03537nam 22005293 450 991101910930332120230808080254.09781119853923111985392397811198539091119853907(MiAaPQ)EBC30676468(Au-PeEL)EBL30676468(CKB)27935709500041(Exl-AI)30676468(OCoLC)1393303366(EXLCZ)992793570950004120230808d2023 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierAdvances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning1st ed.Newark :John Wiley & Sons, Incorporated,2023.©2023.1 online resource (595 pages)IEEE Press Series on Electromagnetic Wave Theory SeriesPrint version: Campbell, Sawyer D. Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning Newark : John Wiley & Sons, Incorporated,c2023 9781119853893 Cover -- Title Page -- Copyright -- Contents -- About the Editors -- List of Contributors -- Preface -- Part I Introduction to AI‐Based Regression and Classification -- Chapter 1 Introduction to Neural Networks -- 1.1 Taxonomy -- 1.1.1 Supervised Versus Unsupervised Learning -- 1.1.2 Regression Versus Classification -- 1.1.3 Training, Validation, and Test Sets -- 1.2 Linear Regression -- 1.2.1 Objective Functions -- 1.2.2 Stochastic Gradient Descent -- 1.3 Logistic Classification -- 1.4 Regularization -- 1.5 Neural Networks -- 1.6 Convolutional Neural Networks -- 1.6.1 Convolutional Layers -- 1.6.2 Pooling Layers -- 1.6.3 Highway Connections -- 1.6.4 Recurrent Layers -- 1.7 Conclusion -- References -- Chapter 2 Overview of Recent Advancements in Deep Learning and Artificial Intelligence -- 2.1 Deep Learning -- 2.1.1 Supervised Learning -- 2.1.1.1 Conventional ApproachesGenerated by AI.This book explores advancements in artificial intelligence and deep learning, particularly focusing on applications in electrical engineering and wave theory. It covers topics such as AI-based regression and classification, neural networks, and the integration of machine learning in designing metasurfaces and electromagnetic applications. Edited by Sawyer D. Campbell and Douglas H. Werner, the book serves as a comprehensive resource for researchers and professionals in the field, aiming to enhance understanding of the latest technologies in AI and deep learning. The intended audience includes academics, engineers, and advanced students interested in AI applications in engineering.Generated by AI.IEEE Press Series on Electromagnetic Wave Theory SeriesArtificial intelligenceGenerated by AIDeep learning (Machine learning)Generated by AIArtificial intelligenceDeep learning (Machine learning)537.0285/63Campbell Sawyer D1837557Werner Douglas H492224MiAaPQMiAaPQMiAaPQBOOK9911019109303321Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning4416305UNINA