get the code from here
This is the second post of the series describing backpropagation algorithm applied to feed forward neural network training. In the last post we described what neural network is and we concluded it is a parametrized mathematical function. We implemented neural network initialization (meaning creating a proper entity representing the network – not weight initialization) and inference routine but we never made any connection to the data itself. In this post we will make such a connection and we will express meaning of parametrization “goodness” in terms of training data and network output.