Tensor Parallelism

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 technique that caps gradient values during training to prevent the exploding gradient problem.

A technique that injects information about the position of tokens in a sequence into transformer architectures.

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

A deep neural network architecture using skip connections that allow gradients to flow directly through layers, enabling very deep networks.

A sequence modelling architecture based on continuous-time dynamical systems that processes long sequences with linear complexity, offering an alternative to attention-based transformers.

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 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.

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

Strategy

A plan of action designed to achieve a long-term or overall aim, involving resource allocation and competitive positioning.