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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
Format: eBook
Language:English
Published: New York, NY Apress 2023
Subjects:
Optimisation Mathématique
Bayesian Statistical Decision Theory / Data Processing
Mathematical Optimization / Http://id.loc.gov/authorities/subjects/sh85082127
Python (computer Program Language) / Fast
Python (computer Program Language) / Http://id.loc.gov/authorities/subjects/sh96008834
Mathematical Optimization / Fast
Théorie De La Décision Bayésienne / Informatique
Python (langage De Programmation)
Bayesian Statistical Decision Theory / Data Processing / Fast
Online Access:
Collection: O'Reilly - Collection details see MPG.ReNa
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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