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Inhaltsangabe
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 technologyDoes 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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Über die Autorin bzw. den Autor
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.
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Learn how to evaluate the changes you make to your system and ensure that your testing does not undermine revenue or other business metrics.
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.
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Hardback. Zustand: New. 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 testBreak the "feedback loops" caused by periodic retraining of ML modelsIncrease experimentation rate with multi-armed banditsTune multiple parameters experimentally with Bayesian optimisationClearly define business metrics used for decision-makingIdentify 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. Bestandsnummer des Verkäufers LU-9781617298158
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Tuning Up: From A/B Testing to Bayesian Optimization
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Zustand: New. Über den AutorDavid 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. Bestandsnummer des Verkäufers 497649442
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Experimentation for Engineers : From A/B Testing to Bayesian Optimization
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Experimentation for Engineers : From A/B Testing to Bayesian Optimization
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Experimentation for Engineers
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Hardback. Zustand: New. 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 testBreak the "feedback loops" caused by periodic retraining of ML modelsIncrease experimentation rate with multi-armed banditsTune multiple parameters experimentally with Bayesian optimisationClearly define business metrics used for decision-makingIdentify 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. Bestandsnummer des Verkäufers LU-9781617298158
Anzahl: 10 verfügbar