Glossary

Glossary

Accuracy

Evaluation metrix measuring the fraction of predictions that a classification model got right (Wikipedia).

Activation function

A function (for example, ReLU or sigmoid) that generates a (typically nonlinear) output. Used in neural networks to define the output of a layer (Wikipedia).

AUROC

Area Under the ROC Curve. An aggregate measure of a classifier’ performance across all possible classification thresholds (Wikipedia).

Automatic differentiation (autodiff)

Soon!

Backpropagation

Mathematical technique used to obtain the gradients of the loss function with respect to the weights of a neural network. Applies the chain rule to compute the gradients one layer at a time, iterating backwards from the output layer (Wikipedia).

Batch

A set of examples used in one iteration (that is, one gradient update) during model training. Batch size defines the number of examples in a batch (Wikipedia).

Bias

Soon!

Boosting

Soon!

Broadcasting

Soon!

Confusion matrix

Soon!

Decision boundary

Frontier between the classes learned by a classification model.

Precision

Evaluation matrix measuring the frequency with which a model was correct when predicting the positive class (Wikipedia).

Self-supervised learning

Form of Machine Learning in which labels are automatically generated from the data itself, then a model is supervisely trained on the resulting dataset.