The Embedding layer is defined as the first hidden layer of a network. ... input_length: This is the length of input sequences, as you would define for any input layer of a Keras model. For example, if all of your input documents are comprised of 1000 words, this would be 1000.

What is the use of embedding?

An embedding is a relatively low-dimensional space into which you can translate high-dimensional vectors. Embeddings make it easier to do machine learning on large inputs like sparse vectors representing words.

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.