Weight Initialisation

Deep Learning

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

Neural Network

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

Convolutional Neural Network

A deep learning architecture designed for processing structured grid data like images, using convolutional filters to detect features.

Recurrent Neural Network

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

Long Short-Term Memory

A recurrent neural network architecture designed to learn long-term dependencies by using gating mechanisms to control information flow.

Gated Recurrent Unit

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

Transformer

A neural network architecture based entirely on attention mechanisms, eliminating recurrence and enabling parallel processing of sequences.

Attention Mechanism

A neural network component that learns to focus on relevant parts of the input when producing each element of the output.

Encoder-Decoder Architecture

A neural network design where an encoder processes input into a fixed representation and a decoder generates output from it.

Autoencoder

A neural network trained to encode input data into a compressed representation and then decode it back to reconstruct the original.

Variational Autoencoder

A generative model that learns a probabilistic latent space representation, enabling generation of new data samples.

Batch Normalisation

A technique that normalises layer inputs during training to stabilise and accelerate deep neural network learning.

An attention mechanism that runs multiple attention operations in parallel, capturing different types of relationships.

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

A selective state space model that achieves transformer-level performance with linear-time complexity by incorporating input-dependent selection mechanisms into the recurrence.

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

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

A transformer architecture adapted for image recognition that divides images into patches and processes them as sequences, rivalling convolutional networks in visual tasks.

A learned dense vector representation of discrete data (like words or categories) in a continuous vector space.

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

Strategy

A plan of action designed to achieve a long-term or overall aim, involving resource allocation and competitive positioning.