Gradient Descent

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.

Stochastic Gradient Descent

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

Adam Optimiser

An adaptive learning rate optimisation algorithm combining momentum and RMSProp for efficient deep learning training.

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.

A situation where the distribution of classes in a dataset is significantly skewed, with some classes vastly outnumbering others.

Uniform Manifold Approximation and Projection — a dimensionality reduction technique for visualisation and general non-linear reduction.

The infrastructure and processes for deploying trained machine learning models to production environments for real-time predictions.

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

A machine learning approach where a model is simultaneously trained on multiple related tasks to improve generalisation.

A dimensionality reduction technique that transforms data into orthogonal components ordered by the amount of variance they explain.

A technique that decomposes a matrix into constituent matrices, widely used in recommendation systems and dimensionality reduction.

A subset of the training data used to compute a gradient update during stochastic gradient descent.