Deep Learning Neural Networks - BigR.io

Neural Networks can be simply is characterized as a construct that implements regression or classification by applying nonlinear transformation to linear combinations of raw input features. What makes it seem magical to many is that it can be made to learn by a mechanism called back propagation where the desired outcome (aka label) is compared to the current output, and the error (difference) is fed back into the neuron layers, resulting in self-adjustment of weighting coefficients and biases to drive down the error value. In short, the Neural Network learns from its mistakes. Neural Networks have evolved a long way since their early form of single layer perceptron, which could not implement a simple exclusive OR function. Today's Deep Learning Neural Network consists of multiple layers, and is extraordinarily expressive with its large selections of transfers function.