This book masters the critical skill of selective data removal from AI systems—essential for regulatory compliance and ethical AI development. This comprehensive book bridges the gap between theoretical foundations and practical implementation, offering clear pathways through both exact and approximate unlearning methodologies. Designed for machine learning engineers, privacy specialists, researchers, and policymakers, it uniquely integrates technical depth with legal and ethical frameworks. From telecom to finance, discover how to eliminate data influence while preserving model utility. Ideal for graduate courses, professional training, and organizational compliance initiatives, this book positions you at the forefront of responsible AI innovation in an increasingly privacy-conscious world.
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This book masters the critical skill of selective data removal from AI systems—essential for regulatory compliance and ethical AI development. This comprehensive book bridges the gap between theoretical foundations and practical implementation, offering clear pathways through both exact and approximate unlearning methodologies. Designed for machine learning engineers, privacy specialists, researchers, and policymakers, it uniquely integrates technical depth with legal and ethical frameworks. From telecom to finance, discover how to eliminate data influence while preserving model utility. Ideal for graduate courses, professional training, and organizational compliance initiatives, this book positions you at the forefront of responsible AI innovation in an increasingly privacy-conscious world.
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Buch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book masters the critical skill of selective data removal from AI systems essential for regulatory compliance and ethical AI development. This comprehensive book bridges the gap between theoretical foundations and practical implementation, offering clear pathways through both exact and approximate unlearning methodologies. Designed for machine learning engineers, privacy specialists, researchers, and policymakers, it uniquely integrates technical depth with legal and ethical frameworks. From telecom to finance, discover how to eliminate data influence while preserving model utility. Ideal for graduate courses, professional training, and organizational compliance initiatives, this book positions you at the forefront of responsible AI innovation in an increasingly privacy-conscious world. 182 pp. Englisch. Bestandsnummer des Verkäufers 9783032184467
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Buch. Zustand: Neu. Machine Unlearning: Concepts, Techniques and Applications | Rajan Gupta (u. a.) | Buch | xxi | Englisch | 2026 | Springer | EAN 9783032184467 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand. Bestandsnummer des Verkäufers 135997553
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Buch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book masters the critical skill of selective data removal from AI systemsessential for regulatory compliance and ethical AI development. This comprehensive book bridges the gap between theoretical foundations and practical implementation, offering clear pathways through both exact and approximate unlearning methodologies. Designed for machine learning engineers, privacy specialists, researchers, and policymakers, it uniquely integrates technical depth with legal and ethical frameworks. From telecom to finance, discover how to eliminate data influence while preserving model utility. Ideal for graduate courses, professional training, and organizational compliance initiatives, this book positions you at the forefront of responsible AI innovation in an increasingly privacy-conscious world.Springer Nature Customer Service Center GmbH, Europaplatz 3,69115 Heidelberg, Germany, Heidelberg 204 pp. Englisch. Bestandsnummer des Verkäufers 9783032184467
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Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book masters the critical skill of selective data removal from AI systems essential for regulatory compliance and ethical AI development. This comprehensive book bridges the gap between theoretical foundations and practical implementation, offering clear pathways through both exact and approximate unlearning methodologies. Designed for machine learning engineers, privacy specialists, researchers, and policymakers, it uniquely integrates technical depth with legal and ethical frameworks. From telecom to finance, discover how to eliminate data influence while preserving model utility. Ideal for graduate courses, professional training, and organizational compliance initiatives, this book positions you at the forefront of responsible AI innovation in an increasingly privacy-conscious world. Bestandsnummer des Verkäufers 9783032184467
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