Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 54,15
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Aggiungi al carrelloCondizione: New. In.
Editore: Springer Nature Switzerland, 2023
ISBN 10: 3031306082 ISBN 13: 9783031306082
Lingua: Inglese
Da: Buchpark, Trebbin, Germania
Condizione: Hervorragend. Zustand: Hervorragend | Seiten: 128 | Sprache: Englisch | Produktart: Bücher.
Editore: Springer Nature Switzerland, Springer Nature Switzerland Mai 2024, 2024
ISBN 10: 3031306112 ISBN 13: 9783031306112
Lingua: Inglese
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
EUR 181,89
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Neuware -This book presents a solution to the challenging issue of optimizing expensive-to-evaluate industrial problems such as the hyperparameter tuning of machine learning models. The approach combines two well-established concepts, Surrogate-Based Optimization (SBO) and parallelization, to efficiently search for optimal parameter setups with as few function evaluations as possible.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 128 pp. Englisch.
Editore: Springer Nature Switzerland, Springer Nature Switzerland Mai 2023, 2023
ISBN 10: 3031306082 ISBN 13: 9783031306082
Lingua: Inglese
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
EUR 181,89
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Aggiungi al carrelloBuch. Condizione: Neu. Neuware -This book presents a solution to the challenging issue of optimizing expensive-to-evaluate industrial problems such as the hyperparameter tuning of machine learning models. The approach combines two well-established concepts, Surrogate-Based Optimization (SBO) and parallelization, to efficiently search for optimal parameter setups with as few function evaluations as possible.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 128 pp. Englisch.
Editore: Springer Nature Switzerland, Springer Nature Switzerland, 2023
ISBN 10: 3031306082 ISBN 13: 9783031306082
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 181,89
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Aggiungi al carrelloBuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents a solution to the challenging issue of optimizing expensive-to-evaluate industrial problems such as the hyperparameter tuning of machine learning models. The approach combines two well-established concepts, Surrogate-Based Optimization (SBO) and parallelization, to efficiently search for optimal parameter setups with as few function evaluations as possible.Through in-depth analysis, the need for parallel SBO solvers is emphasized, and it is demonstrated that they outperform model-free algorithms in scenarios with a low evaluation budget. The SBO approach helps practitioners save significant amounts of time and resources in hyperparameter tuning as well as other optimization projects. As a highlight, a novel framework for objectively comparing the efficiency of parallel SBO algorithms is introduced, enabling practitioners to evaluate and select the most effective approach for their specific use case.Based on practical examples, decision support is delivered, detailing which parts of industrial optimization projects can be parallelized and how to prioritize which parts to parallelize first. By following the framework, practitioners can make informed decisions about how to allocate resources and optimize their models efficiently.
Editore: Springer Nature Switzerland, Springer Nature Switzerland, 2024
ISBN 10: 3031306112 ISBN 13: 9783031306112
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 181,89
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents a solution to the challenging issue of optimizing expensive-to-evaluate industrial problems such as the hyperparameter tuning of machine learning models. The approach combines two well-established concepts, Surrogate-Based Optimization (SBO) and parallelization, to efficiently search for optimal parameter setups with as few function evaluations as possible.Through in-depth analysis, the need for parallel SBO solvers is emphasized, and it is demonstrated that they outperform model-free algorithms in scenarios with a low evaluation budget. The SBO approach helps practitioners save significant amounts of time and resources in hyperparameter tuning as well as other optimization projects. As a highlight, a novel framework for objectively comparing the efficiency of parallel SBO algorithms is introduced, enabling practitioners to evaluate and select the most effective approach for their specific use case.Based on practical examples, decision support is delivered, detailing which parts of industrial optimization projects can be parallelized and how to prioritize which parts to parallelize first. By following the framework, practitioners can make informed decisions about how to allocate resources and optimize their models efficiently.
Da: California Books, Miami, FL, U.S.A.
EUR 213,63
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Da: Books Puddle, New York, NY, U.S.A.
EUR 231,73
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Da: Books Puddle, New York, NY, U.S.A.
EUR 233,08
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Aggiungi al carrelloCondizione: New.
Da: Revaluation Books, Exeter, Regno Unito
EUR 276,59
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Aggiungi al carrelloHardcover. Condizione: Brand New. 125 pages. 9.25x6.10x0.51 inches. In Stock.
Editore: Springer International Publishing AG, Cham, 2023
ISBN 10: 3031306082 ISBN 13: 9783031306082
Lingua: Inglese
Da: Grand Eagle Retail, Fairfield, OH, U.S.A.
