Deep Learning Applications and Intelligent Decision Making in Engineering. Questo articolo non è disponibile.
Lingua: inglese
Editore: Igi Global Scientific Publishing, 2020
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Descrizione dell’articolo da parte del venditore
In English.
Codice articolo ria9781799821090_new
- Titolo
- Deep Learning Applications and Intelligent Decision Making in Engineering
- Editore
- Igi Global Scientific Publishing
- Anno di pubblicazione
- 2020
- Condizione
- New
- Rilegatura
- Brossura
- Lingua
- inglese
- ISBN 10
- 1799821099
- ISBN 13
- 9781799821090
- Peso dell'articolo
- 938 grammi
Deep learning includes a subset of machine learning for processing the unsupervised data with artificial neural network functions. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. When applied to engineering, deep learning can have a great impact on the decision-making process.
Deep Learning Applications and Intelligent Decision Making in Engineering is a pivotal reference source that provides practical applications of deep learning to improve decision-making methods and construct smart environments. Highlighting topics such as smart transportation, e-commerce, and cyber physical systems, this book is ideally designed for engineers, computer scientists, programmers, software engineers, research scholars, IT professionals, academicians, and postgraduate students seeking current research on the implementation of automation and deep learning in various engineering disciplines.
Deep Learning Applications and Intelligent Decision Making in Engineering is a pivotal reference source that provides practical applications of deep learning to improve decision-making methods and construct smart environments. Highlighting topics such as smart transportation, e-commerce, and cyber physical systems, this book is ideally designed for engineers, computer scientists, programmers, software engineers, research scholars, IT professionals, academicians, and postgraduate students seeking current research on the implementation of automation and deep learning in various engineering disciplines.
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