Parikshit Narendra Mahalle, Yashwant Sudhakar Ingle

Explainable Artificial Intelligence: A Practical Guide (eBook, ePUB)

• Format: ePub

Produktbeschreibung

This book explores the growing focus on artificial intelligence (AI) systems in both industry and academia. Key topics include an overview of explainable AI, black-box model understanding, interpretability techniques, practical XAI applications, and future trends and challenges in XAI.

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Produktdetails

• Verlag: Taylor & Francis
• Erscheinungstermin: 2. Dezember 2024
• Englisch
• ISBN-13: 9781040300862
• Artikelnr.: 72287598

Autorenporträt

Dr. Parikshit Narendra Mahalle is a senior member IEEE and is Professor, Dean Research and Development and Head of Department of Artificial Intelligence and Data Science at Vishwakarma Institute of Information Technology, Pune, India. He completed his Ph.D. from Aalborg University, Denmark and continued as Post Doc Researcher at CMI, Copenhagen, Denmark. He has 23+ years of teaching and research experience. He is an ex-member of the Board of Studies in Computer Engineering, ex-Chairman Information Technology, SPPU and various Universities and autonomous colleges across India. He has 12 patents and has 200+ research publications.

Mr. Yashwant Sudhakar Ingle is presently working at VIIT, Pune as Assistant Professor in Department of AI&DS. He has a total of 15 years work experience. He is pursuing a Ph.D. from SPPU and completed his M.Tech. CSE from Visvesvaraya National Institute of Technology, Nagpur and his B.E. CSE from Amravati University. Mr. Ingle has 4 design patents granted, 1 US patent published, 25 Indian utility patents published, 4 software copyrights and 2 literary research copyrights registered. He has authored a Springer book recently on Data Centric AI: A Beginner's Guide. He has published 25+ papers in Scopus, Web of Science journals, IEEE and Springer International Conferences and received 4 Best Paper Awards at the RACE National Conference 2021.

Inhaltsangabe

Preface 1. Explainable Artificial Intellience Overview 2. Understanding
Black Box Models 3. Techniques for Model Interpretability 4. Practical
Applications of XAI 5. Future Trends and Challenges in XAI Author biography
Index