simple network with only one hidden neuron and one output this is probably the simplest neural network that you can create you can't really get smaller than this computing the gradient of our loss with respect to W to here which is that second way between the hidden state and our output can tell us how much a small change in W 2 will impact our loss so that's what the gradient tells us right if we change W 2 in the differential different like a very minor manner how does our loss change does it go up or down how does it change and by how much