Distributed Deep Learning and Explainable AI (XAI) in Industry 4.0: 55 (Information Systems Engineering and Management, 55) - Tapa dura
Sinopsis
The primary aim of XAI is to enhance the transparency and comprehensibility of deep learning model decision-making processes for stakeholders, irrespective of their technical expertise.
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De la contraportada
This book is a comprehensive resource that delves into the integration of advanced artificial intelligence techniques within the context of modern industrial practices. It systematically explores how distributed deep learning methodologies can be effectively combined with explainable AI to enhance transparency in Industry 4.0 applications. In recent years, neural networks and other deep learning models have produced remarkable outcomes in a variety of fields, including image recognition, natural language processing, and decision-making. Concerns have been raised regarding the transparency and interpretability of these models as a result of their increasing intricacy. The demand for methodologies and approaches associated with explainable artificial intelligence (XAI) has consequently increased. The primary aim of XAI is to enhance the transparency and comprehensibility of deep learning model decision-making processes for stakeholders, irrespective of their technical expertise.
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Distributed Deep Learning and Explainable AI (XAI) in Industry 4.0 (eng)
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Distributed Deep Learning and Explainable AI (XAI) in Industry 4.0 (Hardcover)
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ISBN 13: 9783031946363
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Hardcover. Condición: new. Hardcover. This book is a comprehensive resource that delves into the integration of advanced artificial intelligence techniques within the context of modern industrial practices. It systematically explores how distributed deep learning methodologies can be effectively combined with explainable AI to enhance transparency in Industry 4.0 applications. In recent years, neural networks and other deep learning models have produced remarkable outcomes in a variety of fields, including image recognition, natural language processing, and decision-making. Concerns have been raised regarding the transparency and interpretability of these models as a result of their increasing intricacy. The demand for methodologies and approaches associated with explainable artificial intelligence (XAI) has consequently increased. The primary aim of XAI is to enhance the transparency and comprehensibility of deep learning model decision-making processes for stakeholders, irrespective of their technical expertise. The primary aim of XAI is to enhance the transparency and comprehensibility of deep learning model decision-making processes for stakeholders, irrespective of their technical expertise. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9783031946363
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Distributed Deep Learning and Explainable AI (XAI) in Industry 4.0
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Springer, Berlin, Springer Nature Switzerland, Springer, 2025
ISBN 10: 3031946367
ISBN 13: 9783031946363
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book is a comprehensive resource that delves into the integration of advanced artificial intelligence techniques within the context of modern industrial practices. It systematically explores how distributed deep learning methodologies can be effectively combined with explainable AI to enhance transparency in Industry 4.0 applications. In recent years, neural networks and other deep learning models have produced remarkable outcomes in a variety of fields, including image recognition, natural language processing, and decision-making. Concerns have been raised regarding the transparency and interpretability of these models as a result of their increasing intricacy. The demand for methodologies and approaches associated with explainable artificial intelligence (XAI) has consequently increased. The primary aim of XAI is to enhance the transparency and comprehensibility of deep learning model decision-making processes for stakeholders, irrespective of their technical expertise. 424 pp. Englisch. Nº de ref. del artículo: 9783031946363
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Distributed Deep Learning and Explainable AI (XAI) in Industry 4.0 (Hardcover)
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Springer International Publishing AG, Cham, 2025
ISBN 10: 3031946367
ISBN 13: 9783031946363
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Hardcover. Condición: new. Hardcover. This book is a comprehensive resource that delves into the integration of advanced artificial intelligence techniques within the context of modern industrial practices. It systematically explores how distributed deep learning methodologies can be effectively combined with explainable AI to enhance transparency in Industry 4.0 applications. In recent years, neural networks and other deep learning models have produced remarkable outcomes in a variety of fields, including image recognition, natural language processing, and decision-making. Concerns have been raised regarding the transparency and interpretability of these models as a result of their increasing intricacy. The demand for methodologies and approaches associated with explainable artificial intelligence (XAI) has consequently increased. The primary aim of XAI is to enhance the transparency and comprehensibility of deep learning model decision-making processes for stakeholders, irrespective of their technical expertise. The primary aim of XAI is to enhance the transparency and comprehensibility of deep learning model decision-making processes for stakeholders, irrespective of their technical expertise. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Nº de ref. del artículo: 9783031946363
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Distributed Deep Learning and Explainable AI (XAI) in Industry 4.0
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Buch. Condición: Neu. Distributed Deep Learning and Explainable AI (XAI) in Industry 4.0 | Lalitha Krishnasamy (u. a.) | Buch | Information Systems Engineering and Management | vi | Englisch | 2025 | Springer | EAN 9783031946363 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand. Nº de ref. del artículo: 134146290
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Distributed Deep Learning and Explainable AI (XAI) in Industry 4.0 (Information Systems Engineering and Management, 55)
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Distributed Deep Learning and Explainable AI (XAI) in Industry 4.0
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Springer, Palgrave Macmillan Sep 2025, 2025
ISBN 10: 3031946367
ISBN 13: 9783031946363
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Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book is a comprehensive resource that delves into the integration of advanced artificial intelligence techniques within the context of modern industrial practices. It systematically explores how distributed deep learning methodologies can be effectively combined with explainable AI to enhance transparency in Industry 4.0 applications. In recent years, neural networks and other deep learning models have produced remarkable outcomes in a variety of fields, including image recognition, natural language processing, and decision-making. Concerns have been raised regarding the transparency and interpretability of these models as a result of their increasing intricacy. The demand for methodologies and approaches associated with explainable artificial intelligence (XAI) has consequently increased. The primary aim of XAI is to enhance the transparency and comprehensibility of deep learning model decision-making processes for stakeholders, irrespective of their technical expertise.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 432 pp. Englisch. Nº de ref. del artículo: 9783031946363
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Distributed Deep Learning and Explainable AI (XAI) in Industry 4.0
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Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book is a comprehensive resource that delves into the integration of advanced artificial intelligence techniques within the context of modern industrial practices. It systematically explores how distributed deep learning methodologies can be effectively combined with explainable AI to enhance transparency in Industry 4.0 applications. In recent years, neural networks and other deep learning models have produced remarkable outcomes in a variety of fields, including image recognition, natural language processing, and decision-making. Concerns have been raised regarding the transparency and interpretability of these models as a result of their increasing intricacy. The demand for methodologies and approaches associated with explainable artificial intelligence (XAI) has consequently increased. The primary aim of XAI is to enhance the transparency and comprehensibility of deep learning model decision-making processes for stakeholders, irrespective of their technical expertise. Nº de ref. del artículo: 9783031946363
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Distributed Deep Learning and Explainable AI (XAI) in Industry 4.0 (Information Systems Engineering and Management, 55)
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