Fix mkdocs.yml raw image URLs (#14213)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>

docs/en/guides/hyperparameter-tuning.md

@@ -205,3 +205,57 @@ The hyperparameter tuning process in Ultralytics YOLO is simplified yet powerful
 3. [Efficient Hyperparameter Tuning with Ray Tune and YOLOv8](../integrations/ray-tune.md)
 
 For deeper insights, you can explore the `Tuner` class source code and accompanying documentation. Should you have any questions, feature requests, or need further assistance, feel free to reach out to us on [GitHub](https://github.com/ultralytics/ultralytics/issues/new/choose) or [Discord](https://ultralytics.com/discord).
+
+## FAQ
+
+### How do I optimize the learning rate for Ultralytics YOLO during hyperparameter tuning?
+
+To optimize the learning rate for Ultralytics YOLO, start by setting an initial learning rate using the `lr0` parameter. Common values range from `0.001` to `0.01`. During the hyperparameter tuning process, this value will be mutated to find the optimal setting. You can utilize the `model.tune()` method to automate this process. For example:
+
+!!! Example
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Initialize the YOLO model
+        model = YOLO("yolov8n.pt")
+
+        # Tune hyperparameters on COCO8 for 30 epochs
+        model.tune(data="coco8.yaml", epochs=30, iterations=300, optimizer="AdamW", plots=False, save=False, val=False)
+        ```
+
+For more details, check the [Ultralytics YOLO configuration page](../usage/cfg.md#augmentation-settings).
+
+### What are the benefits of using genetic algorithms for hyperparameter tuning in YOLOv8?
+
+Genetic algorithms in Ultralytics YOLOv8 provide a robust method for exploring the hyperparameter space, leading to highly optimized model performance. Key benefits include:
+
+- **Efficient Search**: Genetic algorithms like mutation can quickly explore a large set of hyperparameters.
+- **Avoiding Local Minima**: By introducing randomness, they help in avoiding local minima, ensuring better global optimization.
+- **Performance Metrics**: They adapt based on performance metrics such as AP50 and F1-score.
+
+To see how genetic algorithms can optimize hyperparameters, check out the [hyperparameter evolution guide](../yolov5/tutorials/hyperparameter_evolution.md).
+
+### How long does the hyperparameter tuning process take for Ultralytics YOLO?
+
+The time required for hyperparameter tuning with Ultralytics YOLO largely depends on several factors such as the size of the dataset, the complexity of the model architecture, the number of iterations, and the computational resources available. For instance, tuning YOLOv8n on a dataset like COCO8 for 30 epochs might take several hours to days, depending on the hardware.
+
+To effectively manage tuning time, define a clear tuning budget beforehand ([internal section link](#preparing-for-hyperparameter-tuning)). This helps in balancing resource allocation and optimization goals.
+
+### What metrics should I use to evaluate model performance during hyperparameter tuning in YOLO?
+
+When evaluating model performance during hyperparameter tuning in YOLO, you can use several key metrics:
+
+- **AP50**: The average precision at IoU threshold of 0.50.
+- **F1-Score**: The harmonic mean of precision and recall.
+- **Precision and Recall**: Individual metrics indicating the model's accuracy in identifying true positives versus false positives and false negatives.
+
+These metrics help you understand different aspects of your model's performance. Refer to the [Ultralytics YOLO performance metrics](../guides/yolo-performance-metrics.md) guide for a comprehensive overview.
+
+### Can I use Ultralytics HUB for hyperparameter tuning of YOLO models?
+
+Yes, you can use Ultralytics HUB for hyperparameter tuning of YOLO models. The HUB offers a no-code platform to easily upload datasets, train models, and perform hyperparameter tuning efficiently. It provides real-time tracking and visualization of tuning progress and results.
+
+Explore more about using Ultralytics HUB for hyperparameter tuning in the [Ultralytics HUB Cloud Training](../hub/cloud-training.md) documentation.