ISBN: 9781032689555 - federated deep learning for healthcare: a practical guide with challenges and opportunities (19 risultati)

hardcover. Condizione: Fine. Open Books is a nonprofit social venture that provides literacy experiences for thousands of readers each year through inspiring programs and creative capitalization of books.

Condizione: As New. Unread book in perfect condition.

Hardcover. Condizione: New. Brand new book, sourced directly from publisher. Dispatch time is 7-8 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Condizione: New.

Condizione: New.

Condizione: New.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: New. 1st edition NO-PA16APR2015-KAP.

Condizione: New.

Hardback. Condizione: New. New copy - Usually dispatched within 4 working days.

Condizione: New. In English.

Hardback. Condizione: New. 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. …

Hardcover. Condizione: Brand New. 312 pages. 9.19x6.13x9.21 inches. In Stock.

Hardback. Condizione: New. 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. …

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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..…

HRD. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

HRD. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.…