Bayesian Optimization in Action
Seiten
2023
Manning Publications (Verlag)
978-1-63343-907-8 (ISBN)
Manning Publications (Verlag)
978-1-63343-907-8 (ISBN)
Bayesian Optimization in Action teaches you how to build Bayesian Optimisation systems from the ground up. This book transforms state-of-the-art research into usable techniques you can easily put into practice. With a range of illustrations, and concrete examples, this book proves that Bayesian Optimisation doesn't have to be difficult!
Apply advanced techniques for optimising machine learning processes For machine learning practitioners confident in maths and statistics.
Bayesian Optimization in Action shows you how to optimise hyperparameter tuning, A/B testing, and other aspects of the machine learning process, by applying cutting-edge Bayesian techniques. Using clear language, Bayesian Optimization helps pinpoint the best configuration for your machine-learning models with speed and accuracy. With a range of illustrations, and concrete examples, this book proves that Bayesian Optimisation doesn't have to be difficult!
Key features include:
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 Optimisation to practical use cases such as cost-constrained, multi-objective, and preference optimisation
Use PyTorch, GPyTorch, and BoTorch to implement Bayesian optimisation
You will get in-depth insights into how Bayesian optimisation works and learn how to implement it with cutting-edge Python libraries. The book's easy-to-reuse code samples will let you hit the ground running by plugging them straight into your own projects!
About the technology
Experimenting in science and engineering can be costly and time-consuming, especially without a reliable way to narrow down your choices. Bayesian Optimisation helps you identify optimal configurations to pursue in a search space. It uses a Gaussian process and machine learning techniques to model an objective function and quantify the uncertainty of predictions. Whether you're tuning machine learning models, recommending products to customers, or engaging in research, Bayesian Optimisation can help you make better decisions faster.
Apply advanced techniques for optimising machine learning processes For machine learning practitioners confident in maths and statistics.
Bayesian Optimization in Action shows you how to optimise hyperparameter tuning, A/B testing, and other aspects of the machine learning process, by applying cutting-edge Bayesian techniques. Using clear language, Bayesian Optimization helps pinpoint the best configuration for your machine-learning models with speed and accuracy. With a range of illustrations, and concrete examples, this book proves that Bayesian Optimisation doesn't have to be difficult!
Key features include:
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 Optimisation to practical use cases such as cost-constrained, multi-objective, and preference optimisation
Use PyTorch, GPyTorch, and BoTorch to implement Bayesian optimisation
You will get in-depth insights into how Bayesian optimisation works and learn how to implement it with cutting-edge Python libraries. The book's easy-to-reuse code samples will let you hit the ground running by plugging them straight into your own projects!
About the technology
Experimenting in science and engineering can be costly and time-consuming, especially without a reliable way to narrow down your choices. Bayesian Optimisation helps you identify optimal configurations to pursue in a search space. It uses a Gaussian process and machine learning techniques to model an objective function and quantify the uncertainty of predictions. Whether you're tuning machine learning models, recommending products to customers, or engaging in research, Bayesian Optimisation can help you make better decisions faster.
Quan Nguyen is a Python programmer and machine learning enthusiast. He is interested in solving decision-making problems that involve uncertainty. Quan has authored several books on Python programming and scientific computing. He is currently pursuing a PhD degree in Computer Science at Washington University in St. Louis, where he conducts research on Bayesian methods in machine learning.
Erscheinungsdatum | 01.08.2023 |
---|---|
Verlagsort | New York |
Sprache | englisch |
Maße | 185 x 235 mm |
Gewicht | 774 g |
Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
Mathematik / Informatik ► Mathematik ► Angewandte Mathematik | |
Mathematik / Informatik ► Mathematik ► Finanz- / Wirtschaftsmathematik | |
ISBN-10 | 1-63343-907-0 / 1633439070 |
ISBN-13 | 978-1-63343-907-8 / 9781633439078 |
Zustand | Neuware |
Haben Sie eine Frage zum Produkt? |
Mehr entdecken
aus dem Bereich
aus dem Bereich
Buch | Softcover (2024)
REDLINE (Verlag)
20,00 €
was sie kann & was uns erwartet
Buch | Softcover (2023)
C.H.Beck (Verlag)
18,00 €