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Artificial Neural Networks with Java

Tools for Building Neural Network Applications

  • Book
  • © 2019

Overview

Authors:
  1. Igor Livshin

    1. Chicago, USA

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  • Explains neural network development in the Java environment
  • Shows examples of manual network processing
  • Discusses solutions to unconventional neural network processing

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Table of contents (12 chapters)

  1. Front Matter

    Pages i-xix
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  2. Learning About Neural Networks

    • Igor Livshin
    Pages 1-5

  3. Internal Mechanics of Neural Network Processing

    • Igor Livshin
    Pages 7-19

  4. Manual Neural Network Processing

    • Igor Livshin
    Pages 21-46

  5. Configuring Your Development Environment

    • Igor Livshin
    Pages 47-53

  6. Neural Network Development Using the Java Encog Framework

    • Igor Livshin
    Pages 55-108

  7. Neural Network Prediction Outside the Training Range

    • Igor Livshin
    Pages 109-163

  8. Processing Complex Periodic Functions

    • Igor Livshin
    Pages 165-206

  9. Approximating Noncontinuous Functions

    • Igor Livshin
    Pages 207-288

  10. Approximating Continuous Functions with Complex Topology

    • Igor Livshin
    Pages 289-392

  11. Using Neural Networks to Classify Objects

    • Igor Livshin
    Pages 393-447

  12. The Importance of Selecting the Correct Model

    • Igor Livshin
    Pages 449-529

  13. Approximation of Functions in 3D Space

    • Igor Livshin
    Pages 531-561

  14. Back Matter

    Pages 563-566
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Keywords

About this book

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.


Authors and Affiliations

  • Chicago, USA

    Igor Livshin

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.   

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