Shallow and Deep Learning Principles

This book discusses artificial neural networks (ANN) and their ability to predict outcomes using deep and shallow learning principles. The author first describes ANN implementation, which consists of at least three layers: an input layer, an output layer, and a hidden (intermediate) layer. The author states that it is necessary to develop an architecture that models not mathematical rules but rather the action and response variables that control the event and the reactions that may occur within it. The book explains the reasons and necessity of each ANN model, considering the similarities to previous methods and the philosophical and logical rules.