Neural Network

Deep Learning

A subset of machine learning using neural networks with multiple layers to learn hierarchical representations of data.

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.

Embedding

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

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

A neural network layer where every neuron is connected to every neuron in the adjacent layers.

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.

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

A problem where gradients grow exponentially during backpropagation, causing unstable weight updates and training failure.

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

The automatic discovery of data representations needed for feature detection or classification from raw data.

A regularisation technique that penalises large model weights during training by adding a fraction of the weight magnitude to the loss function, preventing overfitting.