Representation 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 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.

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

The process of adapting a pre-trained model to a specific task by continuing training on a smaller task-specific dataset, transferring learned representations to new domains.

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

A mathematical function applied to neural network outputs to introduce non-linearity, enabling the learning of complex patterns.

A neural network shortcut that allows the output of one layer to bypass intermediate layers and be added to a later layer's output.

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