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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 : 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: A Practical Guide
Da: Ria Christie Collections, Uxbridge, Regno Unito
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Foundations of Statistical Algorithms: With References to R Packages (Chapman & Hall/CRC Computer Science & Data Analysis)
Libro 10 di 23: Chapman & Hall/CRC Computer Science & Data AnalysisDa: My Dead Aunt's Books, Hyattsville, MD, U.S.A.
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Hyperparameter Tuning for Machine and Deep Learning With R : A Practical Guide
Da: GreatBookPricesUK, Woodford Green, Regno Unito
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Hyperparameter Tuning For Machine And De
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Hyperparameter Tuning for Machine and Deep Learning with R: A Practical Guide
Da: Books Puddle, New York, NY, U.S.A.
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Hyperparameter Tuning for Machine and Deep Learning with R: A Practical Guide
Da: Books Puddle, New York, NY, U.S.A.
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Foundations of Statistical Algorithms : With References to R Packages
Libro 10 di 23: Chapman & Hall/CRC Computer Science & Data AnalysisDa: GreatBookPrices, Columbia, MD, U.S.A.
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Hyperparameter Tuning for Machine and Deep Learning With R: A Practical Guide
Lingua: Inglese
Editore: Springer-Nature New York Inc, 2022
ISBN 10: 9811951721 ISBN 13: 9789811951725
Da: Revaluation Books, Exeter, Regno Unito
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Hyperparameter Tuning for Machine and Deep Learning With R : A Practical Guide
Da: GreatBookPricesUK, Woodford Green, Regno Unito
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Hyperparameter Tuning For Machine And De
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Foundations of Statistical Algorithms : With References to R Packages
Libro 10 di 23: Chapman & Hall/CRC Computer Science & Data AnalysisDa: GreatBookPrices, Columbia, MD, U.S.A.
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Foundations of Statistical Algorithms
Libro 10 di 23: Chapman & Hall/CRC Computer Science & Data AnalysisDa: Majestic Books, Hounslow, Regno Unito
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Foundations of Statistical Algorithms : With References to R Packages
Libro 10 di 23: Chapman & Hall/CRC Computer Science & Data AnalysisDa: GreatBookPricesUK, Woodford Green, Regno Unito
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Foundations of Statistical Algorithms: With References to R Packages (Chapman & Hall/CRC Computer Science & Data Analysis)
Libro 10 di 23: Chapman & Hall/CRC Computer Science & Data AnalysisLingua: Inglese
Editore: Chapman and Hall/CRC (edition 1), 2013
ISBN 10: 1439878854 ISBN 13: 9781439878859
Da: BooksRun, Philadelphia, PA, U.S.A.
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Hardcover. Condizione: 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
Lingua: Inglese
Editore: Springer-Nature New York Inc, 2023
ISBN 10: 9811951691 ISBN 13: 9789811951695
Da: Revaluation Books, Exeter, Regno Unito
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EUR 87,16
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Aggiungi al carrelloHardcover. Condizione: Brand New. 340 pages. 9.25x6.10x9.21 inches. In Stock.
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Hyperparameter Tuning for Machine and Deep Learning with R
Lingua: Inglese
Editore: Springer Nature Singapore, Springer Nature Singapore Dez 2022, 2022
ISBN 10: 9811951721 ISBN 13: 9789811951725
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: 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
Libro 10 di 23: Chapman & Hall/CRC Computer Science & Data AnalysisDa: GreatBookPricesUK, Woodford Green, Regno Unito
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Foundations of Statistical Algorithms
Libro 10 di 23: Chapman & Hall/CRC Computer Science & Data AnalysisDa: Books Puddle, New York, NY, U.S.A.
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Foundations of Statistical Algorithms
Libro 10 di 23: Chapman & Hall/CRC Computer Science & Data AnalysisDa: Biblios, Frankfurt am main, HESSE, Germania
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EUR 48,53
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Aggiungi al carrelloTaschenbuch. Condizione: 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.
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Foundations of Statistical Algorithms: With References to R Packages (Chapman & Hall/CRC Computer Science & Data Analysis)
Libro 10 di 23: Chapman & Hall/CRC Computer Science & Data AnalysisDa: New Book Sale, London, Regno Unito
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EUR 56,58
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Aggiungi al carrelloHardcover. Condizione: 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
Lingua: Inglese
Editore: Springer Nature Singapore, Springer Nature Singapore, 2023
ISBN 10: 9811951691 ISBN 13: 9789811951695
EUR 59,27
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Spedito da Germania a U.S.A.Quantità: 1 disponibili
Aggiungi al carrelloBuch. Condizione: 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.
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Aggiungi al carrelloCondizione: 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.
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EUR 23,76
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Aggiungi al carrelloCondizione: 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.
