Data Analytics Made Accessible

by Anil Maheshwari

A neural network is a series of neurons that receive inputs from other neurons. They do a weighted summation function of all the inputs, using different weights (or importance) for each input. The weighted sum is then transformed into an output value using a transfer function. Learning in ANN occurs when the various processing elements in the neural network adjust the underlying relationship (weights, transfer function, etc) between input and outputs, in response to the feedback on their predictions. If the prediction made was correct, then the weights would remain the same, but if the prediction was incorrect, then the parameter values would change. The Transformation (Transfer) Function is any function suitable for the task at hand. The transfer function for ANNs is usually a non-linear sigmoid function. Thus, if the normalized computed value is less than some value (say 0.5) then the output value will be zero. If the computed value is at the cut-off threshold, then the output value will be a 1. It could be a nonlinear hyperbolic function in which the output is either a -1 or a 1. Many other functions could be designed for any or all of the processing elements. Thus, in a neural network, every processing element can potentially have a different number of input values, a different set of weights for those inputs, and a different transformation function. Those values support and compensate for one another until the neural network as a whole learns to provide the correct ou...