Bayesian Optimization
DOWNLOAD
Download Bayesian Optimization PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Bayesian Optimization book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Bayesian Optimization
DOWNLOAD
Author : Roman Garnett
language : en
Publisher: Cambridge University Press
Release Date : 2023-02-09
Bayesian Optimization written by Roman Garnett and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-09 with Computers categories.
A comprehensive introduction to Bayesian optimization that starts from scratch and carefully develops all the key ideas along the way.
Bayesian Optimization In Action
DOWNLOAD
Author : Quan Nguyen
language : en
Publisher: Simon and Schuster
Release Date : 2024-01-09
Bayesian Optimization In Action written by Quan Nguyen and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-09 with Computers categories.
Bayesian optimization helps pinpoint the best configuration for your machine learning models with speed and accuracy. Put its advanced techniques into practice with this hands-on guide. In Bayesian Optimization in Action you will learn how to: Train Gaussian processes on both sparse and large data sets Combine Gaussian processes with deep neural networks to make them flexible and expressive Find the most successful strategies for hyperparameter tuning Navigate a search space and identify high-performing regions Apply Bayesian optimization to cost-constrained, multi-objective, and preference optimization Implement Bayesian optimization with PyTorch, GPyTorch, and BoTorch Bayesian Optimization in Action shows you how to optimize hyperparameter tuning, A/B testing, and other aspects of the machine learning process by applying cutting-edge Bayesian techniques. Using clear language, illustrations, and concrete examples, this book proves that Bayesian optimization doesn’t have to be difficult! You’ll get in-depth insights into how Bayesian optimization works and learn how to implement it with cutting-edge Python libraries. The book’s easy-to-reuse code samples let you hit the ground running by plugging them straight into your own projects. Forewords by Luis Serrano and David Sweet. About the technology In machine learning, optimization is about achieving the best predictions—shortest delivery routes, perfect price points, most accurate recommendations—in the fewest number of steps. Bayesian optimization uses the mathematics of probability to fine-tune ML functions, algorithms, and hyperparameters efficiently when traditional methods are too slow or expensive. About the book Bayesian Optimization in Action teaches you how to create efficient machine learning processes using a Bayesian approach. In it, you’ll explore practical techniques for training large datasets, hyperparameter tuning, and navigating complex search spaces. This interesting book includes engaging illustrations and fun examples like perfecting coffee sweetness, predicting weather, and even debunking psychic claims. You’ll learn how to navigate multi-objective scenarios, account for decision costs, and tackle pairwise comparisons. What's inside Gaussian processes for sparse and large datasets Strategies for hyperparameter tuning Identify high-performing regions Examples in PyTorch, GPyTorch, and BoTorch About the reader For machine learning practitioners who are confident in math and statistics. About the author Quan Nguyen is a research assistant at Washington University in St. Louis. He writes for the Python Software Foundation and has authored several books on Python programming. Table of Contents 1 Introduction to Bayesian optimization 2 Gaussian processes as distributions over functions 3 Customizing a Gaussian process with the mean and covariance functions 4 Refining the best result with improvement-based policies 5 Exploring the search space with bandit-style policies 6 Leveraging information theory with entropy-based policies 7 Maximizing throughput with batch optimization 8 Satisfying extra constraints with constrained optimization 9 Balancing utility and cost with multifidelity optimization 10 Learning from pairwise comparisons with preference optimization 11 Optimizing multiple objectives at the same time 12 Scaling Gaussian processes to large datasets 13 Combining Gaussian processes with neural networks
Bayesian Optimization And Data Science
DOWNLOAD
Author : Francesco Archetti
language : en
Publisher: Springer Nature
Release Date : 2019-09-25
Bayesian Optimization And Data Science written by Francesco Archetti and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-25 with Business & Economics categories.
This volume brings together the main results in the field of Bayesian Optimization (BO), focusing on the last ten years and showing how, on the basic framework, new methods have been specialized to solve emerging problems from machine learning, artificial intelligence, and system optimization. It also analyzes the software resources available for BO and a few selected application areas. Some areas for which new results are shown include constrained optimization, safe optimization, and applied mathematics, specifically BO's use in solving difficult nonlinear mixed integer problems. The book will help bring readers to a full understanding of the basic Bayesian Optimization framework and gain an appreciation of its potential for emerging application areas. It will be of particular interest to the data science, computer science, optimization, and engineering communities.
Bayesian Optimization With Application To Computer Experiments
DOWNLOAD
Author : Tony Pourmohamad
language : en
Publisher: Springer Nature
Release Date : 2021-10-04
Bayesian Optimization With Application To Computer Experiments written by Tony Pourmohamad and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-04 with Mathematics categories.
This book introduces readers to Bayesian optimization, highlighting advances in the field and showcasing its successful applications to computer experiments. R code is available as online supplementary material for most included examples, so that readers can better comprehend and reproduce methods. Compact and accessible, the volume is broken down into four chapters. Chapter 1 introduces the reader to the topic of computer experiments; it includes a variety of examples across many industries. Chapter 2 focuses on the task of surrogate model building and contains a mix of several different surrogate models that are used in the computer modeling and machine learning communities. Chapter 3 introduces the core concepts of Bayesian optimization and discusses unconstrained optimization. Chapter 4 moves on to constrained optimization, and showcases some of the most novel methods found in the field. This will be a useful companion to researchers and practitioners working with computer experiments and computer modeling. Additionally, readers with a background in machine learning but minimal background in computer experiments will find this book an interesting case study of the applicability of Bayesian optimization outside the realm of machine learning.
