Kaushal chetna (43 risultati)

Condizione: New.

Paperback. Condizione: Brand New. 252 pages. 6.14x0.56x9.21 inches. In Stock.

Condizione: 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..

Taschenbuch. Condizione: 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.…

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.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Condizione: New.

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Condizione: New.

Condizione: As New. Unread book in perfect condition.

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Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

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

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

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

Condizione: New. In English.

Hardback. Condizione: New. In this internet age of security challenges and threats from cybercrime, enhanced security measures are necessary. The zero trust model-the IT security model that requires strict identity verification for every person and device trying to access resources on a private network-helps to meet these everincreasing and evolving security challenges. This new volume offers a comprehensive overview of the zero trust security model and its application in the field of cybersecurity, covering the principles, technologies, and best practices for implementing a zero trust approach, equipping readers with the knowledge and tools to secure their digital environments effectively. This book stands out by providing a holistic view of the zero trust security model, combining practical guidance for professionals with educational insights for both professionals and students and offering real-world examples and case studies that bridge the gap between learning and implementation.…

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. 433 pages. 9.18x6.12x9.45 inches. In Stock.

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

Hardback. Condizione: New. In this internet age of security challenges and threats from cybercrime, enhanced security measures are necessary. The zero trust model-the IT security model that requires strict identity verification for every person and device trying to access resources on a private network-helps to meet these everincreasing and evolving security challenges. This new volume offers a comprehensive overview of the zero trust security model and its application in the field of cybersecurity, covering the principles, technologies, and best practices for implementing a zero trust approach, equipping readers with the knowledge and tools to secure their digital environments effectively. This book stands out by providing a holistic view of the zero trust security model, combining practical guidance for professionals with educational insights for both professionals and students and offering real-world examples and case studies that bridge the gap between learning and implementation.…

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. …

Paperback. Condizione: 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.…