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Idioma: Inglés
Publicado por Chapman and Hall/CRC (edition 1), 2013
ISBN 10: 1439878854 ISBN 13: 9781439878859
Librería: BooksRun, Philadelphia, PA, Estados Unidos de America
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Añadir al carritoHardcover. Condición: 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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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
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Librería: Books Puddle, New York, NY, Estados Unidos de America
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Librería: GreatBookPricesUK, Woodford Green, Reino Unido
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Idioma: Inglés
Publicado por Springer-Nature New York Inc, 2022
ISBN 10: 9811951721 ISBN 13: 9789811951725
Librería: Revaluation Books, Exeter, Reino Unido
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Idioma: Inglés
Publicado por Springer-Nature New York Inc, 2023
ISBN 10: 9811951691 ISBN 13: 9789811951695
Librería: Revaluation Books, Exeter, Reino Unido
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Publicado por Springer Nature Singapore, Springer Nature Singapore Dez 2022, 2022
ISBN 10: 9811951721 ISBN 13: 9789811951725
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
EUR 42,79
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Añadir al carritoTaschenbuch. Condición: 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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Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 48,53
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Añadir al carritoTaschenbuch. Condición: 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.
Librería: New Book Sale, London, Reino Unido
EUR 56,99
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Añadir al carritoHardcover. Condición: New. Usually Dispatched within 1-2 Business Days , Buy with confidence , excellent customer service.
Idioma: Inglés
Publicado por Springer Nature Singapore, Springer Nature Singapore, 2023
ISBN 10: 9811951691 ISBN 13: 9789811951695
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 59,27
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Añadir al carritoBuch. Condición: 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.