Adam Optimiser

Ridge Regression

A regularised regression technique that adds an L2 penalty term to prevent overfitting by constraining coefficient magnitudes.

Elastic Net

A regularisation technique combining L1 and L2 penalties, balancing feature selection and coefficient shrinkage.

Cross-Validation

A resampling technique that partitions data into subsets, training on some and validating on others to assess model generalisation.

Overfitting

When a model learns the training data too well, including noise, resulting in poor performance on unseen data.

Underfitting

When a model is too simple to capture the underlying patterns in the data, resulting in poor performance on both training and test data.

Bias-Variance Tradeoff

The balance between a model's ability to minimise bias (error from assumptions) and variance (sensitivity to training data fluctuations).

Regularisation

Techniques that add constraints or penalties to a model to prevent overfitting and improve generalisation to new data.

Gradient Descent

An optimisation algorithm that iteratively adjusts parameters in the direction of steepest descent of the loss function.

Stochastic Gradient Descent

A variant of gradient descent that updates parameters using a randomly selected subset of training data each iteration.

Learning Rate

A hyperparameter that controls how much model parameters are adjusted with respect to the loss gradient during training.

Loss Function

A mathematical function that measures the difference between predicted outputs and actual target values during model training.

Backpropagation

The algorithm for computing gradients of the loss function with respect to network weights, enabling neural network training.

The tendency of neural networks to completely lose previously learned knowledge when trained on new tasks, a fundamental challenge in continual and multi-task learning.

Training a machine learning model on the entire dataset at once before deployment, as opposed to incremental updates.

The process of identifying and selecting the most relevant input variables for a machine learning model.

A machine learning approach where models discover patterns and structures in data without labelled examples.

Continuous observation of deployed machine learning models to detect performance degradation, data drift, anomalous predictions, and infrastructure issues in production.

A statistical method modelling the relationship between a dependent variable and one or more independent variables using a linear equation.

A simple algorithm that classifies data points based on the majority class of their k closest neighbours in feature space.

The process of adjusting a model's predicted probabilities so they accurately reflect the true likelihood of outcomes, essential for risk-sensitive decision-making.

Deep Learning

A subset of machine learning using neural networks with multiple layers to learn hierarchical representations of data.