Last week we discussed the basics of neural networks and deep learning and the basics of automatic differentiation. We looked also at examples on how compute the parameters of a simple network with scalar inputs and ouputs and no or just one hidden layers.
We ended our discussions with the derivation of the equations for a neural network with one hidden layers and two input variables and two hidden nodes but only one output node.