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Artificial Intelligence & Machine Learning vocabulary · Model Training

Hyperparameter

Meaning in artificial intelligence & machine learning, with examples

/ˌhaɪpərpəˈræmɪtər/nounCore term

In artificial intelligence & machine learning, hyperparameter means: A setting chosen by engineers before training rather than learned from data, such as the learning rate, the batch size or the number of layers.

In Turkish: hiperparametre

How it is used

You tune, search and log hyperparameters. Hyperparameter tuning or hyperparameter search means trying many combinations and keeping the best one by validation score. In code and chat people often write 'hparams'.

Example sentences

  1. We ran a hyperparameter search over 40 configurations overnight.

  2. Please log all hyperparameters with each run so we can reproduce the results.

Common collocations

  • hyperparameter tuning
  • hyperparameter search
  • tune hyperparameters
  • default hyperparameters
  • parameterA number inside a model, such as a weight or a bias, that is learned from data during training. Model size is often given as the number of parameters.
  • learning rateA hyperparameter that controls how big a step the model takes each time it updates its parameters. Too high makes training unstable; too low makes it very slow.
  • batch sizeThe number of training examples a model processes together before it updates its parameters once.
  • validation setA part of the data kept out of training and used during development to compare models, tune settings and decide when to stop training.

This word in other fields

The same English word can have a different meaning in another field:

More terms: Model Training

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