From 0fb6788b0f6c45994976f41af4b054dbe5385558 Mon Sep 17 00:00:00 2001 From: Muhammad Rizwan Munawar Date: Sun, 15 Dec 2024 22:42:10 +0500 Subject: [PATCH] Add https://youtu.be/j0MOGKBqx7E to docs (#18222) --- docs/en/guides/hyperparameter-tuning.md | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/docs/en/guides/hyperparameter-tuning.md b/docs/en/guides/hyperparameter-tuning.md index d20b610e..79908eae 100644 --- a/docs/en/guides/hyperparameter-tuning.md +++ b/docs/en/guides/hyperparameter-tuning.md @@ -10,6 +10,17 @@ keywords: Ultralytics YOLO, hyperparameter tuning, machine learning, model optim Hyperparameter tuning is not just a one-time set-up but an iterative process aimed at optimizing the [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) model's performance metrics, such as accuracy, precision, and recall. In the context of Ultralytics YOLO, these hyperparameters could range from learning rate to architectural details, such as the number of layers or types of activation functions used. +

+
+ +
+ Watch: How to Tune Hyperparameters for Better Model Performance 🚀 +

+ ### What are Hyperparameters? Hyperparameters are high-level, structural settings for the algorithm. They are set prior to the training phase and remain constant during it. Here are some commonly tuned hyperparameters in Ultralytics YOLO: