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.

The Box Plot, also known as a box-and-whisker plot, considers the spread of data points around the median and helps identify outliers based on the interquartile range (IQR). Outliers are data points that fall outside the whiskers of the box plot. Z-Score is used for standardization, One-Hot Encoding is used for categorical variables, and K-Means Clustering is a clustering technique and not used for identifying outliers.

In Big Data processing, the first blank should be filled with "Filter," and the second blank with "Join." Filtering and sorting are common operations in data preparation, while aggregations and transformations are typically done using join operations.

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.

Standard Deviation is a measure of the spread or variability of data points around the mean. It quantifies how much individual data points deviate from the average, making it a crucial concept in understanding data variability and distribution.