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The book begins by introducing the concept of multifractality and explaining how modern computational systems differ from traditional linear systems. It discusses how internet traffic, neural networks, cloud infrastructures, distributed databases, and Artificial Intelligence systems generate highly irregular and adaptive computational patterns across multiple scales.The mathematical foundations of multifractal theory are developed through detailed discussions on scaling laws, power-law relationships, fractal dimensions, and multifractal spectra. These mathematical tools provide a framework for analyzing nonlinear computational complexity and scale-dependent system behavior. A major focus of the book is the study of Artificial Neural Networks and deep learning architectures as multifractal systems. The book explains how hierarchical learning,nonlinear activations, adaptive optimization, and dynamic feature extraction producemulti-scale computational behavior similar to natural complex systems.
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