Credits
by Alan M. F. Souza, Fábio M. Soares, Boštjan Kaluža, Yusuke Sugomori
Deep Learning: Practical Neural Networks with Java
- Deep Learning: Practical Neural Networks with Java
- Table of Contents
- Deep Learning: Practical Neural Networks with Java
- Deep Learning: Practical Neural Networks with Java
- Credits
- Preface
- 1. Java Deep Learning Essentials
- 1. Deep Learning Overview
- 2. Algorithms for Machine Learning – Preparing for Deep Learning
- 3. Deep Belief Nets and Stacked Denoising Autoencoders
- 4. Dropout and Convolutional Neural Networks
- 5. Exploring Java Deep Learning Libraries – DL4J, ND4J, and More
- 6. Approaches to Practical Applications – Recurrent Neural Networks and More
- 7. Other Important Deep Learning Libraries
- 8. What's Next?
- 2. Machine Learning in Java
- 1. Applied Machine Learning Quick Start
- 2. Java Libraries and Platforms for Machine Learning
- 3. Basic Algorithms – Classification, Regression, and Clustering
- 4. Customer Relationship Prediction with Ensembles
- 5. Affinity Analysis
- 6. Recommendation Engine with Apache Mahout
- 7. Fraud and Anomaly Detection
- 8. Image Recognition with Deeplearning4j
- 9. Activity Recognition with Mobile Phone Sensors
- 10. Text Mining with Mallet – Topic Modeling and Spam Detection
- 11. What is Next?
- A. References
- 3. Neural Network Programming with Java, Second Edition
- 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
- Bibliography
- 1. Getting Started with Neural Networks
- Index
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