Add Docs glossary links (#16448)

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

docs/en/guides/steps-of-a-cv-project.md

@@ -8,7 +8,7 @@ keywords: Computer Vision, AI, Object Detection, Image Classification, Instance
 
 ## Introduction
 
-Computer vision is a subfield of artificial intelligence (AI) that helps computers see and understand the world like humans do. It processes and analyzes images or videos to extract information, recognize patterns, and make decisions based on that data.
+Computer vision is a subfield of [artificial intelligence](https://www.ultralytics.com/glossary/artificial-intelligence-ai) (AI) that helps computers see and understand the world like humans do. It processes and analyzes images or videos to extract information, recognize patterns, and make decisions based on that data.
 
 <p align="center">
   <br>
@@ -18,7 +18,7 @@ Computer vision is a subfield of artificial intelligence (AI) that helps compute
     allowfullscreen>
   </iframe>
   <br>
-  <strong>Watch:</strong> How to Do Computer Vision Projects | A Step-by-Step Guide
+  <strong>Watch:</strong> How to Do [Computer Vision](https://www.ultralytics.com/glossary/computer-vision-cv) Projects | A Step-by-Step Guide
 </p>
 
 Computer vision techniques like [object detection](../tasks/detect.md), [image classification](../tasks/classify.md), and [instance segmentation](../tasks/segment.md) can be applied across various industries, from [autonomous driving](https://www.ultralytics.com/solutions/ai-in-self-driving) to [medical imaging](https://www.ultralytics.com/solutions/ai-in-healthcare) to gain valuable insights.
@@ -60,7 +60,7 @@ Here are some examples of project objectives and the computer vision tasks that
     - **Computer Vision Task:** Image segmentation is suitable for medical imaging because it provides accurate and detailed boundaries of tumors that are crucial for assessing size, shape, and treatment planning.
 
 - **Objective:** To create a digital system that categorizes various documents (e.g., invoices, receipts, legal paperwork) to improve organizational efficiency and document retrieval.
-    - **Computer Vision Task:** Image classification is ideal here as it handles one document at a time, without needing to consider the document's position in the image. This approach simplifies and accelerates the sorting process.
+    - **Computer Vision Task:** [Image classification](https://www.ultralytics.com/glossary/image-classification) is ideal here as it handles one document at a time, without needing to consider the document's position in the image. This approach simplifies and accelerates the sorting process.
 
 ### Step 1.5: Selecting the Right Model and Training Approach
 
@@ -68,13 +68,13 @@ After understanding the project objective and suitable computer vision tasks, an
 
 Depending on the objective, you might choose to select the model first or after seeing what data you are able to collect in Step 2. For example, suppose your project is highly dependent on the availability of specific types of data. In that case, it may be more practical to gather and analyze the data first before selecting a model. On the other hand, if you have a clear understanding of the model requirements, you can choose the model first and then collect data that fits those specifications.
 
-Choosing between training from scratch or using transfer learning affects how you prepare your data. Training from scratch requires a diverse dataset to build the model's understanding from the ground up. Transfer learning, on the other hand, allows you to use a pre-trained model and adapt it with a smaller, more specific dataset. Also, choosing a specific model to train will determine how you need to prepare your data, such as resizing images or adding annotations, according to the model's specific requirements.
+Choosing between training from scratch or using [transfer learning](https://www.ultralytics.com/glossary/transfer-learning) affects how you prepare your data. Training from scratch requires a diverse dataset to build the model's understanding from the ground up. Transfer learning, on the other hand, allows you to use a pre-trained model and adapt it with a smaller, more specific dataset. Also, choosing a specific model to train will determine how you need to prepare your data, such as resizing images or adding annotations, according to the model's specific requirements.
 
 <p align="center">
   <img width="100%" src="https://github.com/ultralytics/docs/releases/download/0/training-from-scratch-vs-transfer-learning.avif" alt="Training From Scratch Vs. Using Transfer Learning">
 </p>
 
-Note: When choosing a model, consider its [deployment](./model-deployment-options.md) to ensure compatibility and performance. For example, lightweight models are ideal for edge computing due to their efficiency on resource-constrained devices. To learn more about the key points related to defining your project, read [our guide](./defining-project-goals.md) on defining your project's goals and selecting the right model.
+Note: When choosing a model, consider its [deployment](./model-deployment-options.md) to ensure compatibility and performance. For example, lightweight models are ideal for [edge computing](https://www.ultralytics.com/glossary/edge-computing) due to their efficiency on resource-constrained devices. To learn more about the key points related to defining your project, read [our guide](./defining-project-goals.md) on defining your project's goals and selecting the right model.
 
