Contrastive Learning

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

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

A framework where two neural networks compete — a generator creates synthetic data while a discriminator evaluates its authenticity.

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

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

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

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

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

Supervised Learning

A machine learning paradigm where models are trained on labelled data, learning to map inputs to known outputs.

Self-Supervised Learning

A learning paradigm where models generate their own supervisory signals from unlabelled data through pretext tasks.