Recurrent Neural Networks: Concepts and Applications

The text covers various topics related to recurrent neural networks, including their learning algorithms, architectures, stability, and applications in air pollutant modeling, attractor discovery, ECG signal processing, and speech processing. It is written for undergraduate and graduate students, researchers, and industry professionals.

Format: Hardback
Length: 396 pages
Publication date: 08 August 2022
Publisher: Taylor & Francis Ltd

The field of artificial intelligence has witnessed a remarkable resurgence in recent years, driven by the remarkable capabilities of recurrent neural networks (RNNs) in various domains such as prediction, language modeling, and image recognition. RNNs are a type of neural network that is designed to process sequential data, such as text, speech, or images, and have the ability to remember and recall information over long periods of time.

One of the key features of RNNs is their ability to learn complex patterns and relationships in the data. This is achieved through a process called backpropagation, which involves adjusting the weights of the network's neurons based on the errors that are made in the predictions. RNNs have been used to achieve state-of-the-art results in a wide range of tasks, including speech recognition, image captioning, and machine translation.

In this book, we will explore the fundamentals of RNNs, including their learning algorithms, architectures, and stability. We will also discuss important topics such as recurrent and folding networks, long short-term memory (LSTM) networks, gated recurrent unit neural networks, language modeling, neural network models, activation functions, feed-forward networks, learning algorithms, neural turning machines, and approximation ability.

We will also examine diverse applications of RNNs in areas such as air pollutant modeling and prediction, attractor discovery and chaos, ECG signal processing, and speech processing. Case studies are interspersed throughout the book to better understand the practical implications of RNNs in real-world scenarios.

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. It provides a comprehensive and up-to-date introduction to the field of RNNs, and will be a valuable resource for anyone interested in learning more about this powerful tool for artificial intelligence.

Weight: 920g
Dimension: 234 x 156 (mm)
ISBN-13: 9781032081649