decision boundary: The separator between classes learned by a model in a binary class or multi-class classification problems. For example, in the following image representing a binary classification problem, the decision boundary is the frontier between the orange class and the blue class:
deep model: A type of neural network containing multiple hidden layers. Deep models rely on trainable non-linearities.
dense feature: A feature in which most values are non-zero, typically a Tensor of floating-point values. Contrast with sparse feature.
derived feature: Synonym for synthetic feature
discrete feature: A feature with a finite set of possible values. For example, a feature whose values may only be animal, vegetable, or mineral is a discrete (or categorical) feature. Contrast with continuous feature.
dropout regularization: A form of regularization useful in training neural networks. Dropout regularization works by removing a random selection of a fixed number of the units in a network layer for a single gradient step. The more units dropped out, the stronger the regularization. This is analogous to training the network to emulate an exponentially large ensemble of smaller networks.
dynamic model: A model that is trained online in a continuously updating fashion. That is, data is continuously entering the model.
Data Science – Deep Learning Glossary | Ecency
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