Experimentation for Engineers: From A/B Testing to Bayesian Optimization by Davi
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Specifiche dell'oggetto
- Condizione
- ISBN-13
- 9781617298158
- Book Title
- Experimentation for Engineers
- ISBN
- 9781617298158
Informazioni su questo prodotto
Product Identifiers
Publisher
Manning Publications Co. LLC
ISBN-10
1617298158
ISBN-13
9781617298158
eBay Product ID (ePID)
23050418303
Product Key Features
Number of Pages
248 Pages
Publication Name
Experimentation for Engineers
Language
English
Publication Year
2023
Subject
Software Development & Engineering / Quality Assurance & Testing, Data Processing
Type
Textbook
Subject Area
Computers
Format
Hardcover
Dimensions
Item Height
0.6 in
Item Weight
16.1 Oz
Item Length
9.2 in
Item Width
7.5 in
Additional Product Features
Reviews
"Putting an 'improved' version of a system into production can be really risky. This book focuses you on what is important!" Simone Sguazza, University of Applied Sciences and Arts of Southern Switzerland "A must-have for anyone setting up experiments, from A/B tests to contextual bandits and Bayesian optimization." Maxim Volgin, KLM "Shows a non-mathematical programmer exactly what they need to write powerful mathematically-based testing algorithms." Patrick Goetz, The University of Texas at Austin "Gives you the tools you need to get the most out of your experiments." Marc-Anthony Taylor, Raiffeisen Bank International
Synopsis
Experimentation for Engineers: From A/B testing to Bayesian optimization is a toolbox to help you take a deep dive into methods like A/B testing. With sophisticated experimentation practices developed in the world's most competitive industries, the book will help you enhance machine learning systems, software applications, and quantitative trading solutions., Optimize the performance of your systems with practical experiments used by engineers in the world's most competitive industries. 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" caused 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 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 technology Does 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 book 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. What's inside Design, run, and analyze an A/B test Break the "feedback loops" caused by periodic retraining of ML models Increase experimentation rate with multi-armed bandits Tune multiple parameters experimentally with Bayesian optimization About the reader For ML and software engineers looking to extract the most value from their systems. Examples in Python and NumPy. About the author David 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 Contents 1 Optimizing systems by experiment 2 A/B testing: Evaluating a modification to your system 3 Multi-armed bandits: Maximizing business metrics while experimenting 4 Response surface methodology: Optimizing continuous parameters 5 Contextual bandits: Making targeted decisions 6 Bayesian optimization: Automating experimental optimization 7 Managing business metrics 8 Practical considerations, Optimise the performance of your systems with practical experiments used by engineers in the world's most competitive industries. Experimentation for Engineers: From A/B testing to Bayesian optimization is a toolbox of techniques for evaluating new features and fine-tuning parameters. You will 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. You will learn how to: Design, run, and analyse an A/B test Break the "feedback loops" caused by periodic retraining of ML models Increase experimentation rate with multi-armed bandits Tune multiple parameters experimentally with Bayesian optimisation Clearly define business metrics used for decision-making Identify and avoid the common pitfalls of experimentation By the time you're done, you will be able to seamlessly deploy experiments in production, whilst avoiding common pitfalls. About the technology Does 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 and will help you enhance machine learning systems, software applications, and quantitative trading solutions.
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98,3% di Feedback positivi•2,8 milioni oggetti venduti
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Feedback sul venditore (1.057.607)
- c***a (121)- Feedback lasciato dall'acquirente.Mese scorsoAcquisto verificatoThe seller was very responsive and answered me on a timely matter. The product itself came in its packaging and was new, not used at all. The packaging was not beat up or anything, safely delivered to my mailbox. No mix ups and zero stress with delivery. The price for the product is completely understandable for the product. I really appreciate the seller and I am very happy to have purchased through this seller. Completely trustable!
- m***4 (1608)- Feedback lasciato dall'acquirente.Mese scorsoAcquisto verificatoLeaving positive feedback because 1) item was packed well & arrived as described 2) seller did give partial refund when subsequent price dropped below org purchase price. 3) communication was quick However, there was a downside to this transaction -item listed as in-stock but ended up waiting nearly a month for them to get it from their distributer then ship it to me (bought June 29th, arrived around July 21). Auction said 12-15 days. Better clarity would have prevented lot of frustration
- w***i (876)- Feedback lasciato dall'acquirente.Ultimi 6 mesiAcquisto verificatoAbsolutely Wonderful Seller!! Terrific Item As Described!!! Great Service and Communication!! Shipped In Waterproof Packaging!! I Received Item In About One Week!! Very Pleased With Seller! I Will Buy From This Seller Again!!