Neural Network Programming with Java · 2nd Edition by Soares, Fabio M. -- Read -- Imperial Library of Trantor

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Neural Network Programming with Java Second Edition

Table of Contents Neural Network Programming with Java Second Edition Credits About the Authors About the Reviewer www.PacktPub.com

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What this book covers What you need for this book Who this book is for Conventions Reader feedback Customer support

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1. Getting Started with Neural Networks

Discovering neural networks Why artificial neural networks?

How neural networks are arranged The very basic element – artificial neuron Giving life to neurons – activation function The flexible values – weights An extra parameter – bias The parts forming the whole – layers Learning about neural network architectures Monolayer networks Multilayer networks Feedforward networks Feedback 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

2. Getting Neural Networks to Learn

Learning ability in neural networks

How learning helps solving problems

Learning paradigms

Supervised learning Unsupervised learning

The learning process

The cost function finding the way down to the optimum Learning in progress - weight update Calculating the cost function General error and overall error Can the neural network learn forever? When is it good to stop?

Examples of learning algorithms

The delta rule The learning rate Implementing the delta rule The core of the delta rule learning - train and calcNewWeight methods Another learning algorithm - Hebbian learning Adaline

Time to see the learning in practice!

Teaching the neural network – the training dataset

Amazing, it learned! Or, did it really? A further step – testing

Overfitting and overtraining

Summary

3. Perceptrons and Supervised Learning

Supervised learning – teaching the neural net

Classification – finding the appropriate class Regression – mapping real inputs to outputs

A basic neural architecture – perceptrons

Applications and limitations Linear separation The XOR case

Multi-layer perceptrons

MLP properties MLP weights Recurrent MLP Coding an MLP

Learning in MLPs

Backpropagation algorithm The momentum Coding the backpropagation Levenberg-Marquardt algorithm Coding the Levenberg-Marquardt with matrix algebra Extreme learning machines

Practical example 1 – the XOR case with delta rule and backpropagation Practical example 2 – predicting enrolment status Summary

4. Self-Organizing Maps

Neural networks unsupervised learning Unsupervised learning algorithms

Competitive learning Competitive layer

Kohonen self-organizing maps

Extending the neural network code to Kohonen Zero-dimensional SOM One-dimensional SOM Two-dimensional SOM 2D competitive layer SOM learning algorithm Effect of neighboring neurons – the neighborhood function The learning rate A new class for competitive learning Visualizing the SOMs Plotting 2D training datasets and neuron weights Testing Kohonen learning

Summary

5. Forecasting Weather

Neural networks for regression problems Loading/selecting data

Building auxiliary classes Getting a dataset from a CSV file Building time series Dropping NaNs Getting weather data Weather variables

Choosing input and output variables Preprocessing

Normalization Adapting NeuralDataSet to handle normalization Adapting the learning algorithm to normalization Java implementation of weather forecasting Collecting weather data Delaying variables Loading the data and beginning to play! Let's perform a correlation analysis Creating neural networks Training and test

Training the neural network Plotting the error

Viewing the neural network output

Empirical design of neural networks

Designing experiments Results and simulations

Summary

6. Classifying Disease Diagnosis

Foundations of classification problems

Categorical data Working with categorical data

Logistic regression

Multiple classes versus binary classes Confusion matrix Sensitivity and specificity Implementing a confusion matrix

Neural networks for classification Disease diagnosis with neural networks

Breast cancer Diabetes

Summary

7. Clustering Customer Profiles

Clustering tasks

Cluster analysis Cluster evaluation and validation Implementation External validation

Applied unsupervised learning

Kohonen neural network

Profiling

Pre-processing Implementation in Java Card – credit analysis for customer profiling Product profiling How many clusters?

Summary

8. Text Recognition

Pattern recognition

Defined classes Undefined classes

Neural networks in pattern recognition

Data pre-processing Text recognition (optical character recognition) Digit recognition Digit representation Implementation in Java Generating data Neural architecture Experiments Results

Summary

9. Optimizing and Adapting Neural Networks

Common issues in neural network implementations Input selection

Data correlation Transforming data Dimensionality reduction Data filtering Cross-validation Structure selection

Online retraining

Stochastic online learning Implementation Application

Adaptive neural networks

Adaptive resonance theory Implementation

Summary

10. Current Trends in Neural Networks

Deep learning Deep architectures

How to implement deep learning in Java

Hybrid systems

Neuro-fuzzy Neuro-genetic

Implementing a hybrid neural network Summary

A. References

Chapter 1: Getting Started with Neural Networks Chapter 2: Getting Neural Networks to Learn Chapter 3: Perceptrons and Supervised Learning Chapter 4: Self-Organizing Maps Chapter 5: Forecasting Weather Chapter 6: Classifying Disease Diagnosis Chapter 7: Clustering Customer Profiles Chapter 8: Text Recognition Chapter 9: Optimizing and Adapting Neural Networks Chapter 10: Current Trends in Neural Networks

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