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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