Data Centric Artificial Intelligence Multidisciplinary (30 results)

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Condition: New. Dr. Parikshit N. Mahalle is a senior member of the IEEE and is Professor and Head of Department of Artificial Intelligence and Data Science at Vishwakarma Institute of Information Technology, Pune, India. He completed his Ph.D. from Aalb.

Taschenbuch. Condition: Neu. Neuware - This book explores the need for a datäcentric AI approach and its application in the multidisciplinary domain, compared to a model¿centric approach. It examines the methodologies for datäcentric approaches, the use of datäcentric approaches in different domains, the need for edge AI and how it differs from cloud¿based AI. It discusses the new category of AI technology, 'datäcentric AI' (DCAI), which focuses on comprehending, utilizing, and reaching conclusions from data. By adding machine learning and big data analytics tools, datäcentric AI modifies this by enabling it to learn from data rather than depending on algorithms. It can therefore make wiser choices and deliver more precise outcomes. Additionally, it has the potential to be significantly more scalable than conventional AI methods.¿ Includes a collection of case studies with experimentation results to adhere to the practical approaches¿ Examines challenges in dataset generation, synthetic datasets, analysis, and prediction algorithms in stochastic ways¿ Discusses methodologies to achieve accurate results by improving the quality of data¿ Comprises cases in healthcare and agriculture with implementation and impact of quality data in building AI applications.…

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Hardback. Condition: New. This book explores the need for a data-centric AI approach and its application in the multidisciplinary domain, compared to a model-centric approach. It examines the methodologies for data-centric approaches, the use of data-centric approaches in different domains, the need for edge AI and how it differs from cloud-based AI. It discusses the new category of AI technology, "data-centric AI" (DCAI), which focuses on comprehending, utilizing, and reaching conclusions from data. By adding machine learning and big data analytics tools, data-centric AI modifies this by enabling it to learn from data rather than depending on algorithms. It can therefore make wiser choices and deliver more precise outcomes. Additionally, it has the potential to be significantly more scalable than conventional AI methods. Includes a collection of case studies with experimentation results to adhere to the practical approaches. Examines challenges in dataset generation, synthetic datasets, analysis, and prediction algorithms in stochastic ways. Discusses methodologies to achieve accurate results by improving the quality of data. Comprises cases in healthcare and agriculture with implementation and impact of quality data in building AI applications.…

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Hardback. Condition: New. This book explores the need for a data-centric AI approach and its application in the multidisciplinary domain, compared to a model-centric approach. It examines the methodologies for data-centric approaches, the use of data-centric approaches in different domains, the need for edge AI and how it differs from cloud-based AI. It discusses the new category of AI technology, "data-centric AI" (DCAI), which focuses on comprehending, utilizing, and reaching conclusions from data. By adding machine learning and big data analytics tools, data-centric AI modifies this by enabling it to learn from data rather than depending on algorithms. It can therefore make wiser choices and deliver more precise outcomes. Additionally, it has the potential to be significantly more scalable than conventional AI methods. Includes a collection of case studies with experimentation results to adhere to the practical approaches. Examines challenges in dataset generation, synthetic datasets, analysis, and prediction algorithms in stochastic ways. Discusses methodologies to achieve accurate results by improving the quality of data. Comprises cases in healthcare and agriculture with implementation and impact of quality data in building AI applications.…

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Paperback. Condition: new. Paperback. This book explores the need for a datacentric AI approach and its application in the multidisciplinary domain, compared to a modelcentric approach. It examines the methodologies for datacentric approaches, the use of datacentric approaches in different domains, the need for edge AI and how it differs from cloudbased AI. It discusses the new category of AI technology, "datacentric AI" (DCAI), which focuses on comprehending, utilizing, and reaching conclusions from data. By adding machine learning and big data analytics tools, datacentric AI modifies this by enabling it to learn from data rather than depending on algorithms. It can therefore make wiser choices and deliver more precise outcomes. Additionally, it has the potential to be significantly more scalable than conventional AI methods. Includes a collection of case studies with experimentation results to adhere to the practical approaches Examines challenges in dataset generation, synthetic datasets, analysis, and prediction algorithms in stochastic ways Discusses methodologies to achieve accurate results by improving the quality of data Comprises cases in healthcare and agriculture with implementation and impact of quality data in building AI applications This book explores the need for a data-centric AI approach and its application in the multidisciplinary domain, compared to a model-centric approach. 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. …

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Dr. Parikshit N. Mahalle is a senior member of the IEEE and is Professor and Head of Department of Artificial Intelligence and Data Science at Vishwakarma Institute of Information Technology, Pune, India. He completed his Ph.D. from Aalb.…

Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book explores the need for a data centric AI approach and its application in the multidisciplinary domain, compared to a model centric approach. It examines the methodologies for data centric approaches, the use of data centric approaches in different domains, the need for edge AI and how it differs from cloud based AI. It discusses the new category of AI technology, 'data centric AI' (DCAI), which focuses on comprehending, utilizing, and reaching conclusions from data. By adding machine learning and big data analytics tools, data centric AI modifies this by enabling it to learn from data rather than depending on algorithms. It can therefore make wiser choices and deliver more precise outcomes. Additionally, it has the potential to be significantly more scalable than conventional AI methods.- Includes a collection of case studies with experimentation results to adhere to the practical approaches- Examines challenges in dataset generation, synthetic datasets, analysis, and prediction algorithms in stochastic ways- Discusses methodologies to achieve accurate results by improving the quality of data- Comprises cases in healthcare and agriculture with implementation and impact of quality data in building AI applications 310 pp. Englisch. …

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Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book explores the need for a data centric AI approach and its application in the multidisciplinary domain, compared to a model centric approach. It examines the methodologies for data centric approaches, the use of data centric approaches in different domains, the need for edge AI and how it differs from cloud based AI. It discusses the new category of AI technology, 'data centric AI' (DCAI), which focuses on comprehending, utilizing, and reaching conclusions from data. By adding machine learning and big data analytics tools, data centric AI modifies this by enabling it to learn from data rather than depending on algorithms. It can therefore make wiser choices and deliver more precise outcomes. Additionally, it has the potential to be significantly more scalable than conventional AI methods.- Includes a collection of case studies with experimentation results to adhere to the practical approaches- Examines challenges in dataset generation, synthetic datasets, analysis, and prediction algorithms in stochastic ways- Discusses methodologies to achieve accurate results by improving the quality of data- Comprises cases in healthcare and agriculture with implementation and impact of quality data in building AI applications.…