title: ReLu and Dropout in CNN
body: <p>I am studying Convolutional Neural Networks. I am confused about some layers in CNN.</p>
  
  <p>Regarding ReLu... I just know that it is the sum of an infinite logistic function, but ReLu doesn't connect to any upper layers. Why do we need ReLu, and how does it work?</p>
  
  <p>Regarding Dropout... How does dropout work? I listened to a video talk from G. Hinton. He said there is a strategy which just ignores half of the nodes, randomly, when training the weights, and halves the weight when predicting. He says it was inspired from random forests and works exactly the same as computing the geometric mean of these randomly trained models. </p>
  
  <p>Is this strategy the same as dropout?</p>
  
  <p>Can someone help me to solve this?</p>