Add Docs glossary links (#16448)

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

docs/en/help/FAQ.md

@@ -12,7 +12,7 @@ This FAQ section addresses common questions and issues users might encounter whi
 
 ### What is Ultralytics and what does it offer?
 
-Ultralytics is a computer vision AI company specializing in state-of-the-art object detection and image segmentation models, with a focus on the YOLO (You Only Look Once) family. Their offerings include:
+Ultralytics is a [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) AI company specializing in state-of-the-art object detection and [image segmentation](https://www.ultralytics.com/glossary/image-segmentation) models, with a focus on the YOLO (You Only Look Once) family. Their offerings include:
 
 - Open-source implementations of [YOLOv5](https://docs.ultralytics.com/models/yolov5/) and [YOLOv8](https://docs.ultralytics.com/models/yolov8/)
 - A wide range of [pre-trained models](https://docs.ultralytics.com/models/) for various computer vision tasks
@@ -41,7 +41,7 @@ Detailed installation instructions can be found in the [quickstart guide](https:
 Minimum requirements:
 
 - Python 3.7+
-- PyTorch 1.7+
+- [PyTorch](https://www.ultralytics.com/glossary/pytorch) 1.7+
 - CUDA-compatible GPU (for GPU acceleration)
 
 Recommended setup:
@@ -80,10 +80,10 @@ For a more in-depth guide, including data preparation and advanced training opti
 Ultralytics offers a diverse range of pretrained YOLOv8 models for various tasks:
 
 - Object Detection: YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, YOLOv8x
-- Instance Segmentation: YOLOv8n-seg, YOLOv8s-seg, YOLOv8m-seg, YOLOv8l-seg, YOLOv8x-seg
+- [Instance Segmentation](https://www.ultralytics.com/glossary/instance-segmentation): YOLOv8n-seg, YOLOv8s-seg, YOLOv8m-seg, YOLOv8l-seg, YOLOv8x-seg
 - Classification: YOLOv8n-cls, YOLOv8s-cls, YOLOv8m-cls, YOLOv8l-cls, YOLOv8x-cls
 
-These models vary in size and complexity, offering different trade-offs between speed and accuracy. Explore the full range of [pretrained models](https://docs.ultralytics.com/models/yolov8/) to find the best fit for your project.
+These models vary in size and complexity, offering different trade-offs between speed and [accuracy](https://www.ultralytics.com/glossary/accuracy). Explore the full range of [pretrained models](https://docs.ultralytics.com/models/yolov8/) to find the best fit for your project.
 
 ### How do I perform inference using a trained Ultralytics model?
 
@@ -113,7 +113,7 @@ Absolutely! Ultralytics models are designed for versatile deployment across vari
 
 - Edge devices: Optimize inference on devices like NVIDIA Jetson or Intel Neural Compute Stick using TensorRT, ONNX, or OpenVINO.
 - Mobile: Deploy on Android or iOS devices by converting models to TFLite or Core ML.
-- Cloud: Leverage frameworks like TensorFlow Serving or PyTorch Serve for scalable cloud deployments.
+- Cloud: Leverage frameworks like [TensorFlow](https://www.ultralytics.com/glossary/tensorflow) Serving or PyTorch Serve for scalable cloud deployments.
 - Web: Implement in-browser inference using ONNX.js or TensorFlow.js.
 
 Ultralytics provides export functions to convert models to various formats for deployment. Explore the wide range of [deployment options](https://docs.ultralytics.com/guides/model-deployment-options/) to find the best solution for your use case.
@@ -124,7 +124,7 @@ Key distinctions include:
 
 - Architecture: YOLOv8 features an improved backbone and head design for enhanced performance.
 - Performance: YOLOv8 generally offers superior accuracy and speed compared to YOLOv5.
-- Tasks: YOLOv8 natively supports object detection, instance segmentation, and classification in a unified framework.
+- Tasks: YOLOv8 natively supports [object detection](https://www.ultralytics.com/glossary/object-detection), instance segmentation, and classification in a unified framework.
 - Codebase: YOLOv8 is implemented with a more modular and extensible architecture, facilitating easier customization and extension.
 - Training: YOLOv8 incorporates advanced training techniques like multi-dataset training and hyperparameter evolution for improved results.
 
@@ -174,9 +174,9 @@ Explore the [YOLO models page](https://docs.ultralytics.com/models/yolov8/) for
 
 Enhancing your YOLO model's performance can be achieved through several techniques:
 
-1. Hyperparameter Tuning: Experiment with different hyperparameters using the [Hyperparameter Tuning Guide](https://docs.ultralytics.com/guides/hyperparameter-tuning/) to optimize model performance.
-2. Data Augmentation: Implement techniques like flip, scale, rotate, and color adjustments to enhance your training dataset and improve model generalization.
-3. Transfer Learning: Leverage pre-trained models and fine-tune them on your specific dataset using the [Train YOLOv8](https://docs.ultralytics.com/modes/train/) guide.
+1. [Hyperparameter Tuning](https://www.ultralytics.com/glossary/hyperparameter-tuning): Experiment with different hyperparameters using the [Hyperparameter Tuning Guide](https://docs.ultralytics.com/guides/hyperparameter-tuning/) to optimize model performance.
+2. [Data Augmentation](https://www.ultralytics.com/glossary/data-augmentation): Implement techniques like flip, scale, rotate, and color adjustments to enhance your training dataset and improve model generalization.
+3. [Transfer Learning](https://www.ultralytics.com/glossary/transfer-learning): Leverage pre-trained models and fine-tune them on your specific dataset using the [Train YOLOv8](https://docs.ultralytics.com/modes/train/) guide.
 4. Export to Efficient Formats: Convert your model to optimized formats like TensorRT or ONNX for faster inference using the [Export guide](../modes/export.md).
 5. Benchmarking: Utilize the [Benchmark Mode](https://docs.ultralytics.com/modes/benchmark/) to measure and improve inference speed and accuracy systematically.
 

