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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.
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.