1# Library to Find Best eBook for Read
Author : Dawei Dong
Release : 1991
Genre : Electronic dissertations
Kind : eBook
Book Rating : /5 ( reviews)
Book Synopsis Dynamic Properties of Neural Networks by : Dawei Dong
Download or read book Dynamic Properties of Neural Networks written by Dawei Dong. This book was released on 1991. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Andrea Crisanti
Release : 1988
Genre :
Kind : eBook
Book Rating : /5 ( reviews)
Book Synopsis Static and Dynamic Properties of Neural Networks by : Andrea Crisanti
Download or read book Static and Dynamic Properties of Neural Networks written by Andrea Crisanti. This book was released on 1988. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Markos THANOS
Release : 1969
Genre :
Kind : eBook
Book Rating : /5 ( reviews)
Book Synopsis Signaling and Dynamic Properties of Neural Networks by : Markos THANOS
Download or read book Signaling and Dynamic Properties of Neural Networks written by Markos THANOS. This book was released on 1969. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Michel J.A.M. van Putten
Release : 2020-12-18
Genre : Science
Kind : eBook
Book Rating : 848/5 ( reviews)
Book Synopsis Dynamics of Neural Networks by : Michel J.A.M. van Putten
Download or read book Dynamics of Neural Networks written by Michel J.A.M. van Putten. This book was released on 2020-12-18. Available in PDF, EPUB and Kindle. Book excerpt: This book treats essentials from neurophysiology (Hodgkin–Huxley equations, synaptic transmission, prototype networks of neurons) and related mathematical concepts (dimensionality reductions, equilibria, bifurcations, limit cycles and phase plane analysis). This is subsequently applied in a clinical context, focusing on EEG generation, ischaemia, epilepsy and neurostimulation. The book is based on a graduate course taught by clinicians and mathematicians at the Institute of Technical Medicine at the University of Twente. Throughout the text, the author presents examples of neurological disorders in relation to applied mathematics to assist in disclosing various fundamental properties of the clinical reality at hand. Exercises are provided at the end of each chapter; answers are included. Basic knowledge of calculus, linear algebra, differential equations and familiarity with MATLAB or Python is assumed. Also, students should have some understanding of essentials of (clinical) neurophysiology, although most concepts are summarized in the first chapters. The audience includes advanced undergraduate or graduate students in Biomedical Engineering, Technical Medicine and Biology. Applied mathematicians may find pleasure in learning about the neurophysiology and clinic essentials applications. In addition, clinicians with an interest in dynamics of neural networks may find this book useful, too.
Author : Lingfei Wu
Release : 2022-01-03
Genre : Computers
Kind : eBook
Book Rating : 549/5 ( reviews)
Book Synopsis Graph Neural Networks: Foundations, Frontiers, and Applications by : Lingfei Wu
Download or read book Graph Neural Networks: Foundations, Frontiers, and Applications written by Lingfei Wu. This book was released on 2022-01-03. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning models are at the core of artificial intelligence research today. It is well known that deep learning techniques are disruptive for Euclidean data, such as images or sequence data, and not immediately applicable to graph-structured data such as text. This gap has driven a wave of research for deep learning on graphs, including graph representation learning, graph generation, and graph classification. The new neural network architectures on graph-structured data (graph neural networks, GNNs in short) have performed remarkably on these tasks, demonstrated by applications in social networks, bioinformatics, and medical informatics. Despite these successes, GNNs still face many challenges ranging from the foundational methodologies to the theoretical understandings of the power of the graph representation learning. This book provides a comprehensive introduction of GNNs. It first discusses the goals of graph representation learning and then reviews the history, current developments, and future directions of GNNs. The second part presents and reviews fundamental methods and theories concerning GNNs while the third part describes various frontiers that are built on the GNNs. The book concludes with an overview of recent developments in a number of applications using GNNs. This book is suitable for a wide audience including undergraduate and graduate students, postdoctoral researchers, professors and lecturers, as well as industrial and government practitioners who are new to this area or who already have some basic background but want to learn more about advanced and promising techniques and applications.
