Recurrent Neural Networks

The text discusses recurrent neural networks for prediction and offers new insight into the learning algorithms, architectures, and stability of recurrent neural networks.

It discusses important topics including recurrent and folding networks, long short-term memory 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.

The book-

- 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, healthcare, multimedia (Audio/ Video), transportation, stock market, and logistics.

The text is primarily written for senior undergraduate, graduate students, researchers, and professionals in the fields of electrical, electronics and communication, and computer engineering.

The text examines a comparative study on the problem of real-world's applications' forecast, by using different classes of state-of-the-art recurrent neural networks. It provides an overview of the most important architectures and defines guidelines for configuring recurrent networks to predict real-valued time series. It will be a valuable resource for senior undergraduate, graduate students, researchers, and professionals in the fields of electrical, electronics and communication, and computer engineering.