AI glossary · Machine learning
Training data
Training data is the set of examples, such as text, images or past records, that an AI model learns from. Its quality and range shape what the model can do.
Also called training set
In more detail
A model can only learn what its training data shows. Gaps lead to blind spots, errors in the data become errors in the model, and unfair patterns can turn into bias.
Language models are trained on huge amounts of text from the web, books and licensed sources. Questions about where that data comes from are at the centre of debates about copyright and privacy.
Related terms
DatasetA dataset is an organised collection of data, such as a table of house prices or a folder of labelled photos, used to train or test AI models.AI biasAI bias is when an AI system produces results that are unfair to certain groups of people, usually because of skewed training data or design choices.Pre-trainingPre-training is the first, biggest stage of training an AI model, where it learns general patterns from a huge dataset before being adapted for specific uses.Synthetic dataSynthetic data is artificial data generated by computers, often by AI, to train or test models when real data is scarce, expensive or private.Data augmentationData augmentation creates extra training examples by changing existing ones, such as flipping, cropping or recolouring photos, to help a model generalise.
Browse related tools
AI tools listed on BroUseAI in these categories.