Gradient Checkpointing

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.

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 where gradients grow exponentially during backpropagation, causing unstable weight updates and training failure.

The strategy for setting initial parameter values in a neural network before training begins.

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

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

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

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.