Add Docs glossary links (#16448)

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

docs/en/datasets/classify/caltech256.md

@@ -16,7 +16,7 @@ The [Caltech-256](https://data.caltech.edu/records/nyy15-4j048) dataset is an ex
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-  <strong>Watch:</strong> How to Train Image Classification Model using Caltech-256 Dataset with Ultralytics HUB
+  <strong>Watch:</strong> How to Train [Image Classification](https://www.ultralytics.com/glossary/image-classification) Model using Caltech-256 Dataset with Ultralytics HUB
 </p>
 
 ## Key Features
@@ -33,7 +33,7 @@ Like Caltech-101, the Caltech-256 dataset does not have a formal split between t
 
 ## Applications
 
-The Caltech-256 dataset is extensively used for training and evaluating deep learning models in object recognition tasks, such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and various other machine learning algorithms. Its diverse set of categories and high-quality images make it an invaluable dataset for research and development in the field of machine learning and computer vision.
+The Caltech-256 dataset is extensively used for training and evaluating [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models in object recognition tasks, such as [Convolutional Neural Networks](https://www.ultralytics.com/glossary/convolutional-neural-network-cnn) (CNNs), Support Vector Machines (SVMs), and various other machine learning algorithms. Its diverse set of categories and high-quality images make it an invaluable dataset for research and development in the field of machine learning and [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv).
 
 ## Usage
 
@@ -84,7 +84,7 @@ If you use the Caltech-256 dataset in your research or development work, please
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-We would like to acknowledge Gregory Griffin, Alex Holub, and Pietro Perona for creating and maintaining the Caltech-256 dataset as a valuable resource for the machine learning and computer vision research community. For more information about the
+We would like to acknowledge Gregory Griffin, Alex Holub, and Pietro Perona for creating and maintaining the Caltech-256 dataset as a valuable resource for the [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) and computer vision research community. For more information about the
 
 Caltech-256 dataset and its creators, visit the [Caltech-256 dataset website](https://data.caltech.edu/records/nyy15-4j048).
 
@@ -96,7 +96,7 @@ The [Caltech-256](https://data.caltech.edu/records/nyy15-4j048) dataset is a lar
 
 ### How can I train a YOLO model on the Caltech-256 dataset using Python or CLI?
 
-To train a YOLO model on the Caltech-256 dataset for 100 epochs, you can use the following code snippets. Refer to the model [Training](../../modes/train.md) page for additional options.
+To train a YOLO model on the Caltech-256 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch), you can use the following code snippets. Refer to the model [Training](../../modes/train.md) page for additional options.
 
 !!! example "Train Example"
 
@@ -123,10 +123,10 @@ To train a YOLO model on the Caltech-256 dataset for 100 epochs, you can use the
 
 The Caltech-256 dataset is widely used for various object recognition tasks such as:
 
-- Training Convolutional Neural Networks (CNNs)
-- Evaluating the performance of Support Vector Machines (SVMs)
+- Training Convolutional [Neural Networks](https://www.ultralytics.com/glossary/neural-network-nn) (CNNs)
+- Evaluating the performance of [Support Vector Machines](https://www.ultralytics.com/glossary/support-vector-machine-svm) (SVMs)
 - Benchmarking new deep learning algorithms
-- Developing object detection models using frameworks like Ultralytics YOLO
+- Developing [object detection](https://www.ultralytics.com/glossary/object-detection) models using frameworks like Ultralytics YOLO
 
 Its diversity and comprehensive annotations make it ideal for research and development in machine learning and computer vision.
 
@@ -141,6 +141,6 @@ Ultralytics YOLO models offer several advantages for training on the Caltech-256
 - **High Accuracy**: YOLO models are known for their state-of-the-art performance in object detection tasks.
 - **Speed**: They provide real-time inference capabilities, making them suitable for applications requiring quick predictions.
 - **Ease of Use**: With Ultralytics HUB, users can train, validate, and deploy models without extensive coding.
-- **Pretrained Models**: Starting from pretrained models, like `yolov8n-cls.pt`, can significantly reduce training time and improve model accuracy.
+- **Pretrained Models**: Starting from pretrained models, like `yolov8n-cls.pt`, can significantly reduce training time and improve model [accuracy](https://www.ultralytics.com/glossary/accuracy).
 
 For more details, explore our [comprehensive training guide](../../modes/train.md).