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Add https://youtu.be/j0MOGKBqx7E to docs (#18222)

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docs/en/guides/hyperparameter-tuning.md
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@ -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.
<p align="center">
<br>
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/j0MOGKBqx7E"
title="YouTube video player" frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> How to Tune Hyperparameters for Better Model Performance 🚀
</p>
### 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: