Quick and dirty guide: - Use the tanh activation function. - Use many hidden nodes, e.g. 100 or 1000 for complex tasks. - Tune the learning rate. - Add a pe

Quick and dirty guide: - Use the tanh activation function. - Use many hidden nodes, e.g. 100 or 1000 for complex tasks. - Tune the learning rate. - Add a penalty of k*w^2 to each hidden node. Tune the parameter k to minimize the held-out error. - If this isn't working, plot the error on the training set as you train. It should decrease to 0. Read Yann LeCun's efficient backprop work for more tips.