NeuralN: A Basic FeedForward Neural Network

Abstract

This demonstrates a basic neural network taking data from two generic lists of input arrays. The first generic list contains input data. The second contains output decisions. Lines 28 - 103 take care of initializing weights, biases, and holder variables for those weights and biases to hold updates from gradient optimizers before they can be assigned to their respective weights and biases. Optimization happens to the error function defined on line 237. Gradient opimization is done in pieces.