2. Algorithms for Machine Learning – Preparing for Deep Learning
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
In the previous chapter, you read through how deep learning has been developed by looking back through the history of AI. As you should have noticed, machine learning and deep learning are inseparable. Indeed, you learned that deep learning is the developed method of machine learning algorithms.
In this chapter, as a pre-exercise to understand deep learning well, you will see the mode details of machine learning, and in particular, you will learn the actual code for the method of machine learning, which is closely related to deep learning.
In this chapter, we will cover the following topics:
- The core concepts of machine learning
- An overview of popular machine learning algorithms, especially focusing on neural networks
- Theories and implementations of machine learning algorithms related to deep learning: perceptrons, logistic regression, and multi-layer perceptrons
We will insert the source code of machine learning and deep learning with Java from this chapter. The version of JDK used in the code is 1.8, hence Java versions greater than 8 are required. Also, IntelliJ IDEA 14.1 is used for the IDE. We will use the external library from Chapter 5, Exploring Java Deep Learning Libraries – DL4J, ND4J, and More, so we are starting with a new Maven project.
The root package name of the code used in this book is DLWJ, the initials of Deep Learning with Java, and we will add a new package or a class under DLWJ as required. Please refer to the screenshot below, which shows the screen immediately after the new project is made:
There will be some names of variables and methods in the code that don't follow the Java coding standard. This is to improve your understanding together with some characters in the formulas to increase readability. Please bear this in mind in advance.
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