Architecture

Batch Normalization

Définition

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 Though largely superseded by layer norm in transformers, batch norm remains prevalent in convolutional vision architectures.

Termes Connexes

Layer Normalization

A technique that normalises activations across the feature dimension within a single layer, stabilising training of deep networks. Layer norm is standard in transformer architectures, enabling effective training at billion-parameter scale.

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.

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