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. The technical editor of this book is Kerry Koitzsch.
Bayesian Optimization in Action
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- Nombre de pages396
- FormatGrand Format
- PrésentationBroché
- Poids0.794 kg
- Dimensions18,4 cm × 23,2 cm × 2,3 cm
- ISBN978-1-63343-907-8
- EAN9781633439078
- Date de parution14/11/2023
- ÉditeurManning
- PréfacierLuis Serrano
- PréfacierDavid Sweet
Résumé
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. 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. For machine learning practitioners who are confident in math and statistics.
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. For machine learning practitioners who are confident in math and statistics.