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Neural Network Programming with Java
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  • Title Neural Network Programming with Java
  • Author(s) Alan M.F. Souza, Fabio M. Soares
  • Publisher: Packt Publishing - ebooks Account (January 15, 2016)
  • Paperback 244 pages
  • eBook PDF
  • Languages: English
  • ISBN-10: 178588090X
  • ISBN-13: 978-1785880902
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Book Description

This book gives you a complete walkthrough of the process of developing basic to advanced practical examples based on neural networks with Java.

This book is for Java developers with basic Java programming knowledge. No previous knowledge of neural networks is required as this book covers the concepts from scratch.

Vast quantities of data are produced every second. In this context, neural networks become a powerful technique to extract useful knowledge from large amounts of raw, seemingly unrelated data. One of the most preferred languages for neural network programming is Java as it is easier to write code using it, and most of the most popular neural network packages around already exist for Java. This makes it a versatile programming language for neural networks.

This book adopts a step-by-step approach to neural network development and provides many hands-on examples using Java programming. Each neural network concept is explored through real-world problems and is delivered in an easy-to-comprehend manner.

  • Get to grips with the basics of neural networks and what they are used for
  • Develop neural networks using hands-on examples
  • Explore and code the most widely-used learning algorithms to make your neural network learn from most types of data
  • Discover the power of neural network's unsupervised learning process to extract the intrinsic knowledge hidden behind the data
  • Apply the code generated in practical examples, including weather forecasting and pattern recognition
  • Understand how to make the best choice of learning parameters to ensure you have a more effective application
  • Select and split data sets into training, test, and validation, and explore validation strategies
  • Discover how to improve and optimize your neural network
About the Authors
  • Alan M.F. Souza is computer engineer from Instituto de Estudos Superiores da Amazonia (IESAM).
  • Fabio M. Soares holds a master's degree in applied computing from UFPA and is currently a PhD candidate at the same university. He has been designing neural network solutions since 2004 and has developed applications with this technique in several fields, ranging from telecommunications to chemistry process modeling, and his research topics cover supervised learning for data-driven modeling.
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