Publicado por Springer Verlag, Singapore, Singapore, 2024
ISBN 10: 9819997178 ISBN 13: 9789819997176
Idioma: Inglés
Librería: Grand Eagle Retail, Fairfield, OH, Estados Unidos de America
EUR 254,98
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Añadir al carritoHardcover. Condición: new. Hardcover. This book delves into practical implementation of evolutionary and metaheuristic algorithms to advance the capacity of machine learning. The readers can gain insight into the capabilities of data-driven evolutionary optimization in materials mechanics, and optimize your learning algorithms for maximum efficiency. Or unlock the strategies behind hyperparameter optimization to enhance your transfer learning algorithms, yielding remarkable outcomes. Or embark on an illuminating journey through evolutionary techniques designed for constructing deep-learning frameworks. The book also introduces an intelligent RPL attack detection system tailored for IoT networks. Explore a promising avenue of optimization by fusing Particle Swarm Optimization with Reinforcement Learning. It uncovers the indispensable role of metaheuristics in supervised machine learning algorithms. Ultimately, this book bridges the realms of evolutionary dynamic optimization andmachine learning, paving the way for pioneering innovations in the field. This book delves into practical implementation of evolutionary and metaheuristic algorithms to advance the capacity of machine learning. The readers can gain insight into the capabilities of data-driven evolutionary optimization in materials mechanics, and optimize your learning algorithms for maximum efficiency. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Publicado por Springer Nature Singapore, Springer Nature Singapore, 2024
ISBN 10: 9819997178 ISBN 13: 9789819997176
Idioma: Inglés
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 223,11
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Añadir al carritoBuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book delves into practical implementation of evolutionary and metaheuristic algorithms to advance the capacity of machine learning. The readers can gain insight into the capabilities of data-driven evolutionary optimization in materials mechanics, and optimize your learning algorithms for maximum efficiency. Or unlock the strategies behind hyperparameter optimization to enhance your transfer learning algorithms, yielding remarkable outcomes. Or embark on an illuminating journey through evolutionary techniques designed for constructing deep-learning frameworks. The book also introduces an intelligent RPL attack detection system tailored for IoT networks. Explore a promising avenue of optimization by fusing Particle Swarm Optimization with Reinforcement Learning.It uncovers the indispensable role of metaheuristics in supervised machine learning algorithms. Ultimately, this book bridges the realms of evolutionary dynamic optimization andmachine learning, paving the way for pioneering innovations in the field.
Librería: Books Puddle, New York, NY, Estados Unidos de America
EUR 279,53
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Añadir al carritoCondición: New. 2024th edition NO-PA16APR2015-KAP.
Publicado por Springer-Nature New York Inc, 2024
ISBN 10: 9819997178 ISBN 13: 9789819997176
Idioma: Inglés
Librería: Revaluation Books, Exeter, Reino Unido
EUR 315,51
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Añadir al carritoHardcover. Condición: Brand New. 372 pages. 9.26x6.11x9.33 inches. In Stock.
Librería: dsmbooks, Liverpool, Reino Unido
EUR 376,48
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Añadir al carritohardcover. Condición: New. New. book.
Publicado por Springer Nature Singapore, 2024
ISBN 10: 9819997178 ISBN 13: 9789819997176
Idioma: Inglés
Librería: moluna, Greven, Alemania
EUR 180,07
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Añadir al carritoCondición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. A comprehensive exploration of evolutionary and metaheuristic algorithms applied to various aspects of machine learningShowcases how evolutionary and metaheuristic algorithms are revolutionizing industries like biomed and healthcareIntegrat.
Publicado por Springer Nature Singapore, Springer Nature Singapore Apr 2024, 2024
ISBN 10: 9819997178 ISBN 13: 9789819997176
Idioma: Inglés
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
EUR 213,99
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Añadir al carritoBuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book delves into practical implementation of evolutionary and metaheuristic algorithms to advance the capacity of machine learning. The readers can gain insight into the capabilities of data-driven evolutionary optimization in materials mechanics, and optimize your learning algorithms for maximum efficiency. Or unlock the strategies behind hyperparameter optimization to enhance your transfer learning algorithms, yielding remarkable outcomes. Or embark on an illuminating journey through evolutionary techniques designed for constructing deep-learning frameworks. The book also introduces an intelligent RPL attack detection system tailored for IoT networks. Explore a promising avenue of optimization by fusing Particle Swarm Optimization with Reinforcement Learning.It uncovers the indispensable role of metaheuristics in supervised machine learning algorithms. Ultimately, this book bridges the realms of evolutionary dynamic optimization andmachine learning, paving the way for pioneering innovations in the field. 372 pp. Englisch.
Librería: Majestic Books, Hounslow, Reino Unido
EUR 293,86
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Añadir al carritoCondición: New. Print on Demand.
Librería: Biblios, Frankfurt am main, HESSE, Alemania
EUR 295,54
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Añadir al carritoCondición: New. PRINT ON DEMAND.