Bayesian Optimization in Action – Manning Publications (2023)
English | eBook | Size: 24.95 MB
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.
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.
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