Variational Autoencoder

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.

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.

Small trainable modules inserted between frozen transformer layers that enable task-specific adaptation without modifying the original model weights.

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

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

A model compression technique where a smaller student model learns to mimic the behaviour of a larger teacher model.

A sequence modelling architecture based on continuous-time dynamical systems that processes long sequences with linear complexity, offering an alternative to attention-based transformers.

Methods for adapting large pretrained models to new tasks by only updating a small fraction of their parameters.

A generative model that learns to reverse a gradual noising process, generating high-quality samples from random noise.

A distributed training strategy that replicates the model across multiple devices and divides training data into batches processed simultaneously, synchronising gradients after each step.