Artificial Neural Networks with Java : Tools for Building Neural Network Applications / by Igor Livshin.

Format:

Book

Author/Creator:

Livshin, Igor, Author.

Language:

English

Subjects (All):

Java (Computer program language).

Artificial intelligence.

Open source software.

Computer programming.

Java.

Artificial Intelligence.

Open Source.

Local Subjects:

Java.

Artificial Intelligence.

Open Source.

Physical Description:

1 online resource (xix, 566 pages) : illustrations

Edition:

1st ed. 2019.

Place of Publication:

Berkeley, CA : Apress : Imprint: Apress, 2019.

System Details:

text file

Summary:

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. You will: 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 .

Contents:

Chapter 1. Learning Neural Networks

Chapter 2. Internal Mechanism of Neural Network Processing

Chapter 3. Manual Neural Network Processing

Chapter 4. Java Environment and Development Tools for Building Neural Network Applications

Chapter 5. Neural Network Development Using Java Framework

Chapter 6. Neural network Prediction outside of the Training Range

Chapter 7. Processing More Complex Periodic Functions

Chapter 8. Processing 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 a Correct Model

Chapter 12. Approximation of Functions in 3-D Space.

Notes:

Includes index.

ISBN:

9781484244210

1484244214

OCLC:

1102269317