{"product_id":"machine-unlearning-9798267927000","title":"Machine Unlearning","description":"\u003cp\u003e • Author(s): Ajit Singh\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Machine Theory\u003c\/p\u003e\u003cp\u003eThis book, Machine Unlearning for Technical Developers and AI Researchers, is designed to bridge the gap between theoretical research and practical implementation. It provides a comprehensive exploration of Machine Unlearning, covering foundational concepts, algorithmic approaches, real-world applications, and emerging challenges. The book is structured to cater to both practitioners and researchers, offering rigorous mathematical formulations, hands-on implementation techniques, and insights into legal and ethical considerations. \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003eWhy This Book? \u003cp\u003e\u003c\/p\u003eWhile numerous resources exist on Machine learning, few address the critical need for Machine Unlearning in depth. This book fills that void by: \u003cp\u003e\u003c\/p\u003e1. Demystifying Unlearning Algorithms: Presenting a systematic breakdown of state-of-the-art Unlearning techniques, including exact and approximate Unlearning, differential privacy-based methods, and data deletion frameworks. \u003cbr\u003e2. Bridging Theory and Practice: Providing code snippets, case studies, and implementation guides to help developers integrate Unlearning into real-world AI systems. \u003cbr\u003e3. Addressing Regulatory and Ethical Concerns: Discussing compliance with GDPR, CCPA, and other data protection laws, along with ethical implications of AI memory retention. \u003cbr\u003e4. Exploring Future Directions: Analyzing open research problems, scalability challenges, and the intersection of Unlearning with federated learning, reinforcement learning, and large language models (LLMs). \u003cp\u003e\u003c\/p\u003eWho Should Read This Book? \u003cp\u003e\u003c\/p\u003eThis book is intended for: \u003cp\u003e\u003c\/p\u003e1. AI\/ML Engineers \u0026amp; Developers who need to implement compliant, adaptable AI systems. \u003cbr\u003e2. Data Scientists \u0026amp; Researchers exploring privacy-preserving ML and regulatory constraints. \u003cbr\u003e3. Cybersecurity \u0026amp; Privacy Experts working on data governance and AI auditing. \u003cbr\u003e4. Policy Makers \u0026amp; Legal Professionals seeking technical insights into AI regulation. \u003cp\u003e\u003c\/p\u003eHow to Use This Book: \u003cp\u003e\u003c\/p\u003eThe book is structured into three main parts: \u003cbr\u003e1. Foundations of Machine Unlearning (Chapters 1-3): Covers core concepts, threat models, and legal frameworks. 2. Algorithms \u0026amp; Implementation (Chapters 4-7): Details exact and approximate Unlearning methods with practical examples. \u003cbr\u003e3. Advanced Topics \u0026amp; Future Directions (Chapters 8-10): Explores federated Unlearning, reinforcement learning, and open challenges.","brand":"Atlantic Books","offers":[{"title":"Paperback","offer_id":46332394340503,"sku":"9798267927000","price":2165.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798267927000.webp?v=1768726094","url":"https:\/\/atlanticbooks.com\/products\/machine-unlearning-9798267927000","provider":"Atlantic Books","version":"1.0","type":"link"}