Neural Network Programming with Java - Second Edition

Create and unleash the power of neural networks by implementing professional Java code

Neural Network Programming with Java - Second Edition

Fabio M. Soares, Alan M. F. Souza

1 customer reviews
Create and unleash the power of neural networks by implementing professional Java code
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Book Details

ISBN 139781787126053
Paperback269 pages

Book Description

Want to discover the current state-of-art in the field of neural networks that will let you understand and design new strategies to apply to more complex problems? This book takes you on a complete walkthrough of the process of developing basic to advanced practical examples based on neural networks with Java, giving you everything you need to stand out.

You will first learn the basics of neural networks and their process of learning. We then focus on what Perceptrons are and their features. Next, you will implement self-organizing maps using practical examples. Further on, you will learn about some of the applications that are presented in this book such as weather forecasting, disease diagnosis, customer profiling, generalization, extreme machine learning, and characters recognition (OCR). Finally, you will learn methods to optimize and adapt neural networks in real time.

All the examples generated in the book are provided in the form of illustrative source code, which merges object-oriented programming (OOP) concepts and neural network features to enhance your learning experience.

Table of Contents

Chapter 1: Getting Started with Neural Networks
Discovering neural networks
Why artificial neural networks?
From ignorance to knowledge – learning process
Let the coding begin! Neural networks in practice
The neuron class
The NeuralLayer class
The ActivationFunction interface
The neural network class
Time to play!
Summary
Chapter 2: Getting Neural Networks to Learn
Learning ability in neural networks
Learning paradigms
The learning process
Examples of learning algorithms
Time to see the learning in practice!
Amazing, it learned! Or, did it really? A further step – testing
Summary
Chapter 3: Perceptrons and Supervised Learning
Supervised learning – teaching the neural net
A basic neural architecture – perceptrons
Multi-layer perceptrons
Learning in MLPs
Practical example 1 – the XOR case with delta rule and backpropagation
Practical example 2 – predicting enrolment status
Summary
Chapter 4: Self-Organizing Maps
Neural networks unsupervised learning
Unsupervised learning algorithms
Kohonen self-organizing maps
Summary
Chapter 5: Forecasting Weather
Neural networks for regression problems
Loading/selecting data
Choosing input and output variables
Preprocessing
Empirical design of neural networks
Summary
Chapter 6: Classifying Disease Diagnosis
Foundations of classification problems
Logistic regression
Neural networks for classification
Disease diagnosis with neural networks
Summary
Chapter 7: Clustering Customer Profiles
Clustering tasks
Applied unsupervised learning
Profiling
Summary
Chapter 8: Text Recognition
Pattern recognition
Neural networks in pattern recognition
Summary
Chapter 9: Optimizing and Adapting Neural Networks
Common issues in neural network implementations
Input selection
Online retraining
Adaptive neural networks
Summary
Chapter 10: Current Trends in Neural Networks
Deep learning
Deep architectures
Implementing a hybrid neural network
Summary

What You Will Learn

  • Develop an understanding of neural networks and how they can be fitted
  • Explore the learning process of neural networks
  • Build neural network applications with Java using hands-on examples
  • Discover the power of neural network’s unsupervised learning process to extract the intrinsic knowledge hidden behind the data
  • Apply the code generated in practical examples, including weather forecasting and pattern recognition
  • Understand how to make the best choice of learning parameters to ensure you have a more effective application
  • Select and split data sets into training, test, and validation, and explore validation strategies

Authors

Table of Contents

Chapter 1: Getting Started with Neural Networks
Discovering neural networks
Why artificial neural networks?
From ignorance to knowledge – learning process
Let the coding begin! Neural networks in practice
The neuron class
The NeuralLayer class
The ActivationFunction interface
The neural network class
Time to play!
Summary
Chapter 2: Getting Neural Networks to Learn
Learning ability in neural networks
Learning paradigms
The learning process
Examples of learning algorithms
Time to see the learning in practice!
Amazing, it learned! Or, did it really? A further step – testing
Summary
Chapter 3: Perceptrons and Supervised Learning
Supervised learning – teaching the neural net
A basic neural architecture – perceptrons
Multi-layer perceptrons
Learning in MLPs
Practical example 1 – the XOR case with delta rule and backpropagation
Practical example 2 – predicting enrolment status
Summary
Chapter 4: Self-Organizing Maps
Neural networks unsupervised learning
Unsupervised learning algorithms
Kohonen self-organizing maps
Summary
Chapter 5: Forecasting Weather
Neural networks for regression problems
Loading/selecting data
Choosing input and output variables
Preprocessing
Empirical design of neural networks
Summary
Chapter 6: Classifying Disease Diagnosis
Foundations of classification problems
Logistic regression
Neural networks for classification
Disease diagnosis with neural networks
Summary
Chapter 7: Clustering Customer Profiles
Clustering tasks
Applied unsupervised learning
Profiling
Summary
Chapter 8: Text Recognition
Pattern recognition
Neural networks in pattern recognition
Summary
Chapter 9: Optimizing and Adapting Neural Networks
Common issues in neural network implementations
Input selection
Online retraining
Adaptive neural networks
Summary
Chapter 10: Current Trends in Neural Networks
Deep learning
Deep architectures
Implementing a hybrid neural network
Summary

Book Details

ISBN 139781787126053
Paperback269 pages
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