nondeep

Nondeep is an adjective used primarily in discussions of machine learning and related fields to describe models, architectures, or processes that do not employ deep, hierarchical representations or multiple stacked layers. It is commonly contrasted with deep learning, which emphasizes learning abstract features through many levels of representation.

The term is not standardized in formal literature. It largely appears in informal discourse, blog posts, or niche debates, where speakers seek to distinguish “shallow” or non-hierarchical approaches from deep neural networks. Because of its informal status, nondeep can be ambiguous without additional context; practitioners often prefer more precise descriptors such as shallow, linear, or non-deep learning.

In machine learning, nondeep methods include linear models (such as logistic regression and linear support vector machines), decision trees, k-nearest neighbors, and other shallow classifiers. These approaches typically rely on a limited or explicit feature representation rather than learning complex hierarchical abstractions. Some ensemble methods, like random forests or gradient boosting, combine multiple simple models but are not typically categorized as deep learning; their depth varies and they do not involve deep neural architectures by default.

Limitations and reception: because “nondeep” is informal, it can hamper clear communication. Many researchers favor explicit terminology (shallow vs. deep, linear vs. nonlinear, feature-based vs. learned representations) to avoid ambiguity. Nonetheless, the term is sometimes used to quickly signal a contrast with deep neural networks in discussions of model capabilities, data efficiency, and interpretability.

See also: deep learning, shallow learning, non-deep learning, neural networks.