http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Tools for Building Neural Network Applications
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 Who This Book Is For Intermediate machine learning and deep learning developers who are interested in switching to Java.
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
Description based upon print version of record.
Reflecting Function Topology in the Data.