Transfer learning is one of the most important technologies in the era of artificial intelligence and deep learning. It seeks to leverage existing knowledge by transferring it to another, new domain. Over the years, a number of relevant topics have attracted the interest of the research and application community: transfer learning, pre-training and fine-tuning, domain adaptation, domain generalization, and meta-learning. This book offers a comprehensive tutorial on an overview of transfer learning, introducing new researchers in this area to both classic and more recent algorithms. Most importantly, it takes a “student’s” perspective to introduce all the concepts, theories, algorithms, and applications, allowing readers to quickly and easily enter this area. Accompanying the book, detailed code implementations are provided to better illustrate the core ideas of several important algorithms, presenting good examples for practice.
Direction-of-Arrival (DOA) estimation concerns the estimation of direction finding signals in the form of electromagnetic or acoustic waves, impinging on a sensor or antenna array. DOA estimation is used for locating and tracking signal sources in both civilian and military applications. This authoritative volume provides an overview and performance analysis of the basic DOA algorithms, including comparisons between the various types.The book offers you a detailed understanding of the arrays pertinent to DOA finding, and presents a detailed illustration of the ESPRIT-based DOA algorithms complete with their performance assessments. From antennas and array receiving systems, to advanced topics on DOA estimation, this book serves as a one-stop resource for professionals and students. Nearly 100 illustrations and more than 281 equations support key topics throughout.
The quantum groups of finite and affine type $A$ admit geometric realizations in terms of partial flag varieties of finite and affine type $A$. Recently, the quantum group associated to partial flag varieties of finite type $B/C$ is shown to be a coideal subalgebra of the quantum group of finite type $A$.
Transfer learning is one of the most important technologies in the era of artificial intelligence and deep learning. It seeks to leverage existing knowledge by transferring it to another, new domain. Over the years, a number of relevant topics have attracted the interest of the research and application community: transfer learning, pre-training and fine-tuning, domain adaptation, domain generalization, and meta-learning. This book offers a comprehensive tutorial on an overview of transfer learning, introducing new researchers in this area to both classic and more recent algorithms. Most importantly, it takes a “student’s” perspective to introduce all the concepts, theories, algorithms, and applications, allowing readers to quickly and easily enter this area. Accompanying the book, detailed code implementations are provided to better illustrate the core ideas of several important algorithms, presenting good examples for practice.
Direction-of-Arrival (DOA) estimation concerns the estimation of direction finding signals in the form of electromagnetic or acoustic waves, impinging on a sensor or antenna array. DOA estimation is used for locating and tracking signal sources in both civilian and military applications. This authoritative volume provides an overview and performance analysis of the basic DOA algorithms, including comparisons between the various types.The book offers you a detailed understanding of the arrays pertinent to DOA finding, and presents a detailed illustration of the ESPRIT-based DOA algorithms complete with their performance assessments. From antennas and array receiving systems, to advanced topics on DOA estimation, this book serves as a one-stop resource for professionals and students. Nearly 100 illustrations and more than 281 equations support key topics throughout.
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