In this chapter, we've seen a few topics that make a neural network work better, either by improving its accuracy or by extending its knowledge. These techniques help a lot in designing solutions with artificial neural networks. The reader is welcome to apply this framework in any desired task that neural networks can be used on, in order to explore the enhanced power that these structures can have. Even simple details such as selecting input data may influence the entire learning process, as well as filtering bad data or eliminating redundant variables. We demonstrated two implementations, two strategies that help to improve the performance of a neural network: stochastic online learning and adaptive resonance theory. These methodologies enable the network to extend its knowledge and therefore adapt to new, changing environments.
Neural Network Programming with Java - Second Edition
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Neural Network Programming with Java - Second Edition
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Overview of this book
<p>Want to discover the current state-of-art in the field of neural networks that will let you understand and design new strategies to apply to more complex problems? This book takes you on a complete walkthrough of the process of developing basic to advanced practical examples based on neural networks with Java, giving you everything you need to stand out.</p>
<p>You will first learn the basics of neural networks and their process of learning. We then focus on what Perceptrons are and their features. Next, you will implement self-organizing maps using practical examples. Further on, you will learn about some of the applications that are presented in this book such as weather forecasting, disease diagnosis, customer profiling, generalization, extreme machine learning, and characters recognition (OCR). Finally, you will learn methods to optimize and adapt neural networks in real time.</p>
<p>All the examples generated in the book are provided in the form of illustrative source code, which merges object-oriented programming (OOP) concepts and neural network features to enhance your learning experience.</p>
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Current Title:
Neural Network Programming with Java - Second Edition
Table of Contents (19 chapters)
Neural Network Programming with Java Second Edition
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Free Chapter
Getting Started with Neural Networks
Getting Neural Networks to Learn
Perceptrons and Supervised Learning
Self-Organizing Maps
Forecasting Weather
Classifying Disease Diagnosis
Clustering Customer Profiles
Text Recognition
Optimizing and Adapting Neural Networks
Current Trends in Neural Networks
References
Index
Customer Reviews