L

Definition: A technique in machine learning that helps to prevent overfitting by adding a penalty equivalent to the absolute value of the magnitude of coefficients. This method encourages the model to keep the weights of features small, making the model simpler and less prone to overfitting.

Definition: Similar to L1 regularisation, but it adds a penalty equivalent to the square of the magnitude of coefficients. This technique also helps to prevent overfitting and is particularly useful when dealing with high-dimensional data.

Definition: A process in data preprocessing that involves converting categorical data into a numerical format so that machine learning algorithms can understand it. Each category is assigned a unique integer based on alphabetical ordering.

Definition: Data that has been tagged with one or more labels identifying certain properties or classifications, which is used to train supervised learning models.

Definition: A strategy used in optimisation problems that helps find the local maxima and minima of a function subject to equality constraints. It is widely used in machine learning for handling constraints in optimisation problems.

Definition: A data-processing architecture designed to handle massive quantities of data by taking advantage of both batch and stream-processing methods. This approach is beneficial in AI for managing and analysing large datasets efficiently.

Definition: In the context of data management, it’s the storage area where data is initially collected. In AI and machine learning workflows, the landing zone is crucial for data ingestion before further processing and analysis.

Definition: A variable that is not directly observed but is inferred from other variables within a dataset. These are often used in statistical models to account for hidden or unseen influences.

Definition: A collection of neurons that process a set of inputs to produce an output in a neural network. Layers are stacked to build deep learning models, with each layer learning to transform its input data into a slightly more abstract and composite representation.

Definition: A type of activation function used in neural networks, which is similar to a rectified linear unit (ReLU), but it allows a small, positive gradient when the unit is inactive and output is otherwise zero.

Definition: A hyperparameter that controls how much to change the model in response to the estimated error each time the model weights are updated. Choosing the right learning rate is crucial for good training performance.

Definition: A mathematical method used in regression analysis that minimises the sum of the squares of the residuals (the difference between observed values and those predicted by the model), resulting in a line of best fit.

Definition: In machine learning, leveraging refers to using complex algorithms or data strategies to gain insights from data. It can also refer to enhancing the capabilities of a model with additional data or techniques.

Definition: The process of converting a sequence of characters into a sequence of tokens. This is typically one of the first phases of a compiler, which is important for natural language processing applications in AI.

Definition: In data mining, lift is a measure of the performance of a targeting model (or classifier) at predicting or classifying cases as having an enhanced response (with respect to the population as a whole), measured against a random choice targeting model. Lift indicates how much better one can expect to do with the predictive model comparing to without a model.