Processing ......
FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World
 
Neural Networks - A Systematic Introduction
🌠 Top Free JavaScript Books - 100% Free or Open Source!
  • Title: Neural Networks - A Systematic Introduction
  • Author(s): Raul Rojas
  • Publisher: Springer; 1 edition (July 12, 1996)
  • Paperback: 502 pages
  • eBook: PDF (509 pages) and PDF Files
  • Language: English
  • ISBN-10: 3540605053
  • ISBN-13: 978-3540605058
  • Share This:  

Book Description

Neural networks are a computing paradigm that is finding increasing attention among computer scientists. In this book, theoretical laws and models previously scattered in the literature are brought together into a general theory of artificial neural nets. Always with a view to biology and starting with the simplest nets, it is shown how the properties of models change when more general computing elements and net topologies are introduced.

Each chapter contains examples, numerous illustrations, and a bibliography. The book is aimed at readers who seek an overview of the field or who wish to deepen their knowledge. It is suitable as a basis for university courses in neurocomputing.

About the Authors
  • Raul Rojas is a professor of Artificial Intelligence at Deparment of Computer Science and Mathematics at The Free University of Berlin, Germany, and University of Nevada, Reno, USA.
Reviews and Rating: Related Book Categories: Read and Download Links: Similar Books:
  • Neural Network Design (Martin T. Hagan)

    This book provides a clear and detailed coverage of fundamental neural network architectures and learning rules. It emphasizes a coherent presentation of the principal neural networks, methods for training them and their applications to practical problems.

  • Neural Network Learning: Theoretical Foundations

    This book describes recent theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions.

  • Neural Networks and Deep Learning (Michael Nielsen)

    Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you the core concepts behind neural networks and deep learning.

  • Introduction to Artificial Neural Networks (Jan Larsen, et al.)

    This fundamental book on Artificial Neural Networks (ANN) has its emphasis on clear concepts, ease of understanding and simple examples. It presents a large variety of standard neural networks with architecture, algorithms and applications.

  • Applied Artificial Neural Networks (Christian Dawson)

    This book focuses on the application of neural networks to a diverse range of fields and problems. It collates contributions concerning neural network applications in areas such as engineering, hydrology and medicine.

  • An Introduction to Neural Networks (Kevin Gurney)

    With an easy to understand format using graphical illustrations and multidisciplinary scientific context, this book fills the gap in the market for neural networks for multi-dimensional scientific data, and relates neural networks to statistics.

  • Neural Networks (Rolf Pfeifer, et al)

    Beginning with an introductory discussion on the role of neural networks in scientific data analysis, this book provides a solid foundation of basic neural network concepts. It is a systematic introduction to neural networks, biological foundation.

Book Categories
:
Other Categories
Resources and Links