Add https://youtu.be/w4yHORvDBw0 to docs (#16031)

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

docs/en/guides/coral-edge-tpu-on-raspberry-pi.md

@@ -14,6 +14,17 @@ keywords: Coral Edge TPU, Raspberry Pi, YOLOv8, Ultralytics, TensorFlow Lite, ML
 
 The Coral Edge TPU is a compact device that adds an Edge TPU coprocessor to your system. It enables low-power, high-performance ML inference for TensorFlow Lite models. Read more at the [Coral Edge TPU home page](https://coral.ai/products/accelerator).
 
+<p align="center">
+  <br>
+  <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/w4yHORvDBw0"
+    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 Run Inference on Raspberry Pi using Google Coral Edge TPU
+</p>
+
 ## Boost Raspberry Pi Model Performance with Coral Edge TPU
 
 Many people want to run their models on an embedded or mobile device such as a Raspberry Pi, since they are very power efficient and can be used in many different applications. However, the inference performance on these devices is usually poor even when using formats like [onnx](../integrations/onnx.md) or [openvino](../integrations/openvino.md). The Coral Edge TPU is a great solution to this problem, since it can be used with a Raspberry Pi and accelerate inference performance greatly.