Pretraining

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 parameter-efficient method that prepends trainable continuous vectors to the input of each transformer layer, guiding model behaviour without altering base parameters.

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

A memory optimisation that trades computation for memory by recomputing intermediate activations during the backward pass instead of storing them all during the forward pass.

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.

An activation function that converts a vector of numbers into a probability distribution, commonly used in multi-class classification.

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

A technique that caps gradient values during training to prevent the exploding gradient problem.