ANALYSIS OF DEEP NEURAL NETWORKS USING FOURIER TRANSFORM

Abstract

Deep neural networks (DNNs) have become an important tool in various fields of science and technology due to their ability to recognize complex patterns and find hidden relationships in large volumes of data. However, understanding the inner workings of these models remains a challenge. This thesis examines the application of the Fourier transform to analyze the internal structure and behavior of deep neural networks.