Bayesian optimization : theory and practice using Python /

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 approach...

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Bibliographic Details
Main Author:	Liu, Peng (Author)
Format:	eBook
Language:	English
Published:	New York, NY : Apress, 2023.
Subjects:	Bayesian statistical decision theory > Data processing. Python (Computer program language) Mathematical optimization.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

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.