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
Call Number:Libro Electrónico
Main Author: Liu, Peng (Author)
Format: Electronic eBook
Language:Inglés
Published: New York, NY : Apress, 2023.
Subjects:
Bayesian statistical decision theory > Data processing.
Python (Computer program language)
Mathematical optimization.
Théorie de la décision bayésienne > Informatique.
Python (Langage de programmation)
Optimisation mathématique.
Bayesian statistical decision theory > Data processing
Mathematical optimization
Online Access:Texto completo (Requiere registro previo con correo institucional)
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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