This object defines the nature of each decision variable that the tree and nodes can process. The values can be ranges, continuous, or discrete.
- Title Page
- Copyright and Credits
- Packt Upsell
- Contributors
- Preface
- Machine Learning Basics
- Introduction to machine learning
- Data mining
- Artificial Intelligence
- Bio-AI
- Deep learning
- Probability and statistics
- Approaching your machine learning project
- Iris dataset
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Build, buy, or open source
- Additional reading
- Summary
- References
- ReflectInsight – Real-Time Monitoring
- Bayes Intuition – Solving the Hit and Run Mystery and Performing Data Analysis
- Risk versus Reward – Reinforcement Learning
- Fuzzy Logic – Navigating the Obstacle Course
- Color Blending – Self-Organizing Maps and Elastic Neural Networks
- Facial and Motion Detection – Imaging Filters
- Encyclopedias and Neurons – Traveling Salesman Problem
- Should I Take the Job – Decision Trees in Action
- Deep Belief – Deep Networks and Dreaming
- Microbenchmarking and Activation Functions
- Intuitive Deep Learning in C# .NET
- Quantum Computing – The Future
Decision variable
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