docs/en/help/contributing.md

@@ -135,7 +135,7 @@ We encourage all contributors to familiarize themselves with the terms of the AG
 
 Thank you for your interest in contributing to [Ultralytics](https://www.ultralytics.com/) [open-source](https://github.com/ultralytics) YOLO projects. Your participation is essential in shaping the future of our software and building a vibrant community of innovation and collaboration. Whether you're enhancing code, reporting bugs, or suggesting new features, your contributions are invaluable.
 
-We're excited to see your ideas come to life and appreciate your commitment to advancing object detection technology. Together, let's continue to grow and innovate in this exciting open-source journey. Happy coding! 🚀🌟
+We're excited to see your ideas come to life and appreciate your commitment to advancing [object detection](https://www.ultralytics.com/glossary/object-detection) technology. Together, let's continue to grow and innovate in this exciting open-source journey. Happy coding! 🚀🌟
 
 ## FAQ
 

docs/en/help/index.md

@@ -20,9 +20,9 @@ We encourage you to review these resources for a seamless and productive experie
 
 ## FAQ
 
-### What is Ultralytics YOLO and how does it benefit my machine learning projects?
+### What is Ultralytics YOLO and how does it benefit my [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) projects?
 
-Ultralytics YOLO (You Only Look Once) is a state-of-the-art, real-time object detection model. Its latest version, YOLOv8, enhances speed, accuracy, and versatility, making it ideal for a wide range of applications, from real-time video analytics to advanced machine learning research. YOLO's efficiency in detecting objects in images and videos has made it the go-to solution for businesses and researchers looking to integrate robust computer vision capabilities into their projects.
+Ultralytics YOLO (You Only Look Once) is a state-of-the-art, real-time [object detection](https://www.ultralytics.com/glossary/object-detection) model. Its latest version, YOLOv8, enhances speed, [accuracy](https://www.ultralytics.com/glossary/accuracy), and versatility, making it ideal for a wide range of applications, from real-time video analytics to advanced machine learning research. YOLO's efficiency in detecting objects in images and videos has made it the go-to solution for businesses and researchers looking to integrate robust [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) capabilities into their projects.
 
 For more details on YOLOv8, visit the [YOLOv8 documentation](../tasks/detect.md).
 
@@ -32,7 +32,7 @@ Contributing to Ultralytics YOLO repositories is straightforward. Start by revie
 
 ### Why should I use Ultralytics HUB for my machine learning projects?
 
-Ultralytics HUB offers a seamless, no-code solution for managing your machine learning projects. It enables you to generate, train, and deploy AI models like YOLOv8 effortlessly. Unique features include cloud training, real-time tracking, and intuitive dataset management. Ultralytics HUB simplifies the entire workflow, from data processing to model deployment, making it an indispensable tool for both beginners and advanced users.
+Ultralytics HUB offers a seamless, no-code solution for managing your machine learning projects. It enables you to generate, train, and deploy AI models like YOLOv8 effortlessly. Unique features include cloud training, real-time tracking, and intuitive dataset management. Ultralytics HUB simplifies the entire workflow, from data processing to [model deployment](https://www.ultralytics.com/glossary/model-deployment), making it an indispensable tool for both beginners and advanced users.
 
 To get started, visit [Ultralytics HUB Quickstart](../hub/quickstart.md).
 
@@ -42,7 +42,7 @@ Continuous Integration (CI) in Ultralytics involves automated processes that ens
 
 Learn more in the [Continuous Integration (CI) Guide](../help/CI.md).
 
-### How is data privacy handled by Ultralytics?
+### How is [data privacy](https://www.ultralytics.com/glossary/data-privacy) handled by Ultralytics?
 
 Ultralytics takes data privacy seriously. Our [Privacy Policy](../help/privacy.md) outlines how we collect and use anonymized data to improve the YOLO package while prioritizing user privacy and control. We adhere to strict data protection regulations to ensure your information is secure at all times.
 

docs/en/help/privacy.md

@@ -19,7 +19,7 @@ keywords: Ultralytics, data collection, YOLO, Python package, Google Analytics,
 - **System Information**: We collect general non-identifiable information about your computing environment to ensure our package performs well across various systems.
 - **Performance Data**: Understanding the performance of our models during training, validation, and inference helps us in identifying optimization opportunities.
 
-For more information about Google Analytics and data privacy, visit [Google Analytics Privacy](https://support.google.com/analytics/answer/6004245).
+For more information about Google Analytics and [data privacy](https://www.ultralytics.com/glossary/data-privacy), visit [Google Analytics Privacy](https://support.google.com/analytics/answer/6004245).
 
 ### How We Use This Data