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Beschreibung
Learn and implement responsible AI models using Python. This book will teach you how to balance ethical challenges with opportunities in artificial intelligence.

The book starts with an introduction to the fundamentals of AI, with special emphasis given to the key principles of responsible AI. The authors then walk you through the critical issues of detecting and mitigating bias, making AI decisions understandable, preserving privacy, ensuring security, and designing robust models. Along the way, yoüll gain an overview of tools, techniques, and code examples to implement the key principles you learn in real-world scenarios.

The book concludes with a chapter devoted to fostering a deeper understanding of responsible AI¿s profound implications for the future. Each chapter offers a hands-on approach, enriched with practical insights and code snippets, enabling you to translate ethical considerations into actionable solutions.

What You Will Learn

Understand the principles of responsible AI and their importance in today's digital world
Master techniques to detect and mitigate bias in AI
Explore methods and tools for achieving transparency and explainability
Discover best practices for privacy preservation and security in AI
Gain insights into designing robust and reliable AI models

Who This Book Is For

AI practitioners, data scientists, machine learning engineers, researchers, policymakers, and students interested in the ethical aspects of AI
Learn and implement responsible AI models using Python. This book will teach you how to balance ethical challenges with opportunities in artificial intelligence.

The book starts with an introduction to the fundamentals of AI, with special emphasis given to the key principles of responsible AI. The authors then walk you through the critical issues of detecting and mitigating bias, making AI decisions understandable, preserving privacy, ensuring security, and designing robust models. Along the way, yoüll gain an overview of tools, techniques, and code examples to implement the key principles you learn in real-world scenarios.

The book concludes with a chapter devoted to fostering a deeper understanding of responsible AI¿s profound implications for the future. Each chapter offers a hands-on approach, enriched with practical insights and code snippets, enabling you to translate ethical considerations into actionable solutions.

What You Will Learn

Understand the principles of responsible AI and their importance in today's digital world
Master techniques to detect and mitigate bias in AI
Explore methods and tools for achieving transparency and explainability
Discover best practices for privacy preservation and security in AI
Gain insights into designing robust and reliable AI models

Who This Book Is For

AI practitioners, data scientists, machine learning engineers, researchers, policymakers, and students interested in the ethical aspects of AI
Über den Autor
Akshay R. Kulkarni is an artificial intelligence (AI) and machine learning (ML) evangelist and a thought leader. He has consulted several Fortune 500 and global enterprises to drive AI and data science-led strategic transformations. He is a Google developer, an author, and a regular speaker at major AI and data science conferences (including the O'Reilly Strata Data & AI Conference and Great International Developer Summit (GIDS)) . He is a visiting faculty member at some of the top graduate institutes in India. In 2019, he was featured as one of India's "top 40 under 40" data scientists. In his spare time, Akshay enjoys reading, writing, coding, and helping aspiring data scientists. He lives in Bangalore with his family.
Adarsha Shivananda is a data science and MLOps leader. He is working on creating world-class MLOps capabilities to ensure continuous value delivery from AI. He aims to build a pool of exceptional data scientists within and outside organizations to solve problems through training programs. He always wants to stay ahead of the curve. Adarsha has worked extensively in the pharma, healthcare, CPG, retail, and marketing domains. He lives in Bangalore and loves to read and teach data science.
Avinash Manure is a seasoned Machine Learning Professional with 10+ years of experience building, deploying, and maintaining state-of-the-art machine learning solutions across different industries. He has 6+ years of experience leading and mentoring high-performance teams in developing ML systems catering to different business requirements. He is proficient in deploying complex machine learning and statistical modeling algorithms/techniques for identifying patterns and extracting valuable insights for key stakeholders and organizational leadership.
Inhaltsverzeichnis

Chapter 1: Introduction.- Chapter 2: Bias and Fairness.- Chapter 3: Transparency and Explainability.- Chapter 4: Privacy and Security.- Chapter 5: Ensuring Robustness and Reliability.- Chapter 6: Conclusion.

Details
Erscheinungsjahr: 2023
Genre: Importe, Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Inhalt: ix
184 S.
18 s/w Illustr.
184 p. 18 illus.
ISBN-13: 9781484299814
ISBN-10: 1484299817
Sprache: Englisch
Einband: Kartoniert / Broschiert
Autor: Manure, Avinash
Bengani, Shaleen
S, Saravanan
Auflage: First Edition
Hersteller: Apress
Apress L.P.
Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, D-14197 Berlin, juergen.hartmann@springer.com
Maße: 235 x 155 x 11 mm
Von/Mit: Avinash Manure (u. a.)
Erscheinungsdatum: 23.11.2023
Gewicht: 0,306 kg
Artikel-ID: 127743911

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