Bayesian Optimization: Theory and Practice Using Python 1st Edition

Bayesian Optimization: Theory and Practice Using Python 1st Edition – Ebook Instant Download/Delivery ISBN(s): 9781484290620,1484290623,9781484290637, 1484290631

Product details:

• ISBN-10: 1484290631
• ISBN-13: 9781484290637
• Author: Peng Liu

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.

Table contents:

1. Bayesian Optimization Overview
2. Gaussian Processes
3. Bayesian Decision Theory and Expected Improvement
4. Gaussian Process Regression with GPyTorch
5. Monte Carlo Acquisition Function with Sobol Sequences and Random Restart
6. Knowledge Gradient: Nested Optimization vs. One-Shot Learning
7. Case Study: Tuning CNN Learning Rate with BoTorch

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