Description

Designed as an introductory level textbook on Artificial Neural Networks at the postgraduate and senior undergraduate levels in any branch of engineering, this self-contained and well-organized book highlights the need for new models of computing based on the fundamental principles of neural networks.

This book provides a comprehensive introduction to ANNs, covering both the theoretical foundations and practical applications. It starts with the basics, explaining the fundamental concepts of neural networks, including neurons, activation functions, and network architectures. The author then delves into more advanced topics such as training algorithms, deep learning, and recurrent neural networks.

One of the strengths of this book is its clarity and accessibility. The author presents complex ideas in a straightforward manner, making it suitable for both beginners and those with some prior knowledge of neural networks. Additionally, the book includes numerous examples and illustrations that help readers grasp the concepts and their real-world applications.

Professor Yegnanarayana compresses, into the covers of a single volume, his several years of rich experience, in teaching and research in the areas of speech processing, image processing, artificial intelligence and neural networks. He gives a masterly analysis of such topics as Basics of artificial neural networks, Functional units of artificial neural networks for pattern recognition tasks, Feedforward and Feedback neural networks, and Archi-tectures for complex pattern recognition tasks. Throughout, the emphasis is on the pattern processing feature of the neural networks. Besides, the presentation of real-world applications provides a practical thrust to the discussion.