.A.
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Paperback or Softback. Condition: New. Hyperparameter Tuning for Machine and Deep Learning with R: A Practical Guide 1.06. Book. Seller Inventory # BBS-9789811951725
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Hyperparameter Tuning for Machine and Deep Learning with R: A Practical Guide
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Hyperparameter Tuning for Machine and Deep Learning with R : A Practical Guide
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Hyperparameter Tuning for Machine and Deep Learning with R: A Practical Guide
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Hyperparameter Tuning for Machine and Deep Learning with R
Published by
Springer Nature Singapore Dez 2022, 2022
ISBN 10: 9811951721
ISBN 13: 9789811951725
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Taschenbuch
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This open access book provides a wealth of hands-on examples that illustrate how hyperparameter tuning can be applied in practice and gives deep insights into the working mechanisms of machine learning (ML) and deep learning (DL) methods. The aim of the book is to equip readers with the ability to achieve better results with significantly less time, costs, effort and resources using the methods described here.The case studies presented in this book can be run on a regular desktop or notebook computer. No high-performance computing facilities are required. The idea for the book originated in a study conducted by Bartz & Bartz GmbH for the Federal Statistical Office of Germany (Destatis). Building on that study, the book is addressed to practitioners in industry as well as researchers, teachers and students in academia. The content focuses on the hyperparameter tuning of ML and DL algorithms, and is divided into two main parts: theory (Part I) and application (Part II). Essential topics covered include: a survey of important model parameters; four parameter tuning studies and one extensive global parameter tuning study; statistical analysis of the performance of ML and DL methods based on severity; and a new, consensus-ranking-based way to aggregate and analyze results from multiple algorithms. The book presents analyses of more than 30 hyperparameters from six relevant ML and DL methods, and provides source code so that users can reproduce the results. Accordingly, it serves as a handbook and textbook alike. 344 pp. Englisch. Seller Inventory # 9789811951725
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Hyperparameter Tuning for Machine and Deep Learning with R : A Practical Guide
Published by
Springer Nature Singapore, Springer Nature Singapore, 2022
ISBN 10: 9811951721
ISBN 13: 9789811951725
New
Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This open access book provides a wealth of hands-on examples that illustrate how hyperparameter tuning can be applied in practice and gives deep insights into the working mechanisms of machine learning (ML) and deep learning (DL) methods. The aim of the book is to equip readers with the ability to achieve better results with significantly less time, costs, effort and resources using the methods described here.The case studies presented in this book can be run on a regular desktop or notebook computer. No high-performance computing facilities are required. The idea for the book originated in a study conducted by Bartz & Bartz GmbH for the Federal Statistical Office of Germany (Destatis). Building on that study, the book is addressed to practitioners in industry as well as researchers, teachers and students in academia. The content focuses on the hyperparameter tuning of ML and DL algorithms, and is divided into two main parts: theory (Part I) and application (Part II).Essential topics covered include: a survey of important model parameters; four parameter tuning studies and one extensive global parameter tuning study; statistical analysis of the performance of ML and DL methods based on severity; and a new, consensus-ranking-based way to aggregate and analyze results from multiple algorithms. The book presents analyses of more than 30 hyperparameters from six relevant ML and DL methods, and provides source code so that users can reproduce the results. Accordingly, it serves as a handbook and textbook alike. Seller Inventory # 9789811951725
Quantity: 1 available
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Hyperparameter Tuning for Machine and Deep Learning With R: A Practical Guide
Published by
Springer-Nature New York Inc, 2022
ISBN 10: 9811951721
ISBN 13: 9789811951725
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Paperback
Seller: Revaluation Books, Exeter, United Kingdom
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Paperback. Condition: Brand New. 340 pages. 9.25x6.10x0.72 inches. In Stock. Seller Inventory # x-9811951721
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Hyperparameter Tuning for Machine and Deep Learning with R
Published by
Springer, Berlin|Springer Nature Singapore|TH Köln, Institute for Data Science, Engineering, and Analytics|Springer, 2022
ISBN 10: 9811951721
ISBN 13: 9789811951725
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Seller: moluna, Greven, Germany
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Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This open access book provides a wealth of hands-on examples that illustrate how hyperparameter tuning can be applied in practice and gives deep insights into the working mechanisms of machine learning (ML) and deep learning (DL) methods. The aim of the . Seller Inventory # 668479473
Quantity: Over 20 available