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About Experimentation for Engineers: From A B testing to Bayesian optimization
Optimize the performance of your systems with practical experiments used by engineers in the world’s most competitive industries.In Experimentation for From A B testing to Bayesian optimization you will learn how Design, run, and analyze an A B testBreak the and “feedback loops and ” caused by periodic retraining of ML modelsIncrease experimentation rate with multi-armed banditsTune multiple parameters experimentally with Bayesian optimizationClearly define business metrics used for decision-makingIdentify and avoid the common pitfalls of experimentationExperimentation for From A B testing to Bayesian optimization is a toolbox of techniques for evaluating new features and fine-tuning parameters. You’ll start with a deep dive into methods like A B testing, and then graduate to advanced techniques used to measure performance in industries such as finance and social media. Learn how to evaluate the changes you make to your system and ensure that your testing doesn’t undermine revenue or other business metrics. By the time you’re done, you’ll be able to seamlessly deploy experiments in production while avoiding common pitfalls.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the technologyDoes my software really work? Did my changes make things better or worse? Should I trade features for performance? Experimentation is the only way to answer questions like these. This unique book reveals sophisticated experimentation practices developed and proven in the world’s most competitive industries that will help you enhance machine learning systems, software applications, and quantitative trading solutions.About the bookExperimentation for From A B testing to Bayesian optimization delivers a toolbox of processes for optimizing software systems. You’ll start by learning the limits of A B testing, and then graduate to advanced experimentation strategies that take advantage of machine learning and probabilistic methods. The skills you’ll master in this practical guide will help you minimize the costs of experimentation and quickly reveal which approaches and features deliver the best business results.What’s insideDesign, run, and analyze an A B testBreak the “feedback loops” caused by periodic retraining of ML modelsIncrease experimentation rate with multi-armed banditsTune multiple parameters experimentally with Bayesian optimizationAbout the readerFor ML and software engineers looking to extract the most value from their systems. Examples in Python and NumPy.About the authorDavid Sweet has worked as a quantitative trader at GETCO and a machine learning engineer at Instagram. He teaches in the AI and Data Science master’s programs at Yeshiva University.Table of Contents1 Optimizing systems by experiment2 A B Evaluating a modification to your system3 Multi-armed Maximizing business metrics while experimenting4 Response surface Optimizing continuous parameters5 Contextual Making targeted decisions6 Bayesian Automating experimental optimization7 Managing business metrics8 Practical considerations
Detailed Information
| Author: | David Sweet |
|---|---|
| Publication Year: | 2023 |
| ISBN: | 9781617298158 |
| Language: | English |
| File Size: | 5.9 |
| Format: | |
| Price: | FREE |
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