Back Matter

Table of Contents

• Front Matter

• 1. Introduction

• 2. Learning Methods

• 3. Data Normalization

• 4. Data Collection, Preparation, Labeling, and Input Coding

• 5. Output Coding

• 6. Post-processing

• 7. Supervised Training Methods

• 8. Unsupervised Training Methods

• 9. Recurrent Neural Networks

• 10. A Plethora of Applications

• 11. Dealing with Limited Amounts of Data

• A. The Feedforward Neural Network

• B. Feature Saliency

• C. Matlab Code for Various Neural Networks

• D. Glossary of Terms

• Back Matter