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Experimentation for engineers : from A/B testing to Bayesian optimization /
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Shelter Island, NY :
Manning Publications Co.,
[2023]
|
| Edición: | [First edition]. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 019 | |a 1382798482 | ||
| 020 | |a 9781617298158 |q (electronic bk.) | ||
| 020 | |a 1617298158 |q (electronic bk.) | ||
| 020 | |a 9781638356905 |q (electronic bk.) | ||
| 020 | |a 1638356904 |q (electronic bk.) | ||
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| 049 | |a UAMI | ||
| 100 | 1 | |a Sweet, David, |e author. | |
| 245 | 1 | 0 | |a Experimentation for engineers : |b from A/B testing to Bayesian optimization / |c David Sweet. |
| 250 | |a [First edition]. | ||
| 264 | 1 | |a Shelter Island, NY : |b Manning Publications Co., |c [2023] | |
| 300 | |a 1 online resource (248 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 500 | |a Includes index. | ||
| 520 | |a 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. | ||
| 505 | 0 | |a Intro -- inside front cover -- Experimentation for Engineers -- Copyright -- dedication -- contents -- front matter -- preface -- acknowledgments -- about this book -- Who should read this book -- How this book is organized: A road map -- About the code -- liveBook discussion forum -- about the author -- about the cover illustration -- 1 Optimizing systems by experiment -- 1.1 Examples of engineering workflows -- 1.1.1 Machine learning engineer's workflow -- 1.1.2 Quantitative trader's workflow -- 1.1.3 Software engineer's workflow -- 1.2 Measuring by experiment -- 1.2.1 Experimental methods | |
| 505 | 8 | |a 1.2.2 Practical problems and pitfalls -- 1.3 Why are experiments necessary? -- 1.3.1 Domain knowledge -- 1.3.2 Offline model quality -- 1.3.3 Simulation -- Summary -- 2 A/B testing: Evaluating a modification to your system -- 2.1 Take an ad hoc measurement -- 2.1.1 Simulate the trading system -- 2.1.2 Compare execution costs -- 2.2 Take a precise measurement -- 2.2.1 Mitigate measurement variation with replication -- 2.3 Run an A/B test -- 2.3.1 Analyze your measurements -- 2.3.2 Design the A/B test -- 2.3.3 Measure and analyze -- 2.3.4 Recap of A/B test stages -- Summary | |
| 505 | 8 | |a 3 Multi-armed bandits: Maximizing business metrics while experimenting -- 3.1 Epsilon-greedy: Account for the impact of evaluation on business metrics -- 3.1.1 A/B testing as a baseline -- 3.1.2 The epsilon-greedy algorithm -- 3.1.3 Deciding when to stop -- 3.2 Evaluating multiple system changes simultaneously -- 3.3 Thompson sampling: A more efficient MAB algorithm -- 3.3.1 Estimate the probability that an arm is the best -- 3.3.2 Randomized probability matching -- 3.3.3 The complete algorithm -- Summary -- 4 Response surface methodology: Optimizing continuous parameters | |
| 505 | 8 | |a 4.1 Optimize a single continuous parameter -- 4.1.1 Design: Choose parameter values to measure -- 4.1.2 Take the measurements -- 4.1.3 Analyze I: Interpolate between measurements -- 4.1.4 Analyze II: Optimize the business metric -- 4.1.5 Validate the optimal parameter value -- 4.2 Optimizing two or more continuous parameters -- 4.2.1 Design the two-parameter experiment -- 4.2.2 Measure, analyze, and validate the 2D experiment -- Summary -- 5 Contextual bandits: Making targeted decisions -- 5.1 Model a business metric offline to make decisions online | |
| 505 | 8 | |a 5.1.1 Model the business-metric outcome of a decision -- 5.1.2 Add the decision-making component -- 5.1.3 Run and evaluate the greedy recommender -- 5.2 Explore actions with epsilon-greedy -- 5.2.1 Missing counterfactuals degrade predictions -- 5.2.2 Explore with epsilon-greedy to collect counterfactuals -- 5.3 Explore parameters with Thompson sampling -- 5.3.1 Create an ensemble of prediction models -- 5.3.2 Randomized probability matching -- 5.4 Validate the contextual bandit -- Summary -- 6 Bayesian optimization: Automating experimental optimization | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Computer engineering |x Experiments. | |
| 650 | 0 | |a Computer engineering |v Handbooks, manuals, etc. | |
| 650 | 6 | |a Ordinateurs |x Conception et construction |x Expériences. | |
| 650 | 6 | |a Ordinateurs |x Conception et construction |v Guides, manuels, etc. | |
| 650 | 7 | |a Computer engineering |2 fast | |
| 655 | 0 | |a Electronic books. | |
| 655 | 7 | |a handbooks. |2 aat | |
| 655 | 7 | |a Handbooks and manuals |2 fast | |
| 655 | 7 | |a Handbooks and manuals. |2 lcgft | |
| 655 | 7 | |a Guides et manuels. |2 rvmgf | |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781617298158/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 938 | |a ProQuest Ebook Central |b EBLB |n EBL7206060 | ||
| 938 | |a EBSCOhost |b EBSC |n 3536589 | ||
| 994 | |a 92 |b IZTAP | ||