Data Centric Artificial Intelligence: A Beginner's Guide
Mahalle, Parikshit N.
Data Centric Artificial Intelligence: A Beginner's Guide [electronic resource] / by Parikshit N. Mahalle, Gitanjali R. Shinde, Yashwant S. Ingle, Namrata N. Wasatkar. - 1st ed. 2023. - XII, 127 p. 42 illus., 34 illus. in color. online resource. - Data-Intensive Research, 2731-5568 . - Data-Intensive Research, .
Acceso multiusuario
Introduction -- Model-Centric AI -- Data-Centric Principles for AI Engineering -- Mathematical Foundation for Data Centric AI -- Data-Centric AI -- Data-Centric AI in healthcare -- Data-Centric AI in Mechanical Engineering -- Data-Centric AI in Information Communication and Technology -- Conclusion.
This book discusses the best research roadmaps, strategies, and challenges in data-centric approach of artificial intelligence (AI) in various domains. It presents comparative studies of model-centric and data-centric AI. It also highlights different phases in data-centric approach and data-centric principles. The book presents prominent use cases of data-centric AI. It serves as a reference guide for researchers and practitioners in academia and industry.
9789819963539
Computational intelligence.
Artificial intelligence.
Artificial intelligence--Data processing.
Computational Intelligence.
Artificial Intelligence.
Data Science.
Q342
006.3
Data Centric Artificial Intelligence: A Beginner's Guide [electronic resource] / by Parikshit N. Mahalle, Gitanjali R. Shinde, Yashwant S. Ingle, Namrata N. Wasatkar. - 1st ed. 2023. - XII, 127 p. 42 illus., 34 illus. in color. online resource. - Data-Intensive Research, 2731-5568 . - Data-Intensive Research, .
Acceso multiusuario
Introduction -- Model-Centric AI -- Data-Centric Principles for AI Engineering -- Mathematical Foundation for Data Centric AI -- Data-Centric AI -- Data-Centric AI in healthcare -- Data-Centric AI in Mechanical Engineering -- Data-Centric AI in Information Communication and Technology -- Conclusion.
This book discusses the best research roadmaps, strategies, and challenges in data-centric approach of artificial intelligence (AI) in various domains. It presents comparative studies of model-centric and data-centric AI. It also highlights different phases in data-centric approach and data-centric principles. The book presents prominent use cases of data-centric AI. It serves as a reference guide for researchers and practitioners in academia and industry.
9789819963539
Computational intelligence.
Artificial intelligence.
Artificial intelligence--Data processing.
Computational Intelligence.
Artificial Intelligence.
Data Science.
Q342
006.3
