Intermediate Alg Graphs Models Aie Sup PDF Download
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Author: Bittinger
Publisher:
ISBN: 9780321168641
Category :
Languages : en
Pages : 944
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Book Description
Author: Bittinger
Publisher:
ISBN: 9780321168641
Category :
Languages : en
Pages : 944
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Book Description
Author: Martin-gay
Publisher:
ISBN: 9780131469013
Category :
Languages : en
Pages : 966
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Book Description
Author: Aufmann
Publisher: Houghton Mifflin
ISBN: 9780395712221
Category : Mathematics
Languages : en
Pages : 788
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Book Description
Author: Oak Ridge National Laboratory
Publisher:
ISBN:
Category : Information storage and retrieval systems
Languages : en
Pages : 296
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Book Description
Author: William L. William L. Hamilton
Publisher: Springer Nature
ISBN: 3031015886
Category : Computers
Languages : en
Pages : 141
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Book Description
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.
Author: Robert F. Blitzer
Publisher:
ISBN: 9780321837349
Category : Algebra
Languages : en
Pages : 0
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Book Description
Bob Blitzer has inspired thousands of students with his engaging approach to mathematics, making this beloved series the #1 in the market. Blitzer draws on his unique background in mathematics and behavioral science to present the full scope of mathematics with vivid applications in real-life situations. Students stay engaged because Blitzer often uses pop-culture and up-to-date references to connect math to students' lives, showing that their world is profoundly mathematical.
Author: Elaine Hubbard
Publisher: Brooks Cole
ISBN: 9780618223756
Category : Mathematics
Languages : en
Pages : 828
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Book Description
Author: Lynn Marecek
Publisher:
ISBN: 9781951693848
Category :
Languages : en
Pages :
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Author:
Publisher:
ISBN:
Category : Education
Languages : en
Pages : 1032
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Author: Leonard Bacon
Publisher:
ISBN:
Category :
Languages : en
Pages : 520
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Book Description
