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Experimentation for Engineers: From A/B testing to Bayesian optimization delivers a toolbox of processes for optimising software systems. From learning the limits of A/B testing to advanced experimentation strategies involving machine learning and probabilistic methods, this practical guide will help you master the skills. It will also help you minimise the costs of experimentation and will quickly reveal which approaches and features deliver the best business results.
What's inside- 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
For ML and software engineers looking to extract the most value from their systems. Examples are found in Python and NumPy.
Experimentation for Engineers: From A/B testing to Bayesian optimization delivers a toolbox of processes for optimising software systems. From learning the limits of A/B testing to advanced experimentation strategies involving machine learning and probabilistic methods, this practical guide will help you master the skills. It will also help you minimise the costs of experimentation and will quickly reveal which approaches and features deliver the best business results.
What's inside- 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
For ML and software engineers looking to extract the most value from their systems. Examples are found in Python and NumPy.
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.
| Erscheinungsjahr: | 2023 |
|---|---|
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Seiten: | 248 |
| Inhalt: | Gebunden |
| ISBN-13: | 9781617298158 |
| ISBN-10: | 1617298158 |
| Sprache: | Englisch |
| Einband: | Gebunden |
| Autor: | Sweet, David |
| Hersteller: | Manning Publications |
| Maße: | 232 x 185 x 15 mm |
| Von/Mit: | David Sweet |
| Erscheinungsdatum: | 20.02.2023 |
| Gewicht: | 0,464 kg |
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.
| Erscheinungsjahr: | 2023 |
|---|---|
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Seiten: | 248 |
| Inhalt: | Gebunden |
| ISBN-13: | 9781617298158 |
| ISBN-10: | 1617298158 |
| Sprache: | Englisch |
| Einband: | Gebunden |
| Autor: | Sweet, David |
| Hersteller: | Manning Publications |
| Maße: | 232 x 185 x 15 mm |
| Von/Mit: | David Sweet |
| Erscheinungsdatum: | 20.02.2023 |
| Gewicht: | 0,464 kg |
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