Artificial Neural Networks with Java: Tools for Building Neural Network Applications
- 3h 59m
- Igor Livshin
- Apress
- 2019
Use Java to develop neural network applications in this practical book. After learning the rules involved in neural network processing, you will manually process the first neural network example. This covers the internals of front and back propagation, and facilitates the understanding of the main principles of neural network processing. Artificial Neural Networks with Java also teaches you how to prepare the data to be used in neural network development and suggests various techniques of data preparation for many unconventional tasks.
The next big topic discussed in the book is using Java for neural network processing. You will use the Encog Java framework and discover how to do rapid development with Encog, allowing you to create large-scale neural network applications.
The book also discusses the inability of neural networks to approximate complex non-continuous functions, and it introduces the micro-batch method that solves this issue. The step-by-step approach includes plenty of examples, diagrams, and screen shots to help you grasp the concepts quickly and easily.
What You Will Learn
- Prepare your data for many different tasks
- Carry out some unusual neural network tasks
- Create neural network to process non-continuous functions
- Select and improve the development model
Who This Book Is For
Intermediate machine learning and deep learning developers who are interested in switching to Java.
About the Author
Igor Livshin is a senior architect with extensive experience in developing large-scale applications. He worked for many years for two large insurance companies: CNN and Blue Cross & Blue Shield of Illinois. He currently works as a senior researcher at DevTechnologies specializing in AI and neural networks. Igor has a master’s degree in computer science from the Institute of Technology in Odessa, Russia/Ukraine.
In this Book
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Learning about Neural Networks
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Internal Mechanics of Neural Network Processing
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Manual Neural Network Processing
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Configuring your Development Environment
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Neural Network Development Using the Java Encog Framework
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Neural Network Prediction Outside the Training Range
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Processing Complex Periodic Functions
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Approximating Noncontinuous Functions
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Approximating Continuous Functions with Complex Topology
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Using Neural Networks to Classify Objects
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The Importance of Selecting the Correct Model
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Approximation of Functions in 3D Space
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