multiple layers in machine learning, input is run through multiple layers of nodes and weights. Input first goes to layer 1, then to layer 2, then to output (assuming 2 layers). With this output, you get a Delta. This Delta is first used to adjust the weights of the nodes in layer 2, then using these new weight values, the new values of layer 1 are calculated and adjusted. This process of going backwards is back propagation.