Distributed Deep Learning and Explainable Ai, Xai in Industry 4.0 - Couverture rigide

9783031946363: Distributed Deep Learning and Explainable Ai, Xai in Industry 4.0

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ISBN 10 : 3031946367 ISBN 13 : 9783031946363

Editeur : Springer International Publishing AG, 2025

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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.

Les informations fournies dans la section � Synopsis � peuvent faire r�f�rence � une autre �dition de ce titre.

�diteur: Springer International Publishing AG
Date d'�dition: 2025
Langue: anglais
ISBN 10: 3031946367
ISBN 13: 9783031946363
Reliure: Reli�
Nombre de pages: 430
�diteur: Krishnasamy Lalitha, Dhanaraj Rajesh Kumar, Pamucar Dragan, Ouaissa Mariya
Coordonn�es du fabricant: ProductSafety@springernature.com
ProductSafety@springernature.com

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64293
Allemagne

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Distributed Deep Learning and Explainable AI (XAI) in Industry 4.0 (eng)

Krishnasamy, Lalitha

Edit� par Springer, 2025

ISBN 10 : 3031946367 ISBN 13 : 9783031946363

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Distributed Deep Learning and Explainable AI (XAI) in Industry 4.0 (Hardcover)

Lalitha Krishnasamy

Edit� par Springer International Publishing AG, Cham, 2025

ISBN 10 : 3031946367 ISBN 13 : 9783031946363

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Hardcover. Etat : 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. N� de r�f. du vendeur 9783031946363

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Distributed Deep Learning and Explainable AI (XAI) in Industry 4.0

Lalitha Krishnasamy

Edit� par Springer, Berlin, Springer Nature Switzerland, Springer, 2025

ISBN 10 : 3031946367 ISBN 13 : 9783031946363

Neuf Couverture rigide

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Buch. Etat : 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. N� de r�f. du vendeur 9783031946363

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Distributed Deep Learning and Explainable AI (XAI) in Industry 4.0 (Hardcover)

Lalitha Krishnasamy

Edit� par Springer International Publishing AG, Cham, 2025

ISBN 10 : 3031946367 ISBN 13 : 9783031946363

Neuf Couverture rigide

Vendeur : CitiRetail, Stevenage, Royaume-Uni

�valuation du vendeur 5 sur 5 �toiles

Hardcover. Etat : 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. N� de r�f. du vendeur 9783031946363

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Distributed Deep Learning and Explainable AI (XAI) in Industry 4.0

Lalitha Krishnasamy (u. a.)

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ISBN 10 : 3031946367 ISBN 13 : 9783031946363

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Distributed Deep Learning and Explainable AI (XAI) in Industry 4.0 (Information Systems Engineering and Management, 55)

Edit� par Springer, 2025

ISBN 10 : 3031946367 ISBN 13 : 9783031946363

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Distributed Deep Learning and Explainable AI (XAI) in Industry 4.0

Lalitha Krishnasamy

Edit� par Springer, Palgrave Macmillan Sep 2025, 2025

ISBN 10 : 3031946367 ISBN 13 : 9783031946363

Neuf Couverture rigide

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Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

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Buch. Etat : 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. N� de r�f. du vendeur 9783031946363

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Distributed Deep Learning and Explainable AI (XAI) in Industry 4.0

Lalitha Krishnasamy

Edit� par Springer, Springer, 2025

ISBN 10 : 3031946367 ISBN 13 : 9783031946363

Neuf Couverture rigide

Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Buch. Etat : 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. N� de r�f. du vendeur 9783031946363

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