Exploding 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.

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

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

An activation function that maps input values to a range between 0 and 1, useful for binary classification outputs.

A distributed training approach that partitions a model across multiple devices, enabling training of models too large to fit in a single accelerator's memory.

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

A distributed computing strategy that splits individual layer computations across multiple devices by partitioning weight matrices along specific dimensions.

The process of taking a pretrained model and further training it on a smaller, task-specific dataset.

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

Backpropagation

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