What is an embedding layer and how is it used in deep learning models?

An embedding layer is a trainable layer in a neural network that converts categorical data, such as words or tokens, into dense vector representations of fixed size. It maps discrete variables into continuous vector space, enabling the model to capture semantic relationships and similarities between items. In deep learning models, embedding layers are commonly used in natural language processing tasks. By embedding words into low-dimensional spaces, they allow models to understand and process complex inputs more effectively while reducing dimensionality and computational load.