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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/raspberry-pi.md
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@ -49,7 +49,7 @@ The first thing to do after getting your hands on a Raspberry Pi is to flash a m
## Set Up Ultralytics
There are two ways of setting up Ultralytics package on Raspberry Pi to build your next Computer Vision project. You can use either of them.
There are two ways of setting up Ultralytics package on Raspberry Pi to build your next [Computer Vision](https://www.ultralytics.com/glossary/computer-vision-cv) project. You can use either of them.
- [Start with Docker](#start-with-docker)
- [Start without Docker](#start-without-docker)
@ -70,7 +70,7 @@ After this is done, skip to [Use NCNN on Raspberry Pi section](#use-ncnn-on-rasp
#### Install Ultralytics Package
Here we will install Ultralytics package on the Raspberry Pi with optional dependencies so that we can export the PyTorch models to other different formats.
Here we will install Ultralytics package on the Raspberry Pi with optional dependencies so that we can export the [PyTorch](https://www.ultralytics.com/glossary/pytorch) models to other different formats.
1. Update packages list, install pip and upgrade to latest
@ -136,7 +136,7 @@ The YOLOv8n model in PyTorch format is converted to NCNN to run inference with t
## Raspberry Pi 5 vs Raspberry Pi 4 YOLOv8 Benchmarks
YOLOv8 benchmarks were run by the Ultralytics team on nine different model formats measuring speed and accuracy: PyTorch, TorchScript, ONNX, OpenVINO, TF SavedModel, TF GraphDef, TF Lite, PaddlePaddle, NCNN. Benchmarks were run on both Raspberry Pi 5 and Raspberry Pi 4 at FP32 precision with default input image size of 640.
YOLOv8 benchmarks were run by the Ultralytics team on nine different model formats measuring speed and [accuracy](https://www.ultralytics.com/glossary/accuracy): PyTorch, TorchScript, ONNX, OpenVINO, TF SavedModel, TF GraphDef, TF Lite, PaddlePaddle, NCNN. Benchmarks were run on both Raspberry Pi 5 and Raspberry Pi 4 at FP32 [precision](https://www.ultralytics.com/glossary/precision) with default input image size of 640.
!!! note