Shallow and Deep Learning Principles: Scientific, Philosophical, and Logical Perspectives

This book provides a comprehensive overview of Artificial Neural Networks (ANN) and their ability to predict outcomes using deep and shallow learning principles.

The author explains the importance of developing an architecture that does not model mathematical rules but rather focuses on action and response variables that control events and reactions within it.

The book also covers the reasons and necessity of each ANN model, comparing them to previous methods and philosophical-logical rules.

With a detailed explanation of the implementation of at least three layers consisting of cells, this book is an essential resource for anyone interested in understanding the power of ANNs and their ability to predict outcomes with accuracy.