9781032694863 - federated deep learning for healthcare: a practical guide with challenges and opportunities (advances in smart healthcare technologies) (11 resultados)
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Librería: Majestic Books, Hounslow, Reino UnidoMajestic Books
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Librería: California Books, Miami, FL, Estados Unidos de AmericaCalifornia Books
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EUR 85,37
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Librería: PBShop.store US, Wood Dale, IL, Estados Unidos de AmericaPBShop.store US
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PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.
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Librería: PBShop.store UK, Fairford, GLOS, Reino UnidoPBShop.store UK
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PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.
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Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
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EUR 90,24
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Librería: Revaluation Books, Exeter, Reino UnidoRevaluation Books
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EUR 103,55
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Paperback. Condición: Brand New. 252 pages. 6.14x0.56x9.21 inches. In Stock.
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Librería: moluna, Greven, Alemaniamoluna
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EUR 80,68
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Condición: New. Dr. Amandeep Kaur currently holds the position of a professor at the Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab. She earned her doctorate degree from I. K. Gujral Punjab Technical University..
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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EUR 108,00
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Taschenbuch. Condición: Neu. Neuware - This book provides a practical guide to federated deep learning for healthcare including fundamental concepts, framework, and the applications comprising domain adaptation, model distillation, and transfer learning. It covers concerns in model fairness, data bias, regulatory compliance, and… ethical dilemmas. It investigates several privacy-preserving methods such as homomorphic encryption, secure multi-party computation, and differential privacy. It will enable readers to build and implement federated learning systems that safeguard private medical information.Features: - Offers a thorough introduction of federated deep learning methods designed exclusively for medical applications. - Investigates privacy-preserving methods with emphasis on data security and privacy. - Discusses healthcare scaling and resource efficiency considerations. - Examines methods for sharing information among various healthcare organizations while retaining model performance. This book is aimed at graduate students and researchers in federated learning, data science, AI/machine learning, and healthcare.
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Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de AmericaGrand Eagle Retail
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EUR 84,01
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Paperback. Condición: new. Paperback. This book provides a practical guide to federated deep learning for healthcare including fundamental concepts, framework, and the applications comprising domain adaptation, model distillation, and transfer learning. It covers concerns in model fairness, data bias, regulatory compliance, and…ethical dilemmas. It investigates several privacy-preserving methods such as homomorphic encryption, secure multi-party computation, and differential privacy. It will enable readers to build and implement federated learning systems that safeguard private medical information.Features:Offers a thorough introduction of federated deep learning methods designed exclusively for medical applications.Investigates privacy-preserving methods with emphasis on data security and privacy.Discusses healthcare scaling and resource efficiency considerations.Examines methods for sharing information among various healthcare organizations while retaining model performance.This book is aimed at graduate students and researchers in federated learning, data science, AI/machine learning, and healthcare. This book provides a practical guide to federated deep learning for healthcare including fundamental concepts, framework, and the applications comprising of domain adaptation, model distillation, and transfer learning. It covers concerns in model fairness, data bias, regulatory compliance, and ethical dilemmas. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Librería: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
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EUR 66,55
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Paperback. Condición: new. Paperback. This book provides a practical guide to federated deep learning for healthcare including fundamental concepts, framework, and the applications comprising domain adaptation, model distillation, and transfer learning. It covers concerns in model fairness, data bias, regulatory compliance, and…ethical dilemmas. It investigates several privacy-preserving methods such as homomorphic encryption, secure multi-party computation, and differential privacy. It will enable readers to build and implement federated learning systems that safeguard private medical information.Features:Offers a thorough introduction of federated deep learning methods designed exclusively for medical applications.Investigates privacy-preserving methods with emphasis on data security and privacy.Discusses healthcare scaling and resource efficiency considerations.Examines methods for sharing information among various healthcare organizations while retaining model performance.This book is aimed at graduate students and researchers in federated learning, data science, AI/machine learning, and healthcare. This book provides a practical guide to federated deep learning for healthcare including fundamental concepts, framework, and the applications comprising of domain adaptation, model distillation, and transfer learning. It covers concerns in model fairness, data bias, regulatory compliance, and ethical dilemmas. 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.
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Librería: CitiRetail, Stevenage, Reino UnidoCitiRetail
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EUR 90,26
Envío por EUR 43,24Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Paperback. Condición: new. Paperback. This book provides a practical guide to federated deep learning for healthcare including fundamental concepts, framework, and the applications comprising domain adaptation, model distillation, and transfer learning. It covers concerns in model fairness, data bias, regulatory compliance, and…ethical dilemmas. It investigates several privacy-preserving methods such as homomorphic encryption, secure multi-party computation, and differential privacy. It will enable readers to build and implement federated learning systems that safeguard private medical information.Features:Offers a thorough introduction of federated deep learning methods designed exclusively for medical applications.Investigates privacy-preserving methods with emphasis on data security and privacy.Discusses healthcare scaling and resource efficiency considerations.Examines methods for sharing information among various healthcare organizations while retaining model performance.This book is aimed at graduate students and researchers in federated learning, data science, AI/machine learning, and healthcare. This book provides a practical guide to federated deep learning for healthcare including fundamental concepts, framework, and the applications comprising of domain adaptation, model distillation, and transfer learning. It covers concerns in model fairness, data bias, regulatory compliance, and ethical dilemmas. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
