Articoli correlati a Hyperparameter Tuning for Machine and Deep Learning...
- EditoreSpringer-Nature New York Inc
- Data di pubblicazione2023
- ISBN 10 9811951691
- ISBN 13 9789811951695
- RilegaturaCopertina rigida
- LinguaInglese
- Numero edizione1
- Numero di pagine344
- RedattoreBartz Eva, Bartz-beielstein Thomas, Zaefferer Martin, Mersmann Olaf
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Hyperparameter Tuning for Machine and Deep Learning with R: A Practical Guide
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Hardcover. Condizione: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less 1.45. Codice articolo G9811951691I4N00
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HYPERPARAMETER TUNING FOR MACHINE AND DEEP LEARNING WITH R
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Condizione: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service. Codice articolo ABEJUNE24-297074
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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
Editore:
Springer Nature Singapore, 2023
ISBN 10: 9811951691
ISBN 13: 9789811951695
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Da: Buchpark, Trebbin, Germania
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Condizione: Sehr gut. Zustand: Sehr gut - Gepflegter, sauberer Zustand. | Seiten: 344 | Sprache: Englisch | Produktart: Bücher. Codice articolo 40521393/2
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Hyperparameter Tuning for Machine and Deep Learning with R : A Practical Guide
Editore:
Springer Nature Singapore, 2023
ISBN 10: 9811951691
ISBN 13: 9789811951695
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Da: Buchpark, Trebbin, Germania
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Condizione: Hervorragend. Zustand: Hervorragend | Seiten: 344 | Sprache: Englisch | Produktart: Bücher. Codice articolo 40521393/1
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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
Editore:
Springer Nature Singapore Jan 2023, 2023
ISBN 10: 9811951691
ISBN 13: 9789811951695
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Buch. Condizione: 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. Codice articolo 9789811951695
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Hyperparameter Tuning for Machine and Deep Learning with R: A Practical Guide
Da: Russell Books, Victoria, BC, Canada
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hardcover. Condizione: New. 1st ed. 2023. Special order direct from the distributor. Codice articolo ING9789811951695
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Hyperparameter Tuning for Machine and Deep Learning with R : A Practical Guide
Da: AHA-BUCH GmbH, Einbeck, Germania
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Buch. 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. Codice articolo 9789811951695
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Hyperparameter Tuning for Machine and Deep Learning with R
Editore:
Springer, Berlin|Springer Nature Singapore|TH Köln, Institute for Data Science, Engineering, and Analytics|Springer, 2022
ISBN 10: 9811951691
ISBN 13: 9789811951695
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Da: moluna, Greven, Germania
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Gebunden. Condizione: 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 . Codice articolo 668479472
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