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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/hub/app/ios.md
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@ -4,7 +4,7 @@ description: Discover the Ultralytics iOS App for running YOLO models on your iP
keywords: Ultralytics, iOS App, YOLO models, real-time object detection, Apple Neural Engine, Core ML, FP16 quantization, INT8 quantization, machine learning
---
# Ultralytics iOS App: Real-time Object Detection with YOLO Models
# Ultralytics iOS App: Real-time [Object Detection](https://www.ultralytics.com/glossary/object-detection) with YOLO Models
<a href="https://ultralytics.com/hub" target="_blank">
<img width="100%" src="https://github.com/ultralytics/docs/releases/download/0/ultralytics-android-app-detection.avif" alt="Ultralytics HUB preview image"></a>
@ -44,7 +44,7 @@ The Ultralytics iOS App is a powerful tool that allows you to run YOLO models di
## Quantization and Acceleration
To achieve real-time performance on your iOS device, YOLO models are quantized to either FP16 or INT8 precision. Quantization is a process that reduces the numerical precision of the model's weights and biases, thus reducing the model's size and the amount of computation required. This results in faster inference times without significantly affecting the model's accuracy.
To achieve real-time performance on your iOS device, YOLO models are quantized to either FP16 or INT8 [precision](https://www.ultralytics.com/glossary/precision). Quantization is a process that reduces the numerical precision of the model's weights and biases, thus reducing the model's size and the amount of computation required. This results in faster inference times without significantly affecting the model's [accuracy](https://www.ultralytics.com/glossary/accuracy).
### FP16 Quantization
@ -56,7 +56,7 @@ INT8 (or 8-bit integer) quantization further reduces the model's size and comput
## Apple Neural Engine
The Apple Neural Engine (ANE) is a dedicated hardware component integrated into Apple's A-series and M-series chips. It's designed to accelerate machine learning tasks, particularly for neural networks, allowing for faster and more efficient execution of your YOLO models.
The Apple Neural Engine (ANE) is a dedicated hardware component integrated into Apple's A-series and M-series chips. It's designed to accelerate [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) tasks, particularly for [neural networks](https://www.ultralytics.com/glossary/neural-network-nn), allowing for faster and more efficient execution of your YOLO models.
By combining quantized YOLO models with the Apple Neural Engine, the Ultralytics iOS App achieves real-time object detection on your iOS device without compromising on accuracy or performance.