Artificial Neural Networks with Java Tools for Building Neural Network Applications.

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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 understan...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Livshin, Igor
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berkeley, CA : Apress L. P., 2021.
Edición:2nd ed.
Temas:
Neural networks (Computer science)
Java (Computer program language)
Neural Networks, Computer
Réseaux neuronaux (Informatique)
Java (Langage de programmation)
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Intro
  • Table of Contents
  • About the Author
  • About the Technical Reviewers
  • Acknowledgments
  • Introduction
  • Part I: Getting Started with Neural Networks
  • Chapter 1: Learning About Neural Networks
  • Biological and Artificial Neurons
  • Activation Functions
  • Summary
  • Chapter 2: Internal Mechanics of Neural Network Processing
  • Function to Be Approximated
  • Network Architecture
  • Forward Pass Calculation
  • Input Record 1
  • Input Record 2
  • Input Record 3
  • Input Record 4
  • Back-Propagation Pass
  • Function Derivative and Function Divergent
  • Most Commonly Used Function Derivatives
  • Summary
  • Chapter 3: Manual Neural Network Processing
  • Example: Manual Approximation of a Function at a Single Point
  • Building the Neural Network
  • Forward Pass Calculation
  • Hidden Layers
  • Output Layer
  • Backward Pass Calculation
  • Calculating Weight Adjustments for the Output-Layer Neurons
  • Calculating Adjustment for W211
  • Calculating Adjustment for W212
  • Calculating Adjustment for W213
  • Calculating Weight Adjustments for Hidden-Layer Neurons
  • Calculating Adjustment for W111
  • Calculating Adjustment for W112
  • Calculating Adjustment for W121
  • Calculating Adjustment for W122
  • Calculating Adjustment for W131
  • Calculating Adjustment for W132
  • Updating Network Biases
  • Back to the Forward Pass
  • Hidden Layers
  • Output Layer
  • Matrix Form of Network Calculation
  • Digging Deeper
  • Mini-Batches and Stochastic Gradient
  • Summary
  • Part II: Neural Network Java Development Environment
  • Chapter 4: Configuring Your Development Environment
  • Installing the Java Environment and NetBeans on Your Windows Machine
  • Installing the Encog Java Framework
  • Installing the XChart Package
  • Summary
  • Chapter 5: Neural Networks Development Using the Java Encog Framework
  • Example: Function Approximation Using Java Environment
  • Network Architecture
  • Normalizing the Input Datasets
  • Building the Java Program That Normalizes Both Datasets
  • Building the Neural Network Processing Program
  • Program Code
  • Debugging and Executing the Program
  • Processing Results for the Training Method
  • Testing the Network
  • Testing Results
  • Digging Deeper
  • Summary
  • Chapter 6: Neural Network Prediction Outside of the Training Range
  • Example: Approximating Periodic Functions Outside of the Training Range
  • Network Architecture for the Example
  • Program Code for the Example
  • Testing the Network
  • Example: Correct Way of Approximating Periodic Functions Outside of the Training Range
  • Preparing the Training Data
  • Network Architecture for the Example
  • Program Code for Example
  • Training Results for Example
  • Log of Testing Results for Example 3
  • Summary
  • Chapter 7: Processing Complex Periodic Functions
  • Example: Approximation of a Complex Periodic Function
  • Data Preparation

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