Externally indexed torrent

If you are the original uploader, contact staff to have it moved to your account

Textbook in PDF format

Learn practical and modern experimental methods used by engineers in technology and trading.
In Experimentation for Engineers: From A/B testing to Bayesian optimization you will learn how to
Design, run, and analyze an A/B test
Break the "feedback loops" cause by periodic retraining of ML models
Increase experimentation rate with multi-armed bandits
Tune multiple parameters experimentally with Bayesian optimization
Clearly define business metrics used for decision making
Identify and avoid the common pitfalls of experimentation
Experimentation for Engineers: From A/B testing to Bayesian optimization is a toolbox for optimizing machine learning systems, quantitative trading strategies, and more. You’ll start with a deep dive into tests like A/B testing, and then graduate to advanced techniques used to measure performance in highly competitive industries like finance and social media. The tests in this unique, practical guide will quickly reveal which approaches and features deliver real results for your business.
about the technology
Tuning your systems is best done by following established methods employed by high-performing teams like the ones led by author David Sweet. This book reveals experiments, tests, metrics, and industry-tested tools that will ensure your projects are constantly improving, delivering revenue, and ensuring user satisfaction.
about the book
Experimentation for Engineers: From A/B testing to Bayesian optimization teaches you reliable techniques for evaluating new features and fine-tuning parameters. You’ll learn to optimize production systems with methods that have been proven in highly competitive environments. Each method is fully explained using basic math and Python code, and illustrated with real-world use cases in quantitative trading, recommender systems, and ad serving.
welcome
Optimizing systems by experiment
A/B testing: Evaluating a modification to your system
Multi-armed bandits: Maximizing business metrics while experimenting
Response surface methodology: Optimizing continuous parameters
Contextual bandits: Making targeted decisions
Bayesian optimization: Automating experimental optimization
Managing business metrics
Practical considerations
Appendix A. Linear regression and the normal equations
Appendix B. One factor at a time
Appendix C. Gaussian process regression