Bayesian Optimization in Action
- 7h 7m
- Quan Nguyen
- Manning Publications
- 2023
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.
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.
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.
In this Book
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Forewords
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About This Book
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Introduction to Bayesian Optimization
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Gaussian Processes as Distributions Over Functions
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Customizing a Gaussian Process with the Mean and Covariance Functions
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Refining the Best Result with Improvement-Based Policies
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Exploring the Search Space with Bandit-Style Policies
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Using Information Theory with Entropy-Based Policies
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Maximizing Throughput with Batch Optimization
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Satisfying Extra Constraints with Constrained Optimization
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Balancing Utility and Cost with Multifidelity Optimization
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Learning from Pairwise Comparisons with Preference Optimization
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Optimizing Multiple Objectives at the Same Time
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Scaling Gaussian Processes to Large Datasets
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Combining Gaussian Processes with Neural Networks
