English | 2022 | ISBN: 1617298158 | 326 pages | PDF,EPUB | 9.87 MB
Learn practical and modern expental methods used by eeers in technology and trading.
In Expentation for Eeers: 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 expentation rate with multi-armed bandits
Tune multiple parameters expentally with Bayesian optimization
Clearly define business metrics used for decision making
Identify and avoid the common pitfalls of expentation
Expentation for Eeers: From A/B testing to Bayesian optimization is a toolbox for optimizing machine learning systems, quantitative trading strats, 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-perfog teams like the ones led by author David Sweet. This book reveals expents, tests, metrics, and industry-tested tools that will ensure your projects are constantly improving, delivering revenue, and ensuring user satisfaction.
about the book
Expentation for Eeers: 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.
You'll learn how to evaluate the changes you make to your system and ensure that your testing doesn't undee revenue or other business metrics. By the you're done, you'll be able to seamlessly deploy expents in production while avoiding common pitfalls.
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