Vision Transformer

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.

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

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

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

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

Dense vector representations of words where semantically similar words are mapped to nearby points in vector space.

A problem in deep networks where gradients become extremely small during backpropagation, preventing earlier layers from learning.

An optimisation in autoregressive transformer inference that stores previously computed key and value tensors to avoid redundant computation during sequential token generation.