Multi-Head Attention

Self-Attention

An attention mechanism where each element in a sequence attends to all other elements to compute its representation.

Residual Network

A deep neural network architecture using skip connections that allow gradients to flow directly through layers, enabling very deep networks.

Layer Normalisation

A normalisation technique that normalises across the features of each individual sample rather than across the batch.

Dropout

A regularisation technique that randomly deactivates neurons during training to prevent co-adaptation and reduce overfitting.

Activation Function

A mathematical function applied to neural network outputs to introduce non-linearity, enabling the learning of complex patterns.

ReLU

Rectified Linear Unit — an activation function that outputs the input directly if positive, otherwise outputs zero.

Sigmoid Function

An activation function that maps input values to a range between 0 and 1, useful for binary classification outputs.

Softmax Function

An activation function that converts a vector of numbers into a probability distribution, commonly used in multi-class classification.

Positional Encoding

A technique that injects information about the position of tokens in a sequence into transformer architectures.

Gradient Clipping

A technique that caps gradient values during training to prevent the exploding gradient problem.

Mixed Precision Training

Training neural networks using both 16-bit and 32-bit floating-point arithmetic to speed up computation while maintaining accuracy.

Rotary Positional Encoding

A position encoding method that encodes absolute position with a rotation matrix and naturally incorporates relative position information into attention computations.

A regularisation technique that penalises large model weights during training by adding a fraction of the weight magnitude to the loss function, preventing overfitting.

A simplified variant of LSTM that combines the forget and input gates into a single update gate.

A neural network architecture that groups neurons into capsules to better capture spatial hierarchies and part-whole relationships.

A computing system inspired by biological neural networks, consisting of interconnected nodes that process information in layers.

A memory optimisation that trades computation for memory by recomputing intermediate activations during the backward pass instead of storing them all during the forward pass.

A neural network layer that applies learnable filters across input data to detect local patterns and features.

A form of model parallelism that splits neural network layers across devices and pipelines micro-batches through stages, maximising hardware utilisation during training.

A neural network architecture where connections between nodes form directed cycles, enabling processing of sequential data.