*An embedding is a mapping of a discrete — categorical — variable to a vector of continuous numbers.* In the context of neural networks, embeddings are *low-dimensional,* *learned* continuous vector representations of discrete variables. Neural network embeddings are useful because they can *reduce the dimensionality* of categorical variables and *meaningfully represent* categories in the transformed space.