Data-Centric Artificial Intelligence for Multidisciplinary Applications by Parikshit N. Mahalle, Namrata N. Wasatkar, Gitanjali R. Shinde
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Overview: This book explores the need for a Data-Centric Artificial Intelligence (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. Data-Centric Artificial Intelligence (AI) denotes an approach within AI and Machine Learning (ML) that places significant emphasis on the pivotal role of meticulously curated, high‑quality data in the development and implementation of AI models and systems. Under this paradigm, data assumes the bedrock upon which AI algorithms are constructed and honed, and its effective handling, preprocessing, and analysis stand as pivotal factors for achieving precise and dependable AI outcomes.
Genre: Non-Fiction > Tech & Devices
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Requirements: .PDF reader, 19.4 MB
Overview: This book explores the need for a Data-Centric Artificial Intelligence (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. Data-Centric Artificial Intelligence (AI) denotes an approach within AI and Machine Learning (ML) that places significant emphasis on the pivotal role of meticulously curated, high‑quality data in the development and implementation of AI models and systems. Under this paradigm, data assumes the bedrock upon which AI algorithms are constructed and honed, and its effective handling, preprocessing, and analysis stand as pivotal factors for achieving precise and dependable AI outcomes.
Genre: Non-Fiction > Tech & Devices
Download Instructions:
https://tbit.to/hvt1scfuyroj.html
https://katfile.com/pybrw1s9v4v3/Data-C ... s.pdf.html
https://rapidgator.net/file/5291753d364 ... s.pdf.html
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