The ReLU activation function is known for mitigating the vanishing gradient problem, which is a common issue in deep learning. ReLU allows gradients to flow more freely during backpropagation, making it easier to train deep neural networks.

In Tableau, a "Workbook" is where you can connect to various data sources, design visualizations, and create a unified view of your data. It serves as a container for creating and organizing your data visualizations and analyses.

In L2 regularization (Ridge), the penalty is proportional to the square of the magnitude of the coefficients. This regularization technique adds a penalty term to the loss function based on the sum of squared coefficients, which helps prevent overfitting by discouraging large coefficients.

Data formats like XML allow for tagging or marking up data, which gives it a structured organization. XML is an example of semi-structured data, where data elements are labeled, providing some level of organization.

Random Forest is an ensemble method that builds multiple decision trees. These decision trees are used to make predictions and are combined to create a more accurate and robust model. Random Forest is known for its ability to reduce overfitting and provide reliable predictions, making it a popular choice in machine learning.

Convolutional Neural Networks (CNNs) are commonly used in large-scale image recognition tasks. CNNs have multiple layers that can automatically learn hierarchical features from images, making them well-suited for this purpose.

Canny Edge Detection is a widely used technique for edge detection in images. It applies multiple filters to detect edges with varying intensities, providing information about the location and strength of edges in the image.

Transfer learning in computer vision involves fine-tuning a pre-trained model to adapt it for a new task. It leverages knowledge from a source task to improve performance on a target task, making it more efficient and effective than training from scratch.

Macro-averaging computes the average metric score for each class, treating all other classes as the "negative" class. It provides an equal weight to each class and is useful when you want to assess the model's overall performance while giving equal importance to each class, regardless of class size. Macro-averaging can be particularly useful in imbalanced multi-class classification problems.

To adapt the model for rural scenes, fine-tuning the existing model is a practical approach. Fine-tuning involves training the model on the new rural scene data, which allows the model to leverage its knowledge from the urban scene while adapting to rural conditions.