Batch Normalisation

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.

Embedding

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

A neural network layer that applies learnable filters across input data to detect local patterns and features.

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

An architecture where different specialised sub-networks (experts) are selectively activated based on the input.

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

The initial phase of training a deep learning model on a large unlabelled corpus using self-supervised objectives, establishing general-purpose representations for downstream adaptation.

A regularisation technique that randomly deactivates neurons during training to prevent co-adaptation and reduce overfitting.

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

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