Deep Learning

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.

Embedding

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

An individual attention computation within a multi-head attention layer that learns to focus on different aspects of the input, with outputs concatenated for richer representations.

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

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

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.

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

A problem where gradients grow exponentially during backpropagation, causing unstable weight updates and training failure.

Machine Learning

A subset of AI that enables systems to automatically learn and improve from experience without being explicitly programmed.