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Index
Bandit Algorithms for Website Optimization Preface
Finding the Code for This Book Dealing with Jargon: A Glossary Conventions Used in This Book Using Code Examples Safari® Books Online How to Contact Us Acknowledgments
1. Two Characters: Exploration and Exploitation
The Scientist and the Businessman
Cynthia the Scientist Bob the Businessman Oscar the Operations Researcher
The Explore-Exploit Dilemma
2. Why Use Multiarmed Bandit Algorithms?
What Are We Trying to Do? The Business Scientist: Web-Scale A/B Testing
3. The epsilon-Greedy Algorithm
Introducing the epsilon-Greedy Algorithm Describing Our Logo-Choosing Problem Abstractly
What’s an Arm? What’s a Reward? What’s a Bandit Problem?
Implementing the epsilon-Greedy Algorithm Thinking Critically about the epsilon-Greedy Algorithm
4. Debugging Bandit Algorithms
Monte Carlo Simulations Are Like Unit Tests for Bandit Algorithms Simulating the Arms of a Bandit Problem Analyzing Results from a Monte Carlo Study
Approach 1: Track the Probability of Choosing the Best Arm Approach 2: Track the Average Reward at Each Point in Time Approach 3: Track the Cumulative Reward at Each Point in Time
Exercises
5. The Softmax Algorithm
Introducing the Softmax Algorithm Implementing the Softmax Algorithm Measuring the Performance of the Softmax Algorithm The Annealing Softmax Algorithm Exercises
6. UCB – The Upper Confidence Bound Algorithm
Introducing the UCB Algorithm Implementing UCB Comparing Bandit Algorithms Side-by-Side Exercises
7. Bandits in the Real World: Complexity and Complications
A/A Testing Running Concurrent Experiments Continuous Experimentation vs. Periodic Testing Bad Metrics of Success Scaling Problems with Good Metrics of Success Intelligent Initialization of Values Running Better Simulations Moving Worlds Correlated Bandits Contextual Bandits Implementing Bandit Algorithms at Scale
8. Conclusion
Learning Life Lessons from Bandit Algorithms A Taxonomy of Bandit Algorithms Learning More and Other Topics
Colophon Copyright
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