Architecture

Dropout

Definition

A regularisation technique that randomly sets a fraction of neuron activations to zero during training, forcing the network to learn redundant representations and preventing overfitting. Dropout is widely used in both vision and language models.

Related Terms

Deep Learning

A subset of machine learning based on artificial neural networks with multiple layers. Deep learning can learn complex patterns from large amounts of data and is particularly effective for image recognition, speech processing, and natural language understanding.

Training (AI)

The process of exposing a model to large volumes of labelled or unlabelled data and adjusting its internal parameters to minimise prediction errors. Training is computationally intensive, often performed on GPU clusters, and constitutes the major cost in foundation model development.

Batch Normalization

A technique that normalises activations across the batch dimension during training, accelerating convergence and reducing sensitivity to hyperparameters. Though largely superseded by layer norm in transformers, batch norm remains prevalent in convolutional vision architectures.

Knowing the Terms Is Step One. Applying Them Is Step Two.

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