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Machine Unlearning: Concepts, Techniques and Applications [Kõva köide]

  • Formaat: Hardback, 182 pages, kõrgus x laius: 235x155 mm, 15 Illustrations, color; 3 Illustrations, black and white
  • Sari: Studies in Computational Intelligence
  • Ilmumisaeg: 16-Jun-2026
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3032184460
  • ISBN-13: 9783032184467
Teised raamatud teemal:
  • Kõva köide
  • Hind: 116,69 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Tavahind: 155,59 €
  • Säästad 25%
  • See raamat ei ole veel ilmunud. Raamatu kohalejõudmiseks kulub orienteeruvalt 3-4 nädalat peale raamatu väljaandmist.
  • Kogus:
  • Lisa ostukorvi
  • Tasuta tarne
  • Tellimisaeg 2-4 nädalat
  • Lisa soovinimekirja
  • Formaat: Hardback, 182 pages, kõrgus x laius: 235x155 mm, 15 Illustrations, color; 3 Illustrations, black and white
  • Sari: Studies in Computational Intelligence
  • Ilmumisaeg: 16-Jun-2026
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3032184460
  • ISBN-13: 9783032184467
Teised raamatud teemal:
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.
Chapter 1: Introduction to Machine Unlearning.
Chapter 2: Exact
Unlearning Methods.
Chapter 3: Approximate Unlearning Technique.
Chapter 4:
Machine Unlearning for Different Model Architectures.
Chapter 5:
Verification and Evaluation of Unlearning.
Chapter 6: Applications of
Machine Unlearning.
Chapter 7: Ethics, Privacy, Security, Governance, and
Future Considerations.