Vanishing Gradient

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.

Low-Rank Adaptation — a parameter-efficient fine-tuning technique that adds trainable low-rank matrices to frozen pretrained weights.

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

A position encoding method that encodes absolute position with a rotation matrix and naturally incorporates relative position information into attention computations.

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

A technique that injects information about the position of tokens in a sequence into transformer architectures.

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.

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

The strategy for setting initial parameter values in a neural network before training begins.

Backpropagation

The algorithm for computing gradients of the loss function with respect to network weights, enabling neural network training.