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Published by Kluwer Academic Pub, Secaucus, New Jersey, U.S.A., 1997
ISBN 10: 0792398637 ISBN 13: 9780792398639
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Published by Elsevier Science & Technology, Oxford, 2023
ISBN 10: 0323960987 ISBN 13: 9780323960984
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Paperback. Condition: new. Paperback. Explainable Deep Learning AI: Methods and Challenges presents the latest works of leading researchers in the XAI area, offering an overview of the XAI area, along with several novel technical methods and applications that address explainability challenges for deep learning AI systems. The book overviews XAI and then covers a number of specific technical works and approaches for deep learning, ranging from general XAI methods to specific XAI applications, and finally, with user-oriented evaluation approaches. It also explores the main categories of explainable AI deep learning, which become the necessary condition in various applications of artificial intelligence.The groups of methods such as back-propagation and perturbation-based methods are explained, and the application to various kinds of data classification are presented. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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ISBN 10: 0323960987 ISBN 13: 9780323960984
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Published by Elsevier Science and Technology, GB, 2023
ISBN 10: 0323960987 ISBN 13: 9780323960984
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Paperback. Condition: New. Explainable Deep Learning AI: Methods and Challenges presents the latest works of leading researchers in the XAI area, offering an overview of the XAI area, along with several novel technical methods and applications that address explainability challenges for deep learning AI systems. The book overviews XAI and then covers a number of specific technical works and approaches for deep learning, ranging from general XAI methods to specific XAI applications, and finally, with user-oriented evaluation approaches. It also explores the main categories of explainable AI - deep learning, which become the necessary condition in various applications of artificial intelligence. The groups of methods such as back-propagation and perturbation-based methods are explained, and the application to various kinds of data classification are presented.
Language: English
Published by Elsevier Science and Technology, GB, 2023
ISBN 10: 0323960987 ISBN 13: 9780323960984
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Paperback. Condition: New. Explainable Deep Learning AI: Methods and Challenges presents the latest works of leading researchers in the XAI area, offering an overview of the XAI area, along with several novel technical methods and applications that address explainability challenges for deep learning AI systems. The book overviews XAI and then covers a number of specific technical works and approaches for deep learning, ranging from general XAI methods to specific XAI applications, and finally, with user-oriented evaluation approaches. It also explores the main categories of explainable AI - deep learning, which become the necessary condition in various applications of artificial intelligence. The groups of methods such as back-propagation and perturbation-based methods are explained, and the application to various kinds of data classification are presented.
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Published by Academic Press 2023-02-24, 2023
ISBN 10: 0323960987 ISBN 13: 9780323960984
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Published by Elsevier Science & Technology, 2023
ISBN 10: 0323960987 ISBN 13: 9780323960984
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Condition: New. Provides an overview of main approaches to Explainable Artificial Intelligence (XAI) in the Deep Learning realm, including the most popular techniques and their use, concluding with challenges and exciting future directions of XAI Explores.
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Language: English
Published by Elsevier Science and Technology, GB, 2023
ISBN 10: 0323960987 ISBN 13: 9780323960984
Seller: Rarewaves USA United, OSWEGO, IL, U.S.A.
Paperback. Condition: New. Explainable Deep Learning AI: Methods and Challenges presents the latest works of leading researchers in the XAI area, offering an overview of the XAI area, along with several novel technical methods and applications that address explainability challenges for deep learning AI systems. The book overviews XAI and then covers a number of specific technical works and approaches for deep learning, ranging from general XAI methods to specific XAI applications, and finally, with user-oriented evaluation approaches. It also explores the main categories of explainable AI - deep learning, which become the necessary condition in various applications of artificial intelligence. The groups of methods such as back-propagation and perturbation-based methods are explained, and the application to various kinds of data classification are presented.
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