title: What is the difference between an Embedding Layer and a Dense Layer?
body: <p>The docs for an <a href="https://keras.io/layers/embeddings/" rel="noreferrer">Embedding Layer</a> in Keras say:</p>
  
  <blockquote>
    <p>Turns positive integers (indexes) into dense vectors of fixed size. eg. <code>[[4], [20]]</code> -> <code>[[0.25, 0.1], [0.6, -0.2]]</code></p>
  </blockquote>
  
  <p>I believe this could also be achieved by encoding the inputs as one-hot vectors of length <code>vocabulary_size</code>, and feeding them into a <a href="https://keras.io/layers/core/#dense" rel="noreferrer">Dense Layer</a>.</p>
  
  <p>Is an Embedding Layer merely a convenience for this two-step process, or is something fancier going on under the hood?</p>