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Published by TAYLOR & FRANCIS NP EXCLUSIVE(CBS), 2025
ISBN 10: 1032739398 ISBN 13: 9781032739397
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Add to basketHardcover. Condition: Brand New. 352 pages. 9.18x6.12x9.45 inches. In Stock.
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Published by Taylor & Francis Ltd, London, 2025
ISBN 10: 1032739398 ISBN 13: 9781032739397
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Hardcover. Condition: new. Hardcover. In recent years, deep learning has shown great potential in transforming various fields including healthcare. With the abundance of healthcare data being generated every day, there is a pressing need to develop efficient algorithms that can process and analyze this data to improve patient care and treatment outcomes.Handbook of Deep Learning Models for Healthcare Data Processing: Disease Prediction, Analysis, and Applications covers a wide range of deep learning models, techniques, and applications in healthcare data processing, analysis, and disease prediction, providing a comprehensive overview of the field. It focuses on the practical application of deep learning models in healthcare and offers step-by-step instructions for building and deploying models and using real-world examples. The handbook discusses the potential future applications of deep learning models in healthcare, such as precision medicine, personalized treatment, and clinical decision support. It also addresses the ethical considerations associated with the use of deep learning models in healthcare, such as privacy, security, and bias. It provides technical details on deep learning models, including their architecture, training methods, and optimization techniques, making it useful for data scientists and researchers.Written to be a comprehensive guide for healthcare professionals, researchers, and data analysts, this handbook is an essential need for those who are interested in using deep learning models to analyze and process healthcare data. It is also suitable for those who have a basic understanding of machine learning and want to learn more about the latest advancements in deep learning in healthcare. In recent years, deep learning has shown great potential in transforming various fields, including healthcare. With the abundance of healthcare data being generated daily, there is a pressing need to develop efficient algorithms that can process and analyze this data to improve patient care and treatment outcomes. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Add to basketCondition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Prof. (Dr.) Ajay Kumar is currently serving as Professor (Research Track), Department of Mechanical Engineering, School of Core Engineering, Faculty of Science, Technology & Architecture, Manipal University Jaipur, Rajasthan, India. .
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Published by Taylor & Francis Ltd, London, 2025
ISBN 10: 1032739398 ISBN 13: 9781032739397
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Add to basketHardcover. Condition: new. Hardcover. In recent years, deep learning has shown great potential in transforming various fields including healthcare. With the abundance of healthcare data being generated every day, there is a pressing need to develop efficient algorithms that can process and analyze this data to improve patient care and treatment outcomes.Handbook of Deep Learning Models for Healthcare Data Processing: Disease Prediction, Analysis, and Applications covers a wide range of deep learning models, techniques, and applications in healthcare data processing, analysis, and disease prediction, providing a comprehensive overview of the field. It focuses on the practical application of deep learning models in healthcare and offers step-by-step instructions for building and deploying models and using real-world examples. The handbook discusses the potential future applications of deep learning models in healthcare, such as precision medicine, personalized treatment, and clinical decision support. It also addresses the ethical considerations associated with the use of deep learning models in healthcare, such as privacy, security, and bias. It provides technical details on deep learning models, including their architecture, training methods, and optimization techniques, making it useful for data scientists and researchers.Written to be a comprehensive guide for healthcare professionals, researchers, and data analysts, this handbook is an essential need for those who are interested in using deep learning models to analyze and process healthcare data. It is also suitable for those who have a basic understanding of machine learning and want to learn more about the latest advancements in deep learning in healthcare. In recent years, deep learning has shown great potential in transforming various fields, including healthcare. With the abundance of healthcare data being generated daily, there is a pressing need to develop efficient algorithms that can process and analyze this data to improve patient care and treatment outcomes. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Buch. Condition: Neu. Handbook of Deep Learning Models for Healthcare Data Processing | Disease Prediction, Analysis, and Applications | Ajay Kumar (u. a.) | Buch | Einband - fest (Hardcover) | Englisch | 2025 | CRC Press | EAN 9781032739397 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
Language: English
Published by Taylor & Francis Ltd, London, 2025
ISBN 10: 1032739398 ISBN 13: 9781032739397
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Hardcover. Condition: new. Hardcover. In recent years, deep learning has shown great potential in transforming various fields including healthcare. With the abundance of healthcare data being generated every day, there is a pressing need to develop efficient algorithms that can process and analyze this data to improve patient care and treatment outcomes.Handbook of Deep Learning Models for Healthcare Data Processing: Disease Prediction, Analysis, and Applications covers a wide range of deep learning models, techniques, and applications in healthcare data processing, analysis, and disease prediction, providing a comprehensive overview of the field. It focuses on the practical application of deep learning models in healthcare and offers step-by-step instructions for building and deploying models and using real-world examples. The handbook discusses the potential future applications of deep learning models in healthcare, such as precision medicine, personalized treatment, and clinical decision support. It also addresses the ethical considerations associated with the use of deep learning models in healthcare, such as privacy, security, and bias. It provides technical details on deep learning models, including their architecture, training methods, and optimization techniques, making it useful for data scientists and researchers.Written to be a comprehensive guide for healthcare professionals, researchers, and data analysts, this handbook is an essential need for those who are interested in using deep learning models to analyze and process healthcare data. It is also suitable for those who have a basic understanding of machine learning and want to learn more about the latest advancements in deep learning in healthcare. In recent years, deep learning has shown great potential in transforming various fields, including healthcare. With the abundance of healthcare data being generated daily, there is a pressing need to develop efficient algorithms that can process and analyze this data to improve patient care and treatment outcomes. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In recent years, deep learning has shown great potential in transforming various fields, including healthcare. With the abundance of healthcare data being generated daily, there is a pressing need to develop efficient algorithms that can process and analyze this data to improve patient care and treatment outcomes.