Architecture

Cross-Entropy Loss

Definition

The most common training objective for classification and language modelling tasks, measuring the difference between predicted probability distributions and true labels. Minimising cross-entropy loss during training drives the model to assign high probability to correct outputs.

Related Terms

Softmax

A mathematical function that converts a vector of raw logits into a probability distribution summing to 1. Softmax is applied at the output of classifiers and language models to produce token probabilities used in sampling and decoding.

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.

Supervised Learning

A machine learning approach where the model is trained on labelled input-output pairs. The model learns to map inputs to correct outputs, enabling predictions on new, unseen data. Most commercial ML models in production today are supervised.

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

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