title: How would a multiple output classification neural network work?
body: <p>I currently understand and made a simple neural network which solves the XOR problem. I want to make a neural network for digit recognition. I know using MNIST data I would need 784 input neurons, 15 hidden neurons and 10 output neurons (0-9). </p>
  
  <p>However, I don’t understand how the network would be trained and how feed forward would work with multiple output neurons. </p>
  
  <p>For example, if the input was the pixels for the digit 3, how would the network determine which output neuron is picked and when training, how would the network know which neuron should be associated with the target value. </p>
  
  <p>Any help would be appreciated. </p>