Term
Bayesian Optimization (ˌbeɪ.ziˈæn ˌɒp.tɪ.maɪˈzeɪ.ʃən)
Definition
Bayesian Optimization is a method in AI that uses probability models to find the best settings (hyperparameters) for algorithms. It helps improve the performance of AI systems without trying every possible configuration.
Where you'll find it
This method is commonly used in machine learning and deep learning frameworks where tuning hyperparameters like learning rate, batch size, or network depth is crucial. It's available in AI platforms that support advanced model training methods.
Common use cases
- Improving the accuracy of machine learning models by fine-tuning their settings.
- Reducing the time and computational resources needed to train models by focusing only on the most promising configurations.
- Automating the process of model optimization in complex AI systems.
Things to watch out for
- Bayesian Optimization can be computationally intensive, especially with complex models and large data sets.
- Setting up this method can be quite technical, which might be challenging for beginners.
- Choosing incorrect prior distributions or acquisition functions can lead to suboptimal hyperparameter tuning.
Related terms
- Hyperparameters
- Machine Learning
- Deep Learning
- Probability Model
- Model Training