Bayesian optimization : theory and practice using Python / Peng Liu.
Liu, Peng, author.
2023
QA279.5
Available Online

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Title Bayesian optimization : theory and practice using Python / Peng Liu.
Author Liu, Peng, author.
ISBN 9781484290637 (electronic bk.)
1484290631 (electronic bk.)
1484290623
9781484290620
DOI https://doi.org/10.1007/978-1-4842-9063-7
Imprint New York, NY : Apress, 2023.
Language English
Description 1 online resource (xv, 234 pages) : illustrations (black and white, and colour).
Other Standard Identifiers 10.1007/978-1-4842-9063-7 doi
Call Number QA279.5
Dewey Decimal Classification 519.5/42
Summary This book covers the essential theory and implementation of popular Bayesian optimization techniques in an intuitive and well-illustrated manner. The techniques covered in this book will enable you to better tune the hyperparemeters of your machine learning models and learn sample-efficient approaches to global optimization.
Note Includes index.
Access Note Access limited to authorized users.
Source of Description Description based on print version record.
Available in Other Form Print version: 9781484290620
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
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Chapter 1: Bayesian Optimization Overview
Chapter 2: Gaussian Process
Chapter 3: Bayesian Decision Theory and Expected Improvement
Chapter 4 : Gaussian Process Regression with GPyTorch
Chapter 5: Monte Carlo Acquisition Function with Sobol Sequences and Random Restart
Chapter 6 : Knowledge Gradient: Nested Optimization versus One-shot Learning
Chapter 7 : Case Study: Tuning CNN Learning Rate with BoTorch.

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