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Название: Experimentation for Engineers: From A/B testing to Bayesian optimization (Final Release)
Автор: David Sweet
Издательство: Manning Publications
Год: 2023
Страниц: 250
Язык: английский
Формат: pdf (true)
Размер: 10.2 MB
Experimentation for Engineers: 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. This book is for Machine Learning engineers, quantitative traders, and software engineers looking to measure and improve the performance of whatever they’re building. Performance of the systems they build may be gauged by user behavior, revenue, speed, or similar metrics. You might already be working with an experimentation system at a tech or finance company and want to understand it more deeply. You might be planning or aspiring to work with or build such a system. Students entering industry might find that this book is an ideal introduction to industry practices. A reader should be comfortable with Python, NumPy, and undergraduate math (including basic linear algebra).
Автор: David Sweet
Издательство: Manning Publications
Год: 2023
Страниц: 250
Язык: английский
Формат: pdf (true)
Размер: 10.2 MB
Experimentation for Engineers: 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. This book is for Machine Learning engineers, quantitative traders, and software engineers looking to measure and improve the performance of whatever they’re building. Performance of the systems they build may be gauged by user behavior, revenue, speed, or similar metrics. You might already be working with an experimentation system at a tech or finance company and want to understand it more deeply. You might be planning or aspiring to work with or build such a system. Students entering industry might find that this book is an ideal introduction to industry practices. A reader should be comfortable with Python, NumPy, and undergraduate math (including basic linear algebra).