Deep learning on graphs, Ma, Yao (michigan State University) Tang, Jiliang (michigan State University)
Автор: Goodfellow Ian, Bengio Yoshua, Courville Aaron Название: Deep Learning ISBN: 0262035618 ISBN-13(EAN): 9780262035613 Издательство: MIT Press Рейтинг: Цена: 13543.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.
"Written by three experts in the field, Deep Learning is the only comprehensive book on the subject." -- Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX
Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.
The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.
Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
Автор: Bollobas, Bela Название: Random graphs ISBN: 0521797225 ISBN-13(EAN): 9780521797221 Издательство: Cambridge Academ Рейтинг: Цена: 13306.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: In this second edition of a now classic text, the addition of two new sections, numerous new results and over 150 references mean that this represents a comprehensive account of random graph theory. Suitable for mathematicians, computer scientists and electrical engineers, as well as people working in biomathematics.
Описание: Since its inception in the 1960s, the theory of random graphs has evolved into a dynamic branch of discrete mathematics. Yet despite the lively activity and important applications, the last comprehensive volume on the subject is Bollobas`s well-known 1985 book.
Автор: Kocay William Название: Graphs, Algorithms, and Optimization ISBN: 1482251167 ISBN-13(EAN): 9781482251166 Издательство: Taylor&Francis Рейтинг: Цена: 12554.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
The second edition of this popular book presents the theory of graphs from an algorithmic viewpoint. The authors present the graph theory in a rigorous, but informal style and cover most of the main areas of graph theory. The ideas of surface topology are presented from an intuitive point of view. We have also included a discussion on linear programming that emphasizes problems in graph theory. The text is suitable for students in computer science or mathematics programs.
Автор: Yanpei Liu Название: Algebraic Elements of Graphs ISBN: 3110480735 ISBN-13(EAN): 9783110480733 Издательство: Walter de Gruyter Цена: 22305.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
This book studies algebraic representations of graphs in order to investigate combinatorial structures via local symmetries. Topological, combinatorial and algebraic classifications are distinguished by invariants of polynomial type and algorithms are designed to determine all such classifications with complexity analysis. Being a summary of the author's original work on graph embeddings, this book is an essential reference for researchers in graph theory.
Contents Abstract Graphs Abstract Maps Duality Orientability Orientable Maps Nonorientable Maps Isomorphisms of Maps Asymmetrization Asymmetrized Petal Bundles Asymmetrized Maps Maps within Symmetry Genus Polynomials Census with Partitions Equations with Partitions Upper Maps of a Graph Genera of a Graph Isogemial Graphs Surface Embeddability
Автор: Philipp Blanchard; Dimitri Volchenkov Название: Random Walks and Diffusions on Graphs and Databases ISBN: 3642268420 ISBN-13(EAN): 9783642268427 Издательство: Springer Рейтинг: Цена: 12571.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book introduces the theory of graphs and random walks on graphs, covers methods for exploring the structure of finite connected graphs and databases, and details applications in electric resistance networks, urban planning, linguistic databases and more.
Автор: Jakob Jonsson Название: Simplicial Complexes of Graphs ISBN: 3540758585 ISBN-13(EAN): 9783540758587 Издательство: Springer Рейтинг: Цена: 7959.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: A graph complex is a finite family of graphs closed under deletion of edges. Identifying each graph with its edge set, one may view a graph complex as a simplicial complex and hence interpret it as a geometric object. This volume examines topological properties of graph complexes, focusing on homotopy type and homology.
Автор: Erhard Godehardt Название: Graphs as Structural Models ISBN: 3528063122 ISBN-13(EAN): 9783528063122 Издательство: Springer Рейтинг: Цена: 12157.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The advent of the high-speed computer with its enormous storage capabilities enabled statisticians as well as researchers from the different topics of life sciences to apply mul- tivariate statistical procedures to large data sets to explore their structures.
Автор: Alison M. Marr; W.D. Wallis Название: Magic Graphs ISBN: 1489996281 ISBN-13(EAN): 9781489996282 Издательство: Springer Рейтинг: Цена: 6981.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This concise, self-contained exposition is unique in its focus on the theory of magic graphs/labelings and its application to a number of new areas. It may serve as a graduate text for courses and seminars in mathematics or computer science, or as a professional text for the researcher.
Автор: Coolen A.C.C. Название: Generating Random Networks and Graphs ISBN: 0198709897 ISBN-13(EAN): 9780198709893 Издательство: Oxford Academ Рейтинг: Цена: 11246.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book describes how to correctly and efficiently generate random networks based on certain constraints. Being able to test a hypothesis against a properly specified control case is at the heart of the `scientific method`.
Автор: Beineke Lowell W., Bagga Jay S. Название: Line Graphs and Line Digraphs ISBN: 3030813843 ISBN-13(EAN): 9783030813840 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Part I covers line graphs and their properties, while Part II looks at features that apply specifically to directed graphs, and Part III presents generalizations and variations of both line graphs and line digraphs.Line Graphs and Line Digraphs is the first comprehensive monograph on the topic.
Описание: This book provides readers with a practical guide to the principles of hybrid approaches to natural language processing (NLP) involving a combination of neural methods and knowledge graphs.
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