Experimentation for Engineers
Seiten
2023
Manning Publications (Verlag)
978-1-61729-815-8 (ISBN)
Manning Publications (Verlag)
978-1-61729-815-8 (ISBN)
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.
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.
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.
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 programmes at Yeshiva University.
| Erscheinungsdatum | 17.02.2023 |
|---|---|
| Verlagsort | New York |
| Sprache | englisch |
| Maße | 190 x 234 mm |
| Gewicht | 456 g |
| Themenwelt | Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge |
| Informatik ► Software Entwicklung ► Qualität / Testen | |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| ISBN-10 | 1-61729-815-8 / 1617298158 |
| ISBN-13 | 978-1-61729-815-8 / 9781617298158 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
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