 Before getting into the hands-on work of a computer vision project, it's important to have a clear understanding of these details. Double-check that you've considered the following before moving on to Step 2:
 
@@ -93,8 +93,8 @@ Some libraries, like Ultralytics, provide [built-in support for various datasets
 However, if you choose to collect images or take your own pictures, you'll need to annotate your data. Data annotation is the process of labeling your data to impart knowledge to your model. The type of data annotation you'll work with depends on your specific computer vision technique. Here are some examples:
 
 - **Image Classification:** You'll label the entire image as a single class.
-- **Object Detection:** You'll draw bounding boxes around each object in the image and label each box.
-- **Image Segmentation:** You'll label each pixel in the image according to the object it belongs to, creating detailed object boundaries.
+- **[Object Detection](https://www.ultralytics.com/glossary/object-detection):** You'll draw bounding boxes around each object in the image and label each box.
+- **[Image Segmentation](https://www.ultralytics.com/glossary/image-segmentation):** You'll label each pixel in the image according to the object it belongs to, creating detailed object boundaries.
 
 <p align="center">
   <img width="100%" src="https://github.com/ultralytics/docs/releases/download/0/different-types-of-image-annotation.avif" alt="Different Types of Image Annotation">
@@ -102,14 +102,14 @@ However, if you choose to collect images or take your own pictures, you'll need
 
 [Data collection and annotation](./data-collection-and-annotation.md) can be a time-consuming manual effort. Annotation tools can help make this process easier. Here are some useful open annotation tools: [LabeI Studio](https://github.com/HumanSignal/label-studio), [CVAT](https://github.com/cvat-ai/cvat), and [Labelme](https://github.com/labelmeai/labelme).
 
-## Step 3: Data Augmentation and Splitting Your Dataset
+## Step 3: [Data Augmentation](https://www.ultralytics.com/glossary/data-augmentation) and Splitting Your Dataset
 
 After collecting and annotating your image data, it's important to first split your dataset into training, validation, and test sets before performing data augmentation. Splitting your dataset before augmentation is crucial to test and validate your model on original, unaltered data. It helps accurately assess how well the model generalizes to new, unseen data.
 
 Here's how to split your data:
 
 - **Training Set:** It is the largest portion of your data, typically 70-80% of the total, used to train your model.
-- **Validation Set:** Usually around 10-15% of your data; this set is used to tune hyperparameters and validate the model during training, helping to prevent overfitting.
+- **Validation Set:** Usually around 10-15% of your data; this set is used to tune hyperparameters and validate the model during training, helping to prevent [overfitting](https://www.ultralytics.com/glossary/overfitting).
 - **Test Set:** The remaining 10-15% of your data is set aside as the test set. It is used to evaluate the model's performance on unseen data after training is complete.
 
 After splitting your data, you can perform data augmentation by applying transformations like rotating, scaling, and flipping images to artificially increase the size of your dataset. Data augmentation makes your model more robust to variations and improves its performance on unseen images.
@@ -118,7 +118,7 @@ After splitting your data, you can perform data augmentation by applying transfo
   <img width="100%" src="https://github.com/ultralytics/docs/releases/download/0/examples-of-data-augmentations.avif" alt="Examples of Data Augmentations">
 </p>
 
-Libraries like OpenCV, Albumentations, and TensorFlow offer flexible augmentation functions that you can use. Additionally, some libraries, such as Ultralytics, have [built-in augmentation settings](../modes/train.md) directly within its model training function, simplifying the process.
+Libraries like [OpenCV](https://www.ultralytics.com/glossary/opencv), Albumentations, and [TensorFlow](https://www.ultralytics.com/glossary/tensorflow) offer flexible augmentation functions that you can use. Additionally, some libraries, such as Ultralytics, have [built-in augmentation settings](../modes/train.md) directly within its model training function, simplifying the process.
 
 To understand your data better, you can use tools like [Matplotlib](https://matplotlib.org/) or [Seaborn](https://seaborn.pydata.org/) to visualize the images and analyze their distribution and characteristics. Visualizing your data helps identify patterns, anomalies, and the effectiveness of your augmentation techniques. You can also use [Ultralytics Explorer](../datasets/explorer/index.md), a tool for exploring computer vision datasets with semantic search, SQL queries, and vector similarity search.
 
@@ -134,23 +134,23 @@ Once your dataset is ready for training, you can focus on setting up the necessa
 
 First, you'll need to make sure your environment is configured correctly. Typically, this includes the following:
 
-- Installing essential libraries and frameworks like TensorFlow, PyTorch, or [Ultralytics](../quickstart.md).
+- Installing essential libraries and frameworks like TensorFlow, [PyTorch](https://www.ultralytics.com/glossary/pytorch), or [Ultralytics](../quickstart.md).
 - If you are using a GPU, installing libraries like CUDA and cuDNN will help enable GPU acceleration and speed up the training process.
 
