In this chapter, we devoted a lot of time towards decision trees; what they are, how we can use them, and how they can benefit us in our applications. In our next chapter we are going to enter the world of Deep Belief Networks (DBNs), what they are, and how we can use them. We'll even talk a little bit about what a computer dreams, when it dreams!
- 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
Summary
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