Carta PAYBACK di Mondadori
Su Mondadori Store, con la tua carta PAYBACK ti premi ad ogni acquisto.
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
Scrivi una recensione per "Data-Centric Artificial Intelligence for Multidisciplinary Applications"
Torna in cima
Leggi un estratto
dai un voto
