Key-Value Cache

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.

An attention mechanism where each element in a sequence attends to all other elements to compute its representation.

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

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

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

A skip connection that adds a layer's input directly to its output, enabling gradient flow through deep networks and allowing training of architectures with hundreds of layers.

Training neural networks using both 16-bit and 32-bit floating-point arithmetic to speed up computation while maintaining accuracy.

An individual attention computation within a multi-head attention layer that learns to focus on different aspects of the input, with outputs concatenated for richer representations.

Token

A digital asset created and managed on a blockchain, representing value, utility, or ownership rights.