Description: The text discusses recurrent neural networks for prediction and offers new insights into the learning 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, learning algorithm, neural turning 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. FEATURES Covers computational analysis and understanding of natural languages Discusses applications of recurrent neural network in e-Healthcare Provides case studies in every chapter with respect to real-world scenarios Examines open issues with natural language, health care, multimedia (Audio/Video), transportation, stock market, and logistics The 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.
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Location: Wiri
End Time: 2024-11-16T08:45:42.000Z
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EAN: 9781032081649
UPC: 9781032081649
ISBN: 9781032081649
MPN: N/A
Format: Hardback, 396 pages
Author: Amit Kumar Tyagi (Edited by)
Book Title: Recurrent Neural Networks: Concepts and Applicatio
Item Height: 2.4 cm
Item Length: 23.4 cm
Item Weight: 0.45 kg
Item Width: 15.6 cm
Language: Eng
Publisher: Taylor & Francis Ltd
