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Bayesian Optimization in Action
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
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 .
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
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
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
PART 1 MODELING WITH GAUSSIAN PROCESSES
2 Gaussian processes as distributions over functions
3 Customizing a Gaussian process with the mean and covariance functions
PART 2 MAKING DECISIONS WITH BAYESIAN OPTIMIZATION
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
PART 3 EXTENDING BAYESIAN OPTIMIZATION TO SPECIALIZED SETTINGS
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
PART 4 SPECIAL GAUSSIAN PROCESS MODELS
12 Scaling Gaussian processes to large datasets
13 Combining Gaussian processes with neural networks
In Bayesian Optimization in Action you will learn how
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 .
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
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
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
PART 1 MODELING WITH GAUSSIAN PROCESSES
2 Gaussian processes as distributions over functions
3 Customizing a Gaussian process with the mean and covariance functions
PART 2 MAKING DECISIONS WITH BAYESIAN OPTIMIZATION
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
PART 3 EXTENDING BAYESIAN OPTIMIZATION TO SPECIALIZED SETTINGS
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
PART 4 SPECIAL GAUSSIAN PROCESS MODELS
12 Scaling Gaussian processes to large datasets
13 Combining Gaussian processes with neural networks
424 pages, Paperback
Published November 14, 2023
3 people are currently reading
26 people want to read
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Displaying 1 - 2 of 2 reviews
December 8, 2023
This is a great resource to learn about Bayesian Optimization.
The book features a lot of images and code that help the learning process.
The writing keeps a conversational tone. The mathematical is kept to a minimum but it doesn't lose depth.
I liked learning about different acquisition functions and also different heuristics, such as Bayesian Optimization policies, multi-armed bandit policies and entropy-based policies.
This book is suitable to data scientists and ML practitioners, particular those who have an interest in hyperparameter tuning, A/B testing, decision-making and optimization. It is also suited to researchers who face similar optimization problems in their domain.
The book features a lot of images and code that help the learning process.
The writing keeps a conversational tone. The mathematical is kept to a minimum but it doesn't lose depth.
I liked learning about different acquisition functions and also different heuristics, such as Bayesian Optimization policies, multi-armed bandit policies and entropy-based policies.
This book is suitable to data scientists and ML practitioners, particular those who have an interest in hyperparameter tuning, A/B testing, decision-making and optimization. It is also suited to researchers who face similar optimization problems in their domain.
November 14, 2023
If you haven't explored BO yet, you should give it a read. This book starts with the basics, swiftly progressing to real-world examples that simplify complex concepts. It delves deep into the theory and practical application of Bayesian Optimization, making it accessible to both beginners and seasoned practitioners.
Displaying 1 - 2 of 2 reviews