Add https://youtu.be/_gRqR-miFPE & https://youtu.be/CfbHwPG01cE to docs (#14817)

docs/en/guides/steps-of-a-cv-project.md

@@ -10,6 +10,17 @@ keywords: Computer Vision, AI, Object Detection, Image Classification, Instance
 
 Computer vision is a subfield of artificial intelligence (AI) that helps computers see and understand the world like humans do. It processes and analyzes images or videos to extract information, recognize patterns, and make decisions based on that data.
 
+<p align="center">
+  <br>
+  <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/CfbHwPG01cE"
+    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 Do Computer Vision Projects | A Step-by-Step Guide
+</p>
+
 Computer vision techniques like [object detection](../tasks/detect.md), [image classification](../tasks/classify.md), and [instance segmentation](../tasks/segment.md) can be applied across various industries, from [autonomous driving](https://www.ultralytics.com/solutions/ai-in-self-driving) to [medical imaging](https://www.ultralytics.com/solutions/ai-in-healthcare) to gain valuable insights.
 
 <p align="center">

docs/en/models/yolov10.md

@@ -10,6 +10,17 @@ YOLOv10, built on the [Ultralytics](https://ultralytics.com) [Python package](ht
 
 ![YOLOv10 consistent dual assignment for NMS-free training](https://github.com/ultralytics/ultralytics/assets/26833433/f9b1bec0-928e-41ce-a205-e12db3c4929a)
 
+<p align="center">
+  <br>
+  <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/_gRqR-miFPE"
+    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 Train YOLOv10 on SKU-110k Dataset using Ultralytics | Retail Dataset
+</p>
+
 ## Overview
 
 Real-time object detection aims to accurately predict object categories and positions in images with low latency. The YOLO series has been at the forefront of this research due to its balance between performance and efficiency. However, reliance on NMS and architectural inefficiencies have hindered optimal performance. YOLOv10 addresses these issues by introducing consistent dual assignments for NMS-free training and a holistic efficiency-accuracy driven model design strategy.