{"version":"1.0","type":"rich","provider_name":"gaks.ai AI Glossary","provider_url":"https://gaks.ai/glossary","title":"Bayesian Optimization — AI Glossary","author_name":"Glenn Katrud Solheim","author_url":"https://gaks.ai","width":600,"height":200,"html":"<div style=\"font-family:sans-serif;border:1px solid #e0e0e0;border-radius:8px;padding:16px;max-width:600px;background:#ffffff;color:#111111;\"><p style=\"margin:0 0 4px;font-size:11px;color:#666;\">AI Glossary — gaks.ai</p><h3 style=\"margin:0 0 8px;font-size:16px;\">Bayesian Optimization</h3><p style=\"margin:0 0 12px;font-size:14px;line-height:1.6;\">A strategy for efficiently finding the optimal settings for an expensive function, such as a model's hyperparameters, by building a probabilistic model of that function and using it to decide where to evaluate next. It is much more efficient than exhaustive search and is widely used in automated machine learning and neural architecture search.  See also: hyperparameter, bayesian network.</p><a href=\"https://gaks.ai/glossary/bayesian-optimization\" style=\"font-size:12px;color:#0077aa;\">Source: gaks.ai/glossary/bayesian-optimization →</a></div>"}