Add Docs glossary links (#16448)

Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

docs/en/integrations/paddlepaddle.md

@@ -6,7 +6,7 @@ keywords: YOLOv8, PaddlePaddle, export models, computer vision, deep learning, m
 
 # How to Export to PaddlePaddle Format from YOLOv8 Models
 
-Bridging the gap between developing and deploying computer vision models in real-world scenarios with varying conditions can be difficult. PaddlePaddle makes this process easier with its focus on flexibility, performance, and its capability for parallel processing in distributed environments. This means you can use your YOLOv8 computer vision models on a wide variety of devices and platforms, from smartphones to cloud-based servers.
+Bridging the gap between developing and deploying [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) models in real-world scenarios with varying conditions can be difficult. PaddlePaddle makes this process easier with its focus on flexibility, performance, and its capability for parallel processing in distributed environments. This means you can use your YOLOv8 computer vision models on a wide variety of devices and platforms, from smartphones to cloud-based servers.
 
 <p align="center">
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@@ -27,9 +27,9 @@ The ability to export to PaddlePaddle model format allows you to optimize your [
   <img width="75%" src="https://github.com/PaddlePaddle/Paddle/blob/develop/doc/imgs/logo.png" alt="PaddlePaddle Logo">
 </p>
 
-Developed by Baidu, [PaddlePaddle](https://www.paddlepaddle.org.cn/en) (**PA**rallel **D**istributed **D**eep **LE**arning) is China's first open-source deep learning platform. Unlike some frameworks built mainly for research, PaddlePaddle prioritizes ease of use and smooth integration across industries.
+Developed by Baidu, [PaddlePaddle](https://www.paddlepaddle.org.cn/en) (**PA**rallel **D**istributed **D**eep **LE**arning) is China's first open-source [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) platform. Unlike some frameworks built mainly for research, PaddlePaddle prioritizes ease of use and smooth integration across industries.
 
-It offers tools and resources similar to popular frameworks like TensorFlow and PyTorch, making it accessible for developers of all experience levels. From farming and factories to service businesses, PaddlePaddle's large developer community of over 4.77 million is helping create and deploy AI applications.
+It offers tools and resources similar to popular frameworks like [TensorFlow](https://www.ultralytics.com/glossary/tensorflow) and [PyTorch](https://www.ultralytics.com/glossary/pytorch), making it accessible for developers of all experience levels. From farming and factories to service businesses, PaddlePaddle's large developer community of over 4.77 million is helping create and deploy AI applications.
 
 By exporting your Ultralytics YOLOv8 models to PaddlePaddle format, you can tap into PaddlePaddle's strengths in performance optimization. PaddlePaddle prioritizes efficient model execution and reduced memory usage. As a result, your YOLOv8 models can potentially achieve even better performance, delivering top-notch results in practical scenarios.
 
@@ -170,14 +170,14 @@ Exporting Ultralytics YOLOv8 models to PaddlePaddle format is straightforward. Y
 
 For more detailed setup and troubleshooting, check the [Ultralytics Installation Guide](../quickstart.md) and [Common Issues Guide](../guides/yolo-common-issues.md).
 
-### What are the advantages of using PaddlePaddle for model deployment?
+### What are the advantages of using PaddlePaddle for [model deployment](https://www.ultralytics.com/glossary/model-deployment)?
 
 PaddlePaddle offers several key advantages for model deployment:
 
 - **Performance Optimization**: PaddlePaddle excels in efficient model execution and reduced memory usage.
 - **Dynamic-to-Static Graph Compilation**: It supports dynamic-to-static compilation, allowing for runtime optimizations.
 - **Operator Fusion**: By merging compatible operations, it reduces computational overhead.
-- **Quantization Techniques**: Supports both post-training and quantization-aware training, enabling lower-precision data representations for improved performance.
+- **Quantization Techniques**: Supports both post-training and quantization-aware training, enabling lower-[precision](https://www.ultralytics.com/glossary/precision) data representations for improved performance.
 
 You can achieve enhanced results by exporting your Ultralytics YOLOv8 models to PaddlePaddle, ensuring flexibility and high performance across various applications and hardware platforms. Learn more about PaddlePaddle's features [here](https://www.paddlepaddle.org.cn/en).
 
@@ -197,7 +197,7 @@ PaddlePaddle employs several advanced techniques to optimize model performance:
 
 - **Dynamic-to-Static Graph**: Converts models into a static computational graph for runtime optimizations.
 - **Operator Fusion**: Combines compatible operations to minimize memory transfer and increase inference speed.
-- **Quantization**: Reduces model size and increases efficiency using lower-precision data while maintaining accuracy.
+- **Quantization**: Reduces model size and increases efficiency using lower-precision data while maintaining [accuracy](https://www.ultralytics.com/glossary/accuracy).
 
 These techniques prioritize efficient model execution, making PaddlePaddle an excellent option for deploying high-performance YOLOv8 models. For more on optimization, see the [PaddlePaddle official documentation](https://www.paddlepaddle.org.cn/documentation/docs/en/guides/index_en.html).