Neural Network Fundamentals With Graphs Algorithms And Applications

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Neural Network Fundamentals With Graphs Algorithms And Applications

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Author : Nirmal K. Bose
language : en
Publisher: McGraw-Hill Companies
Release Date : 1996

Neural Network Fundamentals With Graphs Algorithms And Applications written by Nirmal K. Bose and has been published by McGraw-Hill Companies this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Computers categories.

Graph Neural Networks Foundations Frontiers And Applications

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Author : Lingfei Wu
language : en
Publisher: Springer Nature
Release Date : 2022-01-03

Graph Neural Networks Foundations Frontiers And Applications written by Lingfei Wu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-03 with Computers categories.

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.

Graph Representation Learning

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Author : William L. Hamilton
language : en
Publisher: Springer Nature
Release Date : 2022-06-01

Graph Representation Learning written by William L. Hamilton and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.

Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.

The Electrical Engineering Handbook Second Edition

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Author : Richard C. Dorf
language : en
Publisher: CRC Press
Release Date : 1997-09-26

The Electrical Engineering Handbook Second Edition written by Richard C. Dorf and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997-09-26 with Technology & Engineering categories.

In 1993, the first edition of The Electrical Engineering Handbook set a new standard for breadth and depth of coverage in an engineering reference work. Now, this classic has been substantially revised and updated to include the latest information on all the important topics in electrical engineering today. Every electrical engineer should have an opportunity to expand his expertise with this definitive guide. In a single volume, this handbook provides a complete reference to answer the questions encountered by practicing engineers in industry, government, or academia. This well-organized book is divided into 12 major sections that encompass the entire field of electrical engineering, including circuits, signal processing, electronics, electromagnetics, electrical effects and devices, and energy, and the emerging trends in the fields of communications, digital devices, computer engineering, systems, and biomedical engineering. A compendium of physical, chemical, material, and mathematical data completes this comprehensive resource. Every major topic is thoroughly covered and every important concept is defined, described, and illustrated. Conceptually challenging but carefully explained articles are equally valuable to the practicing engineer, researchers, and students. A distinguished advisory board and contributors including many of the leading authors, professors, and researchers in the field today assist noted author and professor Richard Dorf in offering complete coverage of this rapidly expanding field. No other single volume available today offers this combination of broad coverage and depth of exploration of the topics. The Electrical Engineering Handbook will be an invaluable resource for electrical engineers for years to come.

Neural Networks And Deep Learning

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Author : Charu C. Aggarwal
language : en
Publisher: Springer
Release Date : 2018-08-25

Neural Networks And Deep Learning written by Charu C. Aggarwal and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-25 with Computers categories.

This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10. The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.

Discrete Mathematics Of Neural Networks

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Author : Martin Anthony
language : en
Publisher: SIAM
Release Date : 2001-01-01

Discrete Mathematics Of Neural Networks written by Martin Anthony and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-01-01 with Computers categories.

This concise, readable book provides a sampling of the very large, active, and expanding field of artificial neural network theory. It considers select areas of discrete mathematics linking combinatorics and the theory of the simplest types of artificial neural networks. Neural networks have emerged as a key technology in many fields of application, and an understanding of the theories concerning what such systems can and cannot do is essential.

An Introduction To Neural Networks

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Author : Kevin Gurney
language : en
Publisher: CRC Press
Release Date : 2018-10-08

An Introduction To Neural Networks written by Kevin Gurney and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-08 with Computers categories.

Though mathematical ideas underpin the study of neural networks, the author presents the fundamentals without the full mathematical apparatus. All aspects of the field are tackled, including artificial neurons as models of their real counterparts; the geometry of network action in pattern space; gradient descent methods, including back-propagation; associative memory and Hopfield nets; and self-organization and feature maps. The traditionally difficult topic of adaptive resonance theory is clarified within a hierarchical description of its operation. The book also includes several real-world examples to provide a concrete focus. This should enhance its appeal to those involved in the design, construction and management of networks in commercial environments and who wish to improve their understanding of network simulator packages. As a comprehensive and highly accessible introduction to one of the most important topics in cognitive and computer science, this volume should interest a wide range of readers, both students and professionals, in cognitive science, psychology, computer science and electrical engineering.

Statistical Learning Using Neural Networks

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Author : Basilio de Braganca Pereira
language : en
Publisher: CRC Press
Release Date : 2020-08-25

Statistical Learning Using Neural Networks written by Basilio de Braganca Pereira and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-25 with Business & Economics categories.

Statistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Python introduces artificial neural networks starting from the basics and increasingly demanding more effort from readers, who can learn the theory and its applications in statistical methods with concrete Python code examples. It presents a wide range of widely used statistical methodologies, applied in several research areas with Python code examples, which are available online. It is suitable for scientists and developers as well as graduate students. Key Features: Discusses applications in several research areas Covers a wide range of widely used statistical methodologies Includes Python code examples Gives numerous neural network models This book covers fundamental concepts on Neural Networks including Multivariate Statistics Neural Networks, Regression Neural Network Models, Survival Analysis Networks, Time Series Forecasting Networks, Control Chart Networks, and Statistical Inference Results. This book is suitable for both teaching and research. It introduces neural networks and is a guide for outsiders of academia working in data mining and artificial intelligence (AI). This book brings together data analysis from statistics to computer science using neural networks.

Handbook Of Hydroinformatics

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Author : Saeid Eslamian
language : en
Publisher: Elsevier
Release Date : 2022-11-30

Handbook Of Hydroinformatics written by Saeid Eslamian and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-30 with Technology & Engineering categories.

Classic Soft-Computing Techniques is the first volume of the three, in the Handbook of HydroInformatics series. Through this comprehensive, 34-chapters work, the contributors explore the difference between traditional computing, also known as hard computing, and soft computing, which is based on the importance given to issues like precision, certainty and rigor. The chapters go on to define fundamentally classic soft-computing techniques such as Artificial Neural Network, Fuzzy Logic, Genetic Algorithm, Supporting Vector Machine, Ant-Colony Based Simulation, Bat Algorithm, Decision Tree Algorithm, Firefly Algorithm, Fish Habitat Analysis, Game Theory, Hybrid Cuckoo–Harmony Search Algorithm, Honey-Bee Mating Optimization, Imperialist Competitive Algorithm, Relevance Vector Machine, etc. It is a fully comprehensive handbook providing all the information needed around classic soft-computing techniques. This volume is a true interdisciplinary work, and the audience includes postgraduates and early career researchers interested in Computer Science, Mathematical Science, Applied Science, Earth and Geoscience, Geography, Civil Engineering, Engineering, Water Science, Atmospheric Science, Social Science, Environment Science, Natural Resources, and Chemical Engineering. - Key insights from global contributors in the fields of data management research, climate change and resilience, insufficient data problem, etc. - Offers applied examples and case studies in each chapter, providing the reader with real world scenarios for comparison. - Introduces classic soft-computing techniques, necessary for a range of disciplines.

Intelligent Infrastructure

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Author : Hojjat Adeli
language : en
Publisher: CRC Press
Release Date : 2008-10-06

Intelligent Infrastructure written by Hojjat Adeli and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-10-06 with Technology & Engineering categories.

Recent estimates hypothesize that the US will need $1.6 trillion dollars for the rehabilitation, replacement, and maintenance of existing infrastructure systems within the next 20 years. Presenting a new vision and way of designing and managing the civil infrastructure of the nation, Intelligent Infrastructure: Neural Networks, Wavelets, and Chaos