This project use backpropagation to solve the classic XOR problem. It uses (but you can change their number): 2 neurons in the Input layer, 3 neurons in the Hidden layer 1 neuron in the Output layer By default it uses a bias neuron in the Input and Hidden layers but, setting the Bias slider variable to zero, you can run the network with no biases. I experienced that with bias neurons the network complete its training after a certain period of time and I saved the weights so you may start in a trained environment; with no biases the network seems to be stuck in an infinite loop (a local minimum?). Meaning of values in slider variable Start: 0 - use random weights 1 - use trained weights 2 - use previous weights Slider variable 'eta' is the learning rate: more high the faster the algorithm runs but to fine tune, use a low number. The % of success is calculated on the last 100 attempts. (Thanks to PutneyCat and his Basic Neural Network for the idea) Reference documentation: https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/
