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Artículo: AMZ-B0G3G2T7HW

Complex Analysis and Neural Networks Theory Models & Applications VOL-2 (AI & New Age Math)

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Complex Analysis and Neural Networks: Theory, Models and Applications By Anshuman MishraIn an age where artificial intelligence is transforming every industry, from communication networks to medical diagnostics, from autonomous systems to next-generation robotics, the demand for deeper, transparent, and more mathematically grounded models has never been higher. While the world talks endlessly about deep learning, the real breakthrough for the next decade lies in complex-valued deep learning, a rapidly emerging, high-performance, mathematically elegant field powered by the remarkable machinery of Complex Analysis.The book you hold in your hands, Complex Analysis and Neural Networks: Theory, Models and Applications, is not merely another textbook. It is a power-packed bridge between classical mathematics and cutting-edge AI research. Written in an accessible yet deeply insightful style by Anshuman Mishra, this work brings to life one of the most powerful ideas in modern computation: using the geometry, smoothness, analyticity, and stability principles of complex numbers to build superior neural networks.This book is for three kinds of readers, and each will gain something transformative.Students will find a solid and understandable introduction to complex analysis, neural networks, and their revolutionary intersection.Researchers will explore stability theory, new activation functions, complex gradient dynamics, complex-valued neural networks, and state-of-the-art signal processing architectures.Developers and engineers will gain practical tools to build safer, stable, and more powerful neural systems for real-world applications in imaging, audio, radar, finance, medical decision-making, wireless communication, and autonomous intelligence.Why This Book MattersWe live in a world generating trillions of signals every second, including audio waves, electromagnetic signals, MRI patterns, radar pulses, seismic vibrations, financial trends, and biological oscillations. Nearly all of these signals are fundamentally complex-valued. Yet traditional neural networks operate only on real numbers.This mismatch is one of the biggest hidden limitations in modern artificial intelligence.Complex-valued neural networks remove this limitation. They naturally capture phase information, amplitude variations, oscillatory behavior, complex frequency components, holomorphic smoothness, and stability conditions through analyticity.This book shows how and why complex numbers revolutionize neural networks in a way that real-valued models simply cannot.A Revolutionary Blend of Mathematics and Artificial IntelligenceUnlike many books that treat mathematics and neural networks as separate worlds, this text unifies them into one coherent and powerful framework. You will see how the Cauchy–Riemann equations frame activation stability, how conformal mappings influence neural transformations, how residue calculus empowers advanced signal models, and how Euler’s formula explains oscillatory neural behavior.The book demonstrates how holomorphic activation functions offer superior regularization, how complex-domain gradients create more stable learning paths, and how Laplace and Fourier transforms connect directly to neural network architecture. It also shows why complex-valued recurrent neural networks outperform classical recurrent models in time-series analysis, how complex convolution improves radar and sonar systems, and why complex Transformers excel in communication and signal-processing domains.Every chapter has been written to ensure that you do not merely learn concepts, but clearly see how mathematics becomes a true computational engine.

$156,49

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$156,49

55% OFF

$71,13

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