Items related to Bayesian Optimization in Action
Synopsis
Apply advanced techniques for optimising machine learning processes
For machine learning practitioners confident in maths and statistics.
Bayesian Optimization in Action shows you how to optimise hyperparameter tuning, A/B testing, and other aspects of the machine learning process, by applying cutting-edge Bayesian techniques. Using clear language, Bayesian Optimization helps pinpoint the best configuration for your machine-learning models with speed and accuracy. With a range of illustrations, and concrete examples, this book proves that Bayesian Optimisation doesn't have to be difficult!
Key features include:
- Train Gaussian processes on both sparse and large data sets
- Combine Gaussian processes with deep neural networks to make them flexible and expressive
- Find the most successful strategies for hyperparameter tuning
- Navigate a search space and identify high-performing regions
- Apply Bayesian Optimisation to practical use cases such as cost-constrained, multi-objective, and preference optimisation
- Use PyTorch, GPyTorch, and BoTorch to implement Bayesian optimisation
You will get in-depth insights into how Bayesian optimisation works and learn how to implement it with cutting-edge Python libraries. The book's easy-to-reuse code samples will let you hit the ground running by plugging them straight into your own projects!
About the technology
Experimenting in science and engineering can be costly and time-consuming, especially without a reliable way to narrow down your choices. Bayesian Optimisation helps you identify optimal configurations to pursue in a search space. It uses a Gaussian process and machine learning techniques to model an objective function and quantify the uncertainty of predictions. Whether you're tuning machine learning models, recommending products to customers, or engaging in research, Bayesian Optimisation can help you make better decisions faster.
"synopsis" may belong to another edition of this title.
About the Author
Quan Nguyen is a Python programmer and machine learning enthusiast. He is interested in solving decision-making problems that involve uncertainty. Quan has authored several books on Python programming and scientific computing. He is currently pursuing a PhD degree in Computer Science at Washington University in St. Louis, where he conducts research on Bayesian methods in machine learning.
From the Back Cover
Bayesian Optimization in Action teaches you how to build Bayesian Optimisation systems from the ground up. This book transforms state-of-the-art research into usable techniques that you can easily put into practice ― all fully illustrated with useful code samples.
You will hone your understanding of Bayesian Optimisation through engaging examples ― from forecasting the weather to finding the optimal amount of sugar for coffee and even deciding if someone is psychic! Along the way, you will explore scenarios with multiple objectives, when each decision has its own cost, and when feedback is in the form of pairwise comparisons. With this collection of techniques, you will be ready to find the optimal solution for everything ― from transport and logistics to cancer treatments.
About the reader
For machine learning practitioners who are confident in math and statistics.
"About this title" may belong to another edition of this title.
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Bayesian Optimization in Action
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Hardback. Condition: New. Apply advanced techniques for optimising machine learning processes For machine learning practitioners confident in maths and statistics. Bayesian Optimization in Action shows you how to optimise hyperparameter tuning, A/B testing, and other aspects of the machine learning process, by applying cutting-edge Bayesian techniques. Using clear language, Bayesian Optimization helps pinpoint the best configuration for your machine-learning models with speed and accuracy. With a range of illustrations, and concrete examples, this book proves that Bayesian Optimisation doesn't have to be difficult! Key features include: Train Gaussian processes on both sparse and large data setsCombine Gaussian processes with deep neural networks to make them flexible and expressiveFind the most successful strategies for hyperparameter tuningNavigate a search space and identify high-performing regionsApply Bayesian Optimisation to practical use cases such as cost-constrained, multi-objective, and preference optimisationUse PyTorch, GPyTorch, and BoTorch to implement Bayesian optimisation You will get in-depth insights into how Bayesian optimisation works and learn how to implement it with cutting-edge Python libraries. The book's easy-to-reuse code samples will let you hit the ground running by plugging them straight into your own projects! About the technology Experimenting in science and engineering can be costly and time-consuming, especially without a reliable way to narrow down your choices. Bayesian Optimisation helps you identify optimal configurations to pursue in a search space. It uses a Gaussian process and machine learning techniques to model an objective function and quantify the uncertainty of predictions. Whether you're tuning machine learning models, recommending products to customers, or engaging in research, Bayesian Optimisation can help you make better decisions faster. Seller Inventory # LU-9781633439078
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