The teaching and learning of linear algebra have evolved significantly over recent decades, underpinned by diverse approaches ranging from theoretical expositions to dynamic, model-based environments.

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction.