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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
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Librería: GreatBookPricesUK, Woodford Green, Reino Unido
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Añadir al carritoHardcover. Condición: Brand New. 488 pages. 9.18x6.12x9.21 inches. In Stock.
Idioma: Inglés
Publicado por Taylor & Francis Ltd, London, 2025
ISBN 10: 1032968877 ISBN 13: 9781032968872
Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de America
EUR 184,51
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Añadir al carritoHardcover. Condición: new. Hardcover. This reference text offers a relevant and thorough examination of the overlap between neuroscience and federated learning. It explores the complexities of utilizing federated learning algorithms for MRI data analysis, demonstrating how to improve the accuracy and efficiency of diagnostic procedures. The book covers topics such as the prediction and diagnosis of Alzheimers disease using neural networks and ensuring data privacy and security in federated learning for neural disorders.This book: Provides a thorough examination of the transformative impact of federated learning on the diagnosis, treatment, and understanding of brain disordersFocuses on combining federated learning with magnetic resonance imaging (MRI) data, which is a fundamental aspect of contemporary neuroimaging researchExamines the use of federated learning as a promising approach for collaborative data analysis in healthcare, with a focus on maintaining privacy and securityExplores the cutting-edge field of healthcare innovation by examining the interface of neuroscience and machine learning, with a specific focus on the breakthrough technique of federated learningOffers a comprehensive understanding of how federated learning may transform patient care, covering both theoretical ideas and practical examplesIt is primarily written for graduate students and academic researchers in electrical engineering, electronics, and communication engineering, computer science and engineering, and biomedical engineering. This text offers a relevant and thorough examination of the overlap between neuroscience and federated learning. It explores the complexities of utilizing federated learning algorithms for MRI data analysis, demonstrating how to improve the accuracy and efficiency of diagnostic procedures. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Idioma: Inglés
Publicado por Taylor & Francis Ltd, London, 2025
ISBN 10: 1032968877 ISBN 13: 9781032968872
Librería: CitiRetail, Stevenage, Reino Unido
EUR 180,37
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Añadir al carritoHardcover. Condición: new. Hardcover. This reference text offers a relevant and thorough examination of the overlap between neuroscience and federated learning. It explores the complexities of utilizing federated learning algorithms for MRI data analysis, demonstrating how to improve the accuracy and efficiency of diagnostic procedures. The book covers topics such as the prediction and diagnosis of Alzheimers disease using neural networks and ensuring data privacy and security in federated learning for neural disorders.This book: Provides a thorough examination of the transformative impact of federated learning on the diagnosis, treatment, and understanding of brain disordersFocuses on combining federated learning with magnetic resonance imaging (MRI) data, which is a fundamental aspect of contemporary neuroimaging researchExamines the use of federated learning as a promising approach for collaborative data analysis in healthcare, with a focus on maintaining privacy and securityExplores the cutting-edge field of healthcare innovation by examining the interface of neuroscience and machine learning, with a specific focus on the breakthrough technique of federated learningOffers a comprehensive understanding of how federated learning may transform patient care, covering both theoretical ideas and practical examplesIt is primarily written for graduate students and academic researchers in electrical engineering, electronics, and communication engineering, computer science and engineering, and biomedical engineering. This text offers a relevant and thorough examination of the overlap between neuroscience and federated learning. It explores the complexities of utilizing federated learning algorithms for MRI data analysis, demonstrating how to improve the accuracy and efficiency of diagnostic procedures. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Añadir al carritoHRD. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Librería: PBShop.store US, Wood Dale, IL, Estados Unidos de America
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Añadir al carritoHRD. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Idioma: Inglés
Publicado por Taylor & Francis Ltd, London, 2025
ISBN 10: 1032968877 ISBN 13: 9781032968872
Librería: AussieBookSeller, Truganina, VIC, Australia
EUR 360,31
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Añadir al carritoHardcover. Condición: new. Hardcover. This reference text offers a relevant and thorough examination of the overlap between neuroscience and federated learning. It explores the complexities of utilizing federated learning algorithms for MRI data analysis, demonstrating how to improve the accuracy and efficiency of diagnostic procedures. The book covers topics such as the prediction and diagnosis of Alzheimers disease using neural networks and ensuring data privacy and security in federated learning for neural disorders.This book: Provides a thorough examination of the transformative impact of federated learning on the diagnosis, treatment, and understanding of brain disordersFocuses on combining federated learning with magnetic resonance imaging (MRI) data, which is a fundamental aspect of contemporary neuroimaging researchExamines the use of federated learning as a promising approach for collaborative data analysis in healthcare, with a focus on maintaining privacy and securityExplores the cutting-edge field of healthcare innovation by examining the interface of neuroscience and machine learning, with a specific focus on the breakthrough technique of federated learningOffers a comprehensive understanding of how federated learning may transform patient care, covering both theoretical ideas and practical examplesIt is primarily written for graduate students and academic researchers in electrical engineering, electronics, and communication engineering, computer science and engineering, and biomedical engineering. This text offers a relevant and thorough examination of the overlap between neuroscience and federated learning. It explores the complexities of utilizing federated learning algorithms for MRI data analysis, demonstrating how to improve the accuracy and efficiency of diagnostic procedures. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 312,21
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Añadir al carritoBuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This text offers a relevant and thorough examination of the overlap between neuroscience and federated learning. It explores the complexities of utilizing federated learning algorithms for MRI data analysis, demonstrating how to improve the accuracy and efficiency of diagnostic procedures.