title: Neural Networks: A step-by-step breakdown of the Backpropagation phase?
body: <p>I have to design an animated visual representation of a neural network that is functional (i.e. with UI that allows you to tweak values etc). The primary goal with it is to help people visualize how and when the different math operations are performed in a slow-motion, real-time animation.  I have the visuals set up along with the UI that allows you to tweak values and change the layout of the neurons, as well as the visualizations for the feed forward stage, but since I don’t actually specialize in neural networks at all, I’m having trouble figuring out the best way to visualize the back propagation phase- mainly due to the fact that I’ve had trouble figuring out the exact order of operations during this stage.</p>
  <p>The visualization starts by firing neurons forward, and then after that chain of fired neurons reach an output, an animation shows the difference between the actual and predicted values, and from this point I want to visualize the network firing backwards while demonstrating the math that is taking place. But this is where I really am unsure about what exactly is supposed to happen.</p>
  <p>So my questions are:</p>
  <ul>
  <li>Which weights are actually adjusted in the backpropagation phase? Are all of the weights adjusted throughout the entire neural network, or just the ones that fired during the forward pass?</li>
  <li>Are all of the weights in each hidden layer adjusted by the same amount during this phase, or are they adjusted by a value that is offset by their current weight, or some other value? It didn't really make sense to me that they would all be adjusted by the same amount, without being offset by a curve or something of the sort.</li>
  </ul>
  <p>I’ve found a lot of great information about the feed forward phase online, but when it comes to the backpropagation phase I’ve had a lot of trouble finding any good visualizations/explanations about what is actually happening during this phase.</p>