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Add Docs glossary links (#16448)

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Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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docs/en/guides/analytics.md
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@ -8,7 +8,7 @@ keywords: Ultralytics, YOLOv8, data visualization, line graphs, bar plots, pie c
## Introduction
This guide provides a comprehensive overview of three fundamental types of data visualizations: line graphs, bar plots, and pie charts. Each section includes step-by-step instructions and code snippets on how to create these visualizations using Python.
This guide provides a comprehensive overview of three fundamental types of [data visualizations](https://www.ultralytics.com/glossary/data-visualization): line graphs, bar plots, and pie charts. Each section includes step-by-step instructions and code snippets on how to create these visualizations using Python.
### Visual Samples
@ -361,7 +361,7 @@ For further details on configuring the `Analytics` class, visit the [Analytics u
Using Ultralytics YOLOv8 for creating bar plots offers several benefits:
1. **Real-time Data Visualization**: Seamlessly integrate object detection results into bar plots for dynamic updates.
1. **Real-time Data Visualization**: Seamlessly integrate [object detection](https://www.ultralytics.com/glossary/object-detection) results into bar plots for dynamic updates.
2. **Ease of Use**: Simple API and functions make it straightforward to implement and visualize data.
3. **Customization**: Customize titles, labels, colors, and more to fit your specific requirements.
4. **Efficiency**: Efficiently handle large amounts of data and update plots in real-time during video processing.
@ -472,11 +472,11 @@ cv2.destroyAllWindows()
To learn about the complete functionality, see the [Tracking](../modes/track.md) section.
### What makes Ultralytics YOLOv8 different from other object detection solutions like OpenCV and TensorFlow?
### What makes Ultralytics YOLOv8 different from other object detection solutions like [OpenCV](https://www.ultralytics.com/glossary/opencv) and [TensorFlow](https://www.ultralytics.com/glossary/tensorflow)?
Ultralytics YOLOv8 stands out from other object detection solutions like OpenCV and TensorFlow for multiple reasons:
1. **State-of-the-art Accuracy**: YOLOv8 provides superior accuracy in object detection, segmentation, and classification tasks.
1. **State-of-the-art [Accuracy](https://www.ultralytics.com/glossary/accuracy)**: YOLOv8 provides superior accuracy in object detection, segmentation, and classification tasks.
2. **Ease of Use**: User-friendly API allows for quick implementation and integration without extensive coding.
3. **Real-time Performance**: Optimized for high-speed inference, suitable for real-time applications.
4. **Diverse Applications**: Supports various tasks including multi-object tracking, custom model training, and exporting to different formats like ONNX, TensorRT, and CoreML.