Add Docs glossary links (#16448)

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

docs/en/modes/train.md

@@ -10,7 +10,7 @@ keywords: Ultralytics, YOLOv8, model training, deep learning, object detection,
 
 ## Introduction
 
-Training a deep learning model involves feeding it data and adjusting its parameters so that it can make accurate predictions. Train mode in Ultralytics YOLOv8 is engineered for effective and efficient training of object detection models, fully utilizing modern hardware capabilities. This guide aims to cover all the details you need to get started with training your own models using YOLOv8's robust set of features.
+Training a [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) model involves feeding it data and adjusting its parameters so that it can make accurate predictions. Train mode in Ultralytics YOLOv8 is engineered for effective and efficient training of object detection models, fully utilizing modern hardware capabilities. This guide aims to cover all the details you need to get started with training your own models using YOLOv8's robust set of features.
 
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@@ -47,7 +47,7 @@ The following are some notable features of YOLOv8's Train mode:
 
 ## Usage Examples
 
-Train YOLOv8n on the COCO8 dataset for 100 epochs at image size 640. The training device can be specified using the `device` argument. If no argument is passed GPU `device=0` will be used if available, otherwise `device='cpu'` will be used. See Arguments section below for a full list of training arguments.
+Train YOLOv8n on the COCO8 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) at image size 640. The training device can be specified using the `device` argument. If no argument is passed GPU `device=0` will be used if available, otherwise `device='cpu'` will be used. See Arguments section below for a full list of training arguments.
 
 !!! example "Single-GPU and CPU Training Example"
 
@@ -140,7 +140,7 @@ While leveraging the computational power of the M1/M2 chips, this enables more e
 
 Resuming training from a previously saved state is a crucial feature when working with deep learning models. This can come in handy in various scenarios, like when the training process has been unexpectedly interrupted, or when you wish to continue training a model with new data or for more epochs.
 
-When training is resumed, Ultralytics YOLO loads the weights from the last saved model and also restores the optimizer state, learning rate scheduler, and the epoch number. This allows you to continue the training process seamlessly from where it was left off.
+When training is resumed, Ultralytics YOLO loads the weights from the last saved model and also restores the optimizer state, [learning rate](https://www.ultralytics.com/glossary/learning-rate) scheduler, and the epoch number. This allows you to continue the training process seamlessly from where it was left off.
 
 You can easily resume training in Ultralytics YOLO by setting the `resume` argument to `True` when calling the `train` method, and specifying the path to the `.pt` file containing the partially trained model weights.
 
@@ -173,7 +173,7 @@ Remember that checkpoints are saved at the end of every epoch by default, or at
 
 ## Train Settings
 
-The training settings for YOLO models encompass various hyperparameters and configurations used during the training process. These settings influence the model's performance, speed, and accuracy. Key training settings include batch size, learning rate, momentum, and weight decay. Additionally, the choice of optimizer, loss function, and training dataset composition can impact the training process. Careful tuning and experimentation with these settings are crucial for optimizing performance.
+The training settings for YOLO models encompass various hyperparameters and configurations used during the training process. These settings influence the model's performance, speed, and [accuracy](https://www.ultralytics.com/glossary/accuracy). Key training settings include batch size, learning rate, momentum, and weight decay. Additionally, the choice of optimizer, [loss function](https://www.ultralytics.com/glossary/loss-function), and training dataset composition can impact the training process. Careful tuning and experimentation with these settings are crucial for optimizing performance.
 
 {% include "macros/train-args.md" %}
 
@@ -181,13 +181,13 @@ The training settings for YOLO models encompass various hyperparameters and conf
 
     The `batch` argument can be configured in three ways:
 
-    - **Fixed Batch Size**: Set an integer value (e.g., `batch=16`), specifying the number of images per batch directly.
+    - **Fixed [Batch Size](https://www.ultralytics.com/glossary/batch-size)**: Set an integer value (e.g., `batch=16`), specifying the number of images per batch directly.
     - **Auto Mode (60% GPU Memory)**: Use `batch=-1` to automatically adjust batch size for approximately 60% CUDA memory utilization.
     - **Auto Mode with Utilization Fraction**: Set a fraction value (e.g., `batch=0.70`) to adjust batch size based on the specified fraction of GPU memory usage.
 
 ## Augmentation Settings and Hyperparameters
 
-Augmentation techniques are essential for improving the robustness and performance of YOLO models by introducing variability into the training data, helping the model generalize better to unseen data. The following table outlines the purpose and effect of each augmentation argument:
+Augmentation techniques are essential for improving the robustness and performance of YOLO models by introducing variability into the [training data](https://www.ultralytics.com/glossary/training-data), helping the model generalize better to unseen data. The following table outlines the purpose and effect of each augmentation argument:
 
 {% include "macros/augmentation-args.md" %}
 
@@ -243,7 +243,7 @@ After running this script, you will need to sign in to your ClearML account on t
 
 ### TensorBoard
 
-[TensorBoard](https://www.tensorflow.org/tensorboard) is a visualization toolkit for TensorFlow. It allows you to visualize your TensorFlow graph, plot quantitative metrics about the execution of your graph, and show additional data like images that pass through it.
+[TensorBoard](https://www.tensorflow.org/tensorboard) is a visualization toolkit for [TensorFlow](https://www.ultralytics.com/glossary/tensorflow). It allows you to visualize your TensorFlow graph, plot quantitative metrics about the execution of your graph, and show additional data like images that pass through it.
 
 To use TensorBoard in [Google Colab](https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb):
 
@@ -272,7 +272,7 @@ After setting up your logger, you can then proceed with your model training. All
 
 ## FAQ
 
-### How do I train an object detection model using Ultralytics YOLOv8?
+### How do I train an [object detection](https://www.ultralytics.com/glossary/object-detection) model using Ultralytics YOLOv8?
 
 To train an object detection model using Ultralytics YOLOv8, you can either use the Python API or the CLI. Below is an example for both: