tion function for the small neural network pictured in figure - the full formula for computing in the case of regression would be (when using tanh nonlinearity) [ tanh( [ [ [ [ [ [ [ [ ] [ tanh( [ [ [ [ [ [ [ [ ] [ tanh( [ [ [ [ [ [ [ [ ] [ [ [ [ [ [ herew are the weights between the input and the hidden layer hand are the weights between the hidden layer and the output the weights and are learned from datax are the input featuresy is the computed outputand are intermediate computations an important parameter that needs to be set by the user is the number of nodes in the hidden layer this can be as small as for very small or simple datasets and as big as , for very complex data it is also possible to add additional hidden layersas shown in figure - supervised machine learning algorithms