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/integrations/edge-tpu.md

@@ -18,7 +18,7 @@ Exporting models to TensorFlow Edge TPU makes machine learning tasks fast and ef
   <img width="100%" src="https://coral.ai/static/docs/images/edgetpu/compile-workflow.png" alt="TFLite Edge TPU">
 </p>
 
-The Edge TPU works with quantized models. Quantization makes models smaller and faster without losing much accuracy. It is ideal for the limited resources of edge computing, allowing applications to respond quickly by reducing latency and allowing for quick data processing locally, without cloud dependency. Local processing also keeps user data private and secure since it's not sent to a remote server​​​​.
+The Edge TPU works with quantized models. Quantization makes models smaller and faster without losing much accuracy. It is ideal for the limited resources of edge computing, allowing applications to respond quickly by reducing latency and allowing for quick data processing locally, without cloud dependency. Local processing also keeps user data private and secure since it's not sent to a remote server.
 
 ## Key Features of TFLite Edge TPU
 
@@ -116,3 +116,70 @@ In this guide, we've learned how to export Ultralytics YOLOv8 models to TFLite E
 For further details on usage, visit the [Edge TPU official website](https://cloud.google.com/edge-tpu).
 
 Also, for more information on other Ultralytics YOLOv8 integrations, please visit our [integration guide page](index.md). There, you'll discover valuable resources and insights.
+
+## FAQ
+
+### How do I export a YOLOv8 model to TFLite Edge TPU format?
+
+To export a YOLOv8 model to TFLite Edge TPU format, you can follow these steps:
+
+!!! Example "Usage"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load the YOLOv8 model
+        model = YOLO("yolov8n.pt")
+
+        # Export the model to TFLite Edge TPU format
+        model.export(format="edgetpu")  # creates 'yolov8n_full_integer_quant_edgetpu.tflite'
+
+        # Load the exported TFLite Edge TPU model
+        edgetpu_model = YOLO("yolov8n_full_integer_quant_edgetpu.tflite")
+
+        # Run inference
+        results = edgetpu_model("https://ultralytics.com/images/bus.jpg")
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Export a YOLOv8n PyTorch model to TFLite Edge TPU format
+        yolo export model=yolov8n.pt format=edgetpu  # creates 'yolov8n_full_integer_quant_edgetpu.tflite'
+
+        # Run inference with the exported model
+        yolo predict model=yolov8n_full_integer_quant_edgetpu.tflite source='https://ultralytics.com/images/bus.jpg'
+        ```
+
+For complete details on exporting models to other formats, refer to our [export guide](../modes/export.md).
+
+### What are the benefits of exporting YOLOv8 models to TFLite Edge TPU?
+
+Exporting YOLOv8 models to TFLite Edge TPU offers several benefits:
+
+- **Optimized Performance**: Achieve high-speed neural network performance with minimal power consumption.
+- **Reduced Latency**: Quick local data processing without the need for cloud dependency.
+- **Enhanced Privacy**: Local processing keeps user data private and secure.
+
+This makes it ideal for applications in edge computing, where devices have limited power and computational resources. Learn more about [why you should export](#why-should-you-export-to-tflite-edge-tpu).
+
+### Can I deploy TFLite Edge TPU models on mobile and embedded devices?
+
+Yes, TensorFlow Lite Edge TPU models can be deployed directly on mobile and embedded devices. This deployment approach allows models to execute directly on the hardware, offering faster and more efficient inferencing. For integration examples, check our [guide on deploying Coral Edge TPU on Raspberry Pi](../guides/coral-edge-tpu-on-raspberry-pi.md).
+
+### What are some common use cases for TFLite Edge TPU models?
+
+Common use cases for TFLite Edge TPU models include:
+
+- **Smart Cameras**: Enhancing real-time image and video analysis.
+- **IoT Devices**: Enabling smart home and industrial automation.
+- **Healthcare**: Accelerating medical imaging and diagnostics.
+- **Retail**: Improving inventory management and customer behavior analysis.
+
+These applications benefit from the high performance and low power consumption of TFLite Edge TPU models. Discover more about [usage scenarios](#deployment-options-with-tflite-edge-tpu).
+
+### How can I troubleshoot issues while exporting or deploying TFLite Edge TPU models?
+
+If you encounter issues while exporting or deploying TFLite Edge TPU models, refer to our [Common Issues guide](../guides/yolo-common-issues.md) for troubleshooting tips. This guide covers common problems and solutions to help you ensure smooth operation. For additional support, visit our [Help Center](https://docs.ultralytics.com/help/).