EUR 224,74
Convertire valutaQuantità: 1 disponibili
Aggiungi al carrelloHardcover. Condizione: new. Hardcover. This book presents a solution to the challenging issue of optimizing expensive-to-evaluate industrial problems such as the hyperparameter tuning of machine learning models. The approach combines two well-established concepts, Surrogate-Based Optimization (SBO) and parallelization, to efficiently search for optimal parameter setups with as few function evaluations as possible.Through in-depth analysis, the need for parallel SBO solvers is emphasized, and it is demonstrated that they outperform model-free algorithms in scenarios with a low evaluation budget. The SBO approach helps practitioners save significant amounts of time and resources in hyperparameter tuning as well as other optimization projects. As a highlight, a novel framework for objectively comparing the efficiency of parallel SBO algorithms is introduced, enabling practitioners to evaluate and select the most effective approach for their specific use case.Based on practical examples, decision support is delivered, detailing which parts of industrial optimization projects can be parallelized and how to prioritize which parts to parallelize first. By following the framework, practitioners can make informed decisions about how to allocate resources and optimize their models efficiently. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Editore: Springer, Berlin|Springer Nature Switzerland|Springer, 2024
ISBN 10: 3031306112 ISBN 13: 9783031306112
Lingua: Inglese
Da: moluna, Greven, Germania
EUR 153,73
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Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book presents a solution to the challenging issue of optimizing expensive-to-evaluate industrial problems such as the hyperparameter tuning of machine learning models. The approach combines two well-established concepts, Surrogate-Based Optimization (S.
Editore: Springer, Berlin|Springer Nature Switzerland|Springer, 2023
ISBN 10: 3031306082 ISBN 13: 9783031306082
Lingua: Inglese
Da: moluna, Greven, Germania
EUR 153,73
Convertire valutaQuantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book presents a solution to the challenging issue of optimizing expensive-to-evaluate industrial problems such as the hyperparameter tuning of machine learning models. The approach combines two well-established concepts, Surrogate-Based Optimization (S.
Editore: Springer Nature Switzerland, Springer International Publishing Mai 2023, 2023
ISBN 10: 3031306082 ISBN 13: 9783031306082
Lingua: Inglese
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 181,89
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Aggiungi al carrelloBuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents a solution to the challenging issue of optimizing expensive-to-evaluate industrial problems such as the hyperparameter tuning of machine learning models. The approach combines two well-established concepts, Surrogate-Based Optimization (SBO) and parallelization, to efficiently search for optimal parameter setups with as few function evaluations as possible.Through in-depth analysis, the need for parallel SBO solvers is emphasized, and it is demonstrated that they outperform model-free algorithms in scenarios with a low evaluation budget. The SBO approach helps practitioners save significant amounts of time and resources in hyperparameter tuning as well as other optimization projects. As a highlight, a novel framework for objectively comparing the efficiency of parallel SBO algorithms is introduced, enabling practitioners to evaluate and select the most effective approach for their specific use case.Based on practical examples, decision support is delivered, detailing which parts of industrial optimization projects can be parallelized and how to prioritize which parts to parallelize first. By following the framework, practitioners can make informed decisions about how to allocate resources and optimize their models efficiently. 128 pp. Englisch.
Editore: Springer Nature Switzerland, Springer International Publishing Mai 2024, 2024
ISBN 10: 3031306112 ISBN 13: 9783031306112
Lingua: Inglese
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 181,89
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents a solution to the challenging issue of optimizing expensive-to-evaluate industrial problems such as the hyperparameter tuning of machine learning models. The approach combines two well-established concepts, Surrogate-Based Optimization (SBO) and parallelization, to efficiently search for optimal parameter setups with as few function evaluations as possible.Through in-depth analysis, the need for parallel SBO solvers is emphasized, and it is demonstrated that they outperform model-free algorithms in scenarios with a low evaluation budget. The SBO approach helps practitioners save significant amounts of time and resources in hyperparameter tuning as well as other optimization projects. As a highlight, a novel framework for objectively comparing the efficiency of parallel SBO algorithms is introduced, enabling practitioners to evaluate and select the most effective approach for their specific use case.Based on practical examples, decision support is delivered, detailing which parts of industrial optimization projects can be parallelized and how to prioritize which parts to parallelize first. By following the framework, practitioners can make informed decisions about how to allocate resources and optimize their models efficiently. 128 pp. Englisch.
Da: Majestic Books, Hounslow, Regno Unito
EUR 239,35
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Aggiungi al carrelloCondizione: New. Print on Demand.
Da: Majestic Books, Hounslow, Regno Unito
EUR 240,73
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Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 246,39
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Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 247,83
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Aggiungi al carrelloCondizione: New. PRINT ON DEMAND.