{"version":"1.0","type":"rich","provider_name":"gaks.ai AI Glossary","provider_url":"https://gaks.ai/glossary","title":"Learning Rate — 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;\">Learning Rate</h3><p style=\"margin:0 0 12px;font-size:14px;line-height:1.6;\">A hyperparameter that controls how large a step the model takes when updating its weights during training. Too high and the model overshoots and fails to converge; too low and training is slow or gets stuck. Setting the right learning rate is one of the most consequential decisions in model training, and most modern training pipelines use schedulers that adjust it dynamically over time.</p><a href=\"https://gaks.ai/glossary/learning-rate\" style=\"font-size:12px;color:#0077aa;\">Source: gaks.ai/glossary/learning-rate →</a></div>"}