Название: Bayesian Optimization in Action (MEAP v7)
Автор: Quan Nguyen
Издательство: Manning Publications
Год: 2023
Страниц: 380
Язык: английский
Формат: pdf, epub
Размер: 26.7 MB

Apply advanced techniques for optimizing Machine Learning processes. Bayesian optimization helps pinpoint the best configuration for your Machine Learning models with speed and accuracy.

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 practical use cases such as cost-constrained, multi-objective, and preference optimization
Use PyTorch, GPyTorch, and BoTorch to implement Bayesian optimization

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.

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 optimization 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 optimization can help you make better decisions, faster.

On a high level, Bayesian optimization is an optimization technique that may be applied when the function (or in general any process that gives you an output when an input is passed in) we are trying to optimize is a black box and expensive to evaluate in terms of time, money, or other resources. This setup encompasses many important tasks including hyperparameter tuning (which we will define shortly). Using Bayesian optimization could accelerate this search procedure and help us locate the optimum of the function as quickly as possible.

As a machine learning practitioner, you might have heard of the term Bayesian optimization from time to time, or you might never encounter it before. While Bayesian optimization has enjoyed enduring interest from the machine learning (ML) research community, it’s not as commonly used and talked about as other ML topics in practice. Why? Some might say Bayesian optimization has a steep learning curve: you need to understand calculus, use some probability, and overall be an experienced ML researcher to use Bayesian optimization in your application. Our goal for this book is to dispel the message that Bayesian optimization is difficult to use, and show that the technology is more intuitive and accessible than one would think.

Throughout this book, we will see a lot of illustrations, plots, and of course, code, which will help make whichever the topic currently being discussed more straightforward and concrete. You will learn how each component of Bayesian optimization works on a high level and how to implement them using state-of-the-art libraries in Python. Another hope of mine for the accompanying code is that it would help you hit the ground running with your own projects, as the Bayesian optimization framework is very general and "plug-and-play." The exercises are also helpful in this regard.

about the book
Bayesian Optimization in Action teaches you how to build Bayesian optimization systems from the ground up. This book transforms state-of-the-art research into usable techniques that you can easily put into practice, all fully illustrated with useful code samples. In it, you’ll hone your understanding of Bayesian optimization through engaging examples—from forecasting the weather, to finding the optimal amount of sugar for coffee, and even deciding if someone is psychic! Along the way, you’ll explore scenarios for when there are multiple objectives, when each decision has its own cost, and when feedback is in the form of pairwise comparisons. With this collection of techniques, you’ll be ready to find the optimal solution for everything from transport and logistics to cancer treatments.








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