Bayesian Approach To Global Optimization
DOWNLOAD
Author : Jonas Mockus
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Bayesian Approach To Global Optimization written by Jonas Mockus and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
·Et moi ... si j'avait su comment en revcnir. One service mathematics has rendered the je o'y semis point alle.' human race. It has put common sense back Jules Verne where it beloogs. on the topmost shelf next to the dusty canister labelled 'discarded non The series is divergent; therefore we may be sense', able to do something with it. Eric T. BclI O. Heaviside Mathematics is a tool for thought. A highly necessary tool in a world where both feedback and non linearities abound. Similarly, all kinds of parts of mathematics serve as tools for other parts and for other sciences. Applying a simple rewriting rule to the quote on the right above one finds such statements as: 'One service topology has rendered mathematical physics ... '; 'One service logic has rendered com puter science .. .'; 'One service category theory has rendered mathematics .. .'. All arguably true. And all statements obtainable this way form part of the raison d'etre of this series.
Bayesian And High Dimensional Global Optimization
DOWNLOAD
Author : Anatoly Zhigljavsky
language : en
Publisher: Springer Nature
Release Date : 2021-03-02
Bayesian And High Dimensional Global Optimization written by Anatoly Zhigljavsky and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-02 with Mathematics categories.
Accessible to a variety of readers, this book is of interest to specialists, graduate students and researchers in mathematics, optimization, computer science, operations research, management science, engineering and other applied areas interested in solving optimization problems. Basic principles, potential and boundaries of applicability of stochastic global optimization techniques are examined in this book. A variety of issues that face specialists in global optimization are explored, such as multidimensional spaces which are frequently ignored by researchers. The importance of precise interpretation of the mathematical results in assessments of optimization methods is demonstrated through examples of convergence in probability of random search. Methodological issues concerning construction and applicability of stochastic global optimization methods are discussed, including the one-step optimal average improvement method based on a statistical model of the objective function. A significant portion of this book is devoted to an analysis of high-dimensional global optimization problems and the so-called ‘curse of dimensionality’. An examination of the three different classes of high-dimensional optimization problems, the geometry of high-dimensional balls and cubes, very slow convergence of global random search algorithms in large-dimensional problems , and poor uniformity of the uniformly distributed sequences of points are included in this book.
Bayesian Process Monitoring Control And Optimization
DOWNLOAD
Author : Bianca M. Colosimo
language : en
Publisher: CRC Press
Release Date : 2006-11-10
Bayesian Process Monitoring Control And Optimization written by Bianca M. Colosimo and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-11-10 with Business & Economics categories.
Although there are many Bayesian statistical books that focus on biostatistics and economics, there are few that address the problems faced by engineers. Bayesian Process Monitoring, Control and Optimization resolves this need, showing you how to oversee, adjust, and optimize industrial processes. Bridging the gap between application and dev
100 Optimization Techniques
DOWNLOAD
Author : Subrata Pandey
language : en
Publisher: SUBRATA PANDEY
Release Date : 2023-02-23
100 Optimization Techniques written by Subrata Pandey and has been published by SUBRATA PANDEY this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-23 with Technology & Engineering categories.
100 optimization techniques is intended as a handbook for optimization techniques. Optimization techniques and algorithms are methods used to find the most efficient solution to a problem. Different techniques and algorithms may be used to solve a particular problem, depending on the nature of the problem. Researchers from varieties of domains are using optimization algorithms to solve problems in their domain. Different optimization techniques have their pros and cons. This book serves as a handbook for researchers who wants to know about different optimization methods currently available and their operating principles. One hundred optimization techniques are arranged in an alphabetical order. Researchers and students who want to use different optimization techniques for solving their domain related problems will find this book helpful.
Learning And Intelligent Optimization
DOWNLOAD
Author : Paola Festa
language : en
Publisher: Springer Nature
Release Date : 2025-01-02
Learning And Intelligent Optimization written by Paola Festa and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-02 with Mathematics categories.
This book constitutes the refereed proceedings of the 18th International Conference on Learning and Intelligent Optimization, LION 18, held in Ischia Island, Italy, in June 2024. The 31 full papers and 4 short papers presented in these proceedings were carefully reviewed and selected from 58 submissions. These papers focus on the current research, challenges and applications in the fields of Artificial Intelligent, Machine Learning and Operations Research.
Optimization In Chemical Engineering
DOWNLOAD
Author : Fernando Israel Gómez-Castro
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2025-04-21
Optimization In Chemical Engineering written by Fernando Israel Gómez-Castro and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-21 with Technology & Engineering categories.
Optimization is an area in constant evolution. The search for robust optimization techniques to deal with the highly non-convex models that represent the systems related to Chemical Engineering has led to important advances in the area. The need for developing economically feasible processes which are simultaneously environmentally friendly, safe, and controllable requires for adequate optimization strategies. Moreover, finding a global optimum is still a challenge for a diversity of cases. Thus, this book presents a compilation of classic and emerging optimization techniques, focusing on their application to systems related to the Chemical Engineering. The book shows the applications of classic mathematical programming, metaheuristic optimization methods and machine learning-based strategies. The analysis of the described techniques allows the reader identifying the advantages and disadvantages of each approach. Moreover, the book will discuss the perspectives for future developments on the area.