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Bayesian Optimization in Action
Paperback
Series: In Action
ISBN13: 9781633439078
Publisher: Manning Publications
Published: Nov 14 2023
Pages: 424
Weight: 1.55
Height: 0.90 Width: 7.30 Depth: 9.10
Language: English
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
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
- 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
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
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