Chapter 2: Getting Neural Networks to Learn
by Alan M. F. Souza, Fabio M. Soares
Neural Network Programming with Java - Second Edition
- 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
- Customer Feedback
- Preface
- 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
- 3. Perceptrons and Supervised Learning
- 4. Self-Organizing Maps
- Neural networks unsupervised learning
- Unsupervised learning algorithms
- 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
- 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
- Viewing the neural network output
- Empirical design of neural networks
- Summary
- 6. Classifying Disease Diagnosis
- 7. Clustering Customer Profiles
- 8. Text Recognition
- 9. Optimizing and Adapting Neural Networks
- 10. Current Trends in Neural Networks
- 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
- Index
Sejnowski, Terrence, J., Neural Network Learning Algorithms, Neural Computers Vol. 41, Springer Study Edition, pp. 291-300, 1989.
Hguyen, Derrick H., Widrow, Bernard, Neural Networks for Self-Learning Control Systems, IEEE Control Systems Magazine, April 1990.
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