Bayesian Optimization : Theory and Practice Using Python

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
Other Authors: Liu, Peng, 1951- author (author)
Format: eBook
Language:Inglés
Published: Berkeley, CA : Apress 2023.
Edition:1st ed. 2023.
Subjects:
Bayesian statistical decision theory > Data processing.
Python (Computer program language)
Mathematical optimization.
See on Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009729738306719
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.

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