In time-series analysis, creating lag features means using previous time steps (observations) as new data points. This allows you to incorporate historical information into your model, which can be valuable for forecasting future values in time series data.

Residual Networks (ResNets) are known for their residual connections, which allow for easier training of very deep networks. ResNets have become a standard in deep learning due to their ability to mitigate the vanishing gradient problem, enabling the training of much deeper architectures.

A Box Plot is a commonly used visualization for detecting outliers in a single variable. It displays the distribution of data and identifies potential outliers based on the interquartile range (IQR). Data points outside the whiskers of the box plot are often considered outliers. Box plots are useful for identifying data anomalies.

Data Preprocessing is the step in the Data Science Life Cycle that involves cleaning the data, handling missing values, and preparing it for analysis. This step is crucial for ensuring the quality and reliability of the data used in subsequent analysis.

The mean, also known as the average, is a common measure of central tendency. It's calculated by adding up all the values in the dataset and then dividing by the number of data points. The mean provides a sense of the "typical" value in the dataset.

The True Positive Rate, also known as Sensitivity or Recall, calculates the ratio of true positives to the sum of true positives and false negatives. It measures the model's ability to correctly identify positive cases. It is an important metric in binary classification evaluation.

k-NN imputation is a technique that uses the similarity of data points to impute missing values. It finds records with similar characteristics to the one with missing data and replaces the missing value with the imputed value from its nearest neighbors. Other options are simpler imputation methods.

Long Short-Term Memory (LSTM) is a variant of RNN designed to handle long-term dependencies by maintaining a cell state that can capture information over long sequences. LSTM's ability to store and retrieve information over extended time steps makes it well-suited for tasks involving long-term dependencies in data sequences.

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.