An Adaptive Transcursive Algorithm for Depth Estimation in Deep Learning Networks

Abstract

Estimation of depth in a Neural Network (NN) or Artificial Neural Network (ANN) is an integral as well as complicated process. In this article, we propose a way of using the transformation of functions combined with recursive nature to have an adaptive, transcursive algorithm to represent the backpropagation concept used in deep learning for a Multilayer Perceptron Network. Each function can be used to represent a hidden layer used in the neural network and they can be made to handle a complex part of the processing. Whenever an undesirable output occurs, we transform (modify) the functions until a desirable output is obtained. We have an algorithm that uses the transcursive model to create an interpretation of the concept of deep learning using multilayer perceptron network (MPN).