What is a training?
The process of determining the ideal parameters (weights and biases) comprising a model.
training explained in plain English
The process of determining the ideal parameters (weights and biases) comprising a model. During training, a system reads in examples and gradually adjusts parameters. Training uses each example anywhere from a few times to billions of times. See Supervised Learning in the Introduction to ML course for more information.
Example
Practitioners refer to training when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.
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A statistical way of comparing two (or more) techniques—the A and the B.
- ablation
A technique for evaluating the importance of a feature or component by temporarily removing it from a model.
- accuracy
The number of correct classification predictions divided by the total number of predictions.
- activation function
A function that enables neural networks to learn nonlinear (complex) relationships between features and the label.
- active learning
A training approach in which the algorithm chooses some of the data it learns from.
- adaptation
Synonym for tuning or fine-tuning.
- agglomerative clustering
See hierarchical clustering.
- anomaly detection
The process of identifying outliers.
- area under the PR curve
See PR AUC (Area under the PR Curve).
- area under the ROC curve
See AUC (Area under the ROC curve).