Artificial neural networks with Java : tools for building neural network applications / Igor Livshin.
Livshin, Igor.
2022
QA76.87
Available Online

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Title Artificial neural networks with Java : tools for building neural network applications / Igor Livshin.
Author Livshin, Igor.
Edition Second edition.
ISBN 9781484273685 (electronic bk.)
1484273680 (electronic bk.)
1484273672
9781484273678
DOI https://doi.org/10.1007/978-1-4842-7368-5
Publication Details New York, NY : Apress, 2022.
Language English
Description 1 online resource
Item Number 10.1007/978-1-4842-7368-5 doi
Call Number QA76.87
Dewey Decimal Classification 005.133
Summary Develop neural network applications using the Java environment. After learning the rules involved in neural network processing, this second edition shows you how to manually process your first neural network example. The book covers the internals of front and back propagation and helps you understand the main principles of neural network processing. You also will learn how to prepare the data to be used in neural network development and you will be able to suggest various techniques of data preparation for many unconventional tasks. This book discusses the practical aspects of using Java for neural network processing. You will know how to use the Encog Java framework for processing large-scale neural network applications. Also covered is the use of neural networks for approximation of non-continuous functions. In addition to using neural networks for regression, this second edition shows you how to use neural networks for computer vision. It focuses on image recognition such as the classification of handwritten digits, input data preparation and conversion, and building the conversion program. And you will learn about topics related to the classification of handwritten digits such as network architecture, program code, programming logic, and execution. The step-by-step approach taken in the book includes plenty of examples, diagrams, and screenshots to help you grasp the concepts quickly and easily. What You Will Learn Use Java for the development of neural network applications Prepare data for many different tasks Carry out some unusual neural network processing Use a neural network to process non-continuous functions Develop a program that recognizes handwritten digits.
Note Includes index.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed February 15, 2022).
Available in Other Form Print version: 9781484273678
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Part 1: Getting Started with Neural Networks
Chapter 1. Learning Neural Network
Chapter 2. Internal Mechanism of Neural Network Processing
Chapter 3. Manual Neural Network Processing
Part 2: Neural Network Java Development Environment
Chapter 4. Configuring Your Development Environment
Chapter 5. Neural Network Development Using Java Encog Framework
Chapter 6. Neural Network Prediction Outside of the Training Range
Chapter 7. Processing Complex Periodic Functions
Chapter 8. Approximating Non-Continuous Functions
Chapter 9. Approximation Continuous Functions with Complex Topology
Chapter 10. Using Neural Network for Classification of Objects
Chapter 11. Importance of Selecting the Correct Model
Chapter 12. Approximation of Functions in 3-D Space
Part 3: Introduction to Computer Vision
Chapter 13. Image Recognition
Chapter 14. Classification of Handwritten Digits. .

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