Embedding layer enables us to convert each word into a fixed length vector of defined size. The resultant vector is a dense one with having real values instead of just 0's and 1's. The fixed length of word vectors helps us to represent words in a better way along with reduced dimensions.

What is word embedding used for?

A word embedding is a learned representation for text where words that have the same meaning have a similar representation. It is this approach to representing words and documents that may be considered one of the key breakthroughs of deep learning on challenging natural language processing problems.

What is the difference between embedding layer and dense layer?

An embedding layer is faster, because it is essentially the equivalent of a dense layer that makes simplifying assumptions. A Dense layer will treat these like actual weights with which to perform matrix multiplication.