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Hyperparameter Tuning for Machine and Deep Learning with R: A Practical Guide
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Foundations of Statistical Algorithms: With References to R Packages (Chapman & Hall/CRC Computer Science & Data Analysis)
Book 10 of 23: Chapman & Hall/CRC Computer Science & Data AnalysisLanguage: English
Published by Chapman and Hall/CRC (edition 1), 2013
ISBN 10: 1439878854 ISBN 13: 9781439878859
Hardcover. Condition: Good. 1. It's a preowned item in good condition and includes all the pages. It may have some general signs of wear and tear, such as markings, highlighting, slight damage to the cover, minimal wear to the binding, etc., but they will not affect the overall reading experience.
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Hyperparameter Tuning for Machine and Deep Learning with R: A Practical Guide
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Foundations of Statistical Algorithms: With References to R Packages (Chapman & Hall/CRC Computer Science & Data Analysis)
Book 10 of 23: Chapman & Hall/CRC Computer Science & Data AnalysisLanguage: English
Published by Chapman and Hall/CRC, 2013
ISBN 10: 1439878854 ISBN 13: 9781439878859
Hardcover. Condition: As New. Unmarked hardcover no jacket.
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Hyperparameter Tuning for Machine and Deep Learning With R : A Practical Guide
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Hyperparameter Tuning For Machine And De
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Foundations of Statistical Algorithms : With References to R Packages
Book 10 of 23: Chapman & Hall/CRC Computer Science & Data AnalysisLanguage: English
Published by Chapman and Hall/CRC, 2019
ISBN 10: 0367379090 ISBN 13: 9780367379094
Condition: As New. Unread book in perfect condition.
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Hyperparameter Tuning for Machine and Deep Learning With R: A Practical Guide
Language: English
Published by Springer-Nature New York Inc, 2022
ISBN 10: 9811951721 ISBN 13: 9789811951725
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Hyperparameter Tuning for Machine and Deep Learning With R : A Practical Guide
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Foundations of Statistical Algorithms : With References to R Packages
Book 10 of 23: Chapman & Hall/CRC Computer Science & Data AnalysisLanguage: English
Published by Chapman and Hall/CRC, 2019
ISBN 10: 0367379090 ISBN 13: 9780367379094
Condition: New.
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Foundations of Statistical Algorithms
Book 10 of 23: Chapman & Hall/CRC Computer Science & Data AnalysisLanguage: English
Published by Chapman and Hall/CRC, 2019
ISBN 10: 0367379090 ISBN 13: 9780367379094
Condition: New. pp. 500.
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Foundations of Statistical Algorithms : With References to R Packages
Book 10 of 23: Chapman & Hall/CRC Computer Science & Data AnalysisLanguage: English
Published by Chapman and Hall/CRC, 2019
ISBN 10: 0367379090 ISBN 13: 9780367379094
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Hyperparameter Tuning for Machine and Deep Learning With R: A Practical Guide
Language: English
Published by Springer-Nature New York Inc, 2023
ISBN 10: 9811951691 ISBN 13: 9789811951695
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Hyperparameter Tuning for Machine and Deep Learning with R
Language: English
Published by Springer Nature Singapore, Springer Nature Singapore Dez 2022, 2022
ISBN 10: 9811951721 ISBN 13: 9789811951725
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. 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.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 344 pp. Englisch.
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Foundations of Statistical Algorithms : With References to R Packages
Book 10 of 23: Chapman & Hall/CRC Computer Science & Data AnalysisLanguage: English
Published by Chapman and Hall/CRC, 2019
ISBN 10: 0367379090 ISBN 13: 9780367379094
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
US$ 106.46
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Add to basketCondition: New.
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Foundations of Statistical Algorithms
Book 10 of 23: Chapman & Hall/CRC Computer Science & Data AnalysisLanguage: English
Published by Chapman and Hall/CRC, 2019
ISBN 10: 0367379090 ISBN 13: 9780367379094
Condition: New. pp. 500.
-
Foundations of Statistical Algorithms
Book 10 of 23: Chapman & Hall/CRC Computer Science & Data AnalysisLanguage: English
Published by Chapman and Hall/CRC, 2019
ISBN 10: 0367379090 ISBN 13: 9780367379094
Condition: New. pp. 500.
-
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.
-
Foundations of Statistical Algorithms: With References to R Packages (Chapman & Hall/CRC Computer Science & Data Analysis)
Book 10 of 23: Chapman & Hall/CRC Computer Science & Data AnalysisUS$ 66.46
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Add to basketHardcover. Condition: New. Usually Dispatched within 1-2 Business Days , Buy with confidence , excellent customer service.
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Hyperparameter Tuning for Machine and Deep Learning with R : A Practical Guide
Language: English
Published by Springer Nature Singapore, Springer Nature Singapore, 2023
ISBN 10: 9811951691 ISBN 13: 9789811951695
Buch. 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.
-
Hyperparameter Tuning for Machine and Deep Learning with R : A Practical Guide
Language: English
Published by Springer Nature Singapore, 2023
ISBN 10: 9811951691 ISBN 13: 9789811951695
Condition: Sehr gut. Zustand: Sehr gut | Seiten: 344 | Sprache: Englisch | Produktart: Bücher | 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.
-
Hyperparameter Tuning for Machine and Deep Learning with R : A Practical Guide
Language: English
Published by Springer Nature Singapore, 2023
ISBN 10: 9811951691 ISBN 13: 9789811951695
Condition: Hervorragend. Zustand: Hervorragend | Seiten: 344 | Sprache: Englisch | Produktart: Bücher | 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.
