Convolutional Neural Network

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.

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.

Embedding

A learned dense vector representation of discrete data (like words or categories) in a continuous vector space.

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

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

A selective state space model that achieves transformer-level performance with linear-time complexity by incorporating input-dependent selection mechanisms into the recurrence.

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

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

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.

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