Long Short-Term Memory

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.

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.

A problem in deep networks where gradients become extremely small during backpropagation, preventing earlier layers from learning.

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

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

A neural network designed to operate on graph-structured data, learning representations of nodes, edges, and entire graphs.

A transformer architecture adapted for image recognition that divides images into patches and processes them as sequences, rivalling convolutional networks in visual tasks.

An attention mechanism that runs multiple attention operations in parallel, capturing different types of relationships.

A mathematical function applied to neural network outputs to introduce non-linearity, enabling the learning of complex patterns.

A neural network layer that reduces spatial dimensions by aggregating values, commonly using max or average operations.