<p>In short, a dropout layer ignores a set of neurons (randomly) as one can see in the picture below. This normally is used to prevent the net from overfitting. </p>

<p>The Dense layer is a normal fully connected layer in a neuronal network. <a href="https://i.stack.imgur.com/c2O5w.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/c2O5w.png" alt="enter image description here"></a></p>

<p>Resources:</p>

<p><a href="https://arxiv.org/abs/1207.0580" rel="nofollow noreferrer">Improving neural networks by preventing co-adaptation of feature detectors</a></p>

<p><a href="http://jmlr.org/papers/v15/srivastava14a.html" rel="nofollow noreferrer">Dropout: A Simple Way to Prevent Neural Networks from Overfitting </a></p>

<p><a href="https://machinelearningmastery.com/dropout-for-regularizing-deep-neural-networks/" rel="nofollow noreferrer">A Gentle Introduction to Dropout for Regularizing Deep Neural Networks</a></p>

<p>Let me know if you need a more precise explanation.</p>
In Keras, what is a "dense" and a "dropout" layer?
<p>what is the main difference between dense and dropout layers in Keras </p>