-Then, you can load your training and validation datasets into your environment. Normalize and preprocess the data through resizing, format conversion, or augmentation. With your model selected, configure the layers and specify hyperparameters. Compile the model by setting the loss function, optimizer, and performance metrics.
+Then, you can load your training and validation datasets into your environment. Normalize and preprocess the data through resizing, format conversion, or augmentation. With your model selected, configure the layers and specify hyperparameters. Compile the model by setting the [loss function](https://www.ultralytics.com/glossary/loss-function), optimizer, and performance metrics.
 
-Libraries like Ultralytics simplify the training process. You can [start training](../modes/train.md) by feeding data into the model with minimal code. These libraries handle weight adjustments, backpropagation, and validation automatically. They also offer tools to monitor progress and adjust hyperparameters easily. After training, save the model and its weights with a few commands.
+Libraries like Ultralytics simplify the training process. You can [start training](../modes/train.md) by feeding data into the model with minimal code. These libraries handle weight adjustments, [backpropagation](https://www.ultralytics.com/glossary/backpropagation), and validation automatically. They also offer tools to monitor progress and adjust hyperparameters easily. After training, save the model and its weights with a few commands.
 
 It's important to keep in mind that proper dataset management is vital for efficient training. Use version control for datasets to track changes and ensure reproducibility. Tools like [DVC (Data Version Control)](../integrations/dvc.md) can help manage large datasets.
 
-## Step 5: Model Evaluation and Model Finetuning
+## Step 5: Model Evaluation and Model [Finetuning](https://www.ultralytics.com/glossary/fine-tuning)
 
-It's important to assess your model's performance using various metrics and refine it to improve accuracy. [Evaluating](../modes/val.md) helps identify areas where the model excels and where it may need improvement. Fine-tuning ensures the model is optimized for the best possible performance.
+It's important to assess your model's performance using various metrics and refine it to improve [accuracy](https://www.ultralytics.com/glossary/accuracy). [Evaluating](../modes/val.md) helps identify areas where the model excels and where it may need improvement. Fine-tuning ensures the model is optimized for the best possible performance.
 
-- **[Performance Metrics](./yolo-performance-metrics.md):** Use metrics like accuracy, precision, recall, and F1-score to evaluate your model's performance. These metrics provide insights into how well your model is making predictions.
+- **[Performance Metrics](./yolo-performance-metrics.md):** Use metrics like accuracy, [precision](https://www.ultralytics.com/glossary/precision), recall, and F1-score to evaluate your model's performance. These metrics provide insights into how well your model is making predictions.
 - **[Hyperparameter Tuning](./hyperparameter-tuning.md):** Adjust hyperparameters to optimize model performance. Techniques like grid search or random search can help find the best hyperparameter values.
 
-- Fine-Tuning: Make small adjustments to the model architecture or training process to enhance performance. This might involve tweaking learning rates, batch sizes, or other model parameters.
+- Fine-Tuning: Make small adjustments to the model architecture or training process to enhance performance. This might involve tweaking [learning rates](https://www.ultralytics.com/glossary/learning-rate), [batch sizes](https://www.ultralytics.com/glossary/batch-size), or other model parameters.
 
 ## Step 6: Model Testing
 
@@ -158,9 +158,9 @@ In this step, you can make sure that your model performs well on completely unse
 
 It's important to thoroughly test and debug any common issues that may arise. Test your model on a separate test dataset that was not used during training or validation. This dataset should represent real-world scenarios to ensure the model's performance is consistent and reliable.
 
-Also, address common problems such as overfitting, underfitting, and data leakage. Use techniques like cross-validation and anomaly detection to identify and fix these issues.
+Also, address common problems such as overfitting, [underfitting](https://www.ultralytics.com/glossary/underfitting), and data leakage. Use techniques like cross-validation and [anomaly detection](https://www.ultralytics.com/glossary/anomaly-detection) to identify and fix these issues.
 
-## Step 7: Model Deployment
+## Step 7: [Model Deployment](https://www.ultralytics.com/glossary/model-deployment)
 
 Once your model has been thoroughly tested, it's time to deploy it. Deployment involves making your model available for use in a production environment. Here are the steps to deploy a computer vision model:
 
@@ -222,7 +222,7 @@ Tools like [Label Studio](https://github.com/HumanSignal/label-studio), [CVAT](h
 Splitting your dataset before augmentation helps validate model performance on original, unaltered data. Follow these steps:
 
 - **Training Set**: 70-80% of your data.
-- **Validation Set**: 10-15% for hyperparameter tuning.
+- **Validation Set**: 10-15% for [hyperparameter tuning](https://www.ultralytics.com/glossary/hyperparameter-tuning).
 - **Test Set**: Remaining 10-15% for final evaluation.
 
 After splitting, apply data augmentation techniques like rotation, scaling, and flipping to increase dataset diversity. Libraries such as Albumentations and OpenCV can help. Ultralytics also offers [built-in augmentation settings](../modes/train.md) for convenience.