Attention Mechanism

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.

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.

Methods for adapting large pretrained models to new tasks by only updating a small fraction of their parameters.

A generative model that learns to reverse a gradual noising process, generating high-quality samples from random noise.

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

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

A neural network shortcut that allows the output of one layer to bypass intermediate layers and be added to a later layer's output.

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

An IO-aware attention algorithm that reduces memory reads and writes by tiling the attention computation, enabling faster training of long-context transformer models.

A framework where two neural networks compete — a generator creates synthetic data while a discriminator evaluates its authenticity.