Recurrent Neural Networks

by: Amit Kumar Tyagi (Editor),Ajith Abraham (Editor)

Publisher:

Edition: 1st

Publication Date: 2022/8/8

Language: English

Print Length: 412 pages

ISBN-10: 1032081643

ISBN-13: 9781032081649

Book Description

The text discusses recurrent neural networks for prediction and offers new insights into the leaing algorithms, architectures, and stability of recurrent neural networks. It discusses important topics including recurrent and folding networks, long short-term memory (LSTM) networks, gated recurrent unit neural networks, language modeling, neural network model, activation function, feed-forward network, leaing algorithm, neural tuing machines, and approximation ability. The text discusses diverse applications in areas including air pollutant modeling and prediction, attractor discovery and chaos, ECG signal processing, and speech processing. Case studies are interspersed throughout the book for better understanding.FEATURESCovers computational analysis and understanding of natural languagesDiscusses applications of recurrent neural network in e-HealthcareProvides case studies in every chapter with respect to real-world scenariosExamines open issues with natural language, health care, multimedia (Audio/Video), transportation, stock market, and logisticsThe text is primarily written for undergraduate and graduate students, researchers, and industry professionals in the fields of electrical, electronics and communication, and computer engineering/information technology.

About the Author

The text discusses recurrent neural networks for prediction and offers new insights into the leaing algorithms, architectures, and stability of recurrent neural networks. It discusses important topics including recurrent and folding networks, long short-term memory (LSTM) networks, gated recurrent unit neural networks, language modeling, neural network model, activation function, feed-forward network, leaing algorithm, neural tuing machines, and approximation ability. The text discusses diverse applications in areas including air pollutant modeling and prediction, attractor discovery and chaos, ECG signal processing, and speech processing. Case studies are interspersed throughout the book for better understanding.FEATURESCovers computational analysis and understanding of natural languagesDiscusses applications of recurrent neural network in e-HealthcareProvides case studies in every chapter with respect to real-world scenariosExamines open issues with natural language, health care, multimedia (Audio/Video), transportation, stock market, and logisticsThe text is primarily written for undergraduate and graduate students, researchers, and industry professionals in the fields of electrical, electronics and communication, and computer engineering/information technology.