Graph neural networks essentials (4 Ergebnisse)
Sprache: Englisch
Verlag: Vibrant Publishers Aug 2026, 2026
- Hardcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 87,03
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Buch. Zustand: Neu. Neuware - What if the most important information in your data lies not in individual rows and columns, but in the connections between them Graph machine learning helps uncover patterns hidden in these relationships.Graph Machine Learning Essentials is a practical and accessible guide to understanding how mach…ine learning works with graph-structured data, where entities are connected through relationships.Designed for software engineers, ML engineers, data scientists, research scholars, professionals, cybersecurity analysts, and students, the book introduces graph machine learning in a clear and structured way. It begins with the fundamentals of graph theory and moves into core graph learning tasks such as node classification, edge prediction, and graph classification. Readers learn how graphs are represented in data structures, how node and edge embeddings work, and why traditional machine learning approaches do not directly apply to graph data.The book gradually builds toward graph neural networks, message passing, and advanced GNN architectures while explaining practical challenges such as graph construction, scalability, oversmoothing, and over-squashing. Concepts are connected to real-world applications across domains such as recommender systems, fraud detection, cybersecurity, bioinformatics, transportation networks, and knowledge graphs.The book includes two helpful appendices-one reviewing essential machine learning concepts and the other introducing PyTorch Geometric to help readers get started quickly.After reading this book, you will be able to: - Understand key graph machine learning concepts and terminology- Implement graph neural networks using PyTorch Geometric- Work on real-world graph learning problems across industries- Handle practical challenges such as large graphs and oversmoothing.
- Hardcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
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EUR 181,89
EUR 64,10 VersandVersand von Deutschland nach USAAnzahl: 1 verfügbar
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book explains the technologies and tools that underpin GNNs, offering a clear and practical guide to their industrial applications and use cases. AI engineers, data scientists, and researchers in AI and graph theory will find detailed insights into the… latest trends and innovations driving this dynamic field. With practical chapters demonstrating how GNNs are reshaping various industry verticals and how they complement advances in generative, agentic, and physical AI this book is an essential resource for understanding and leveraging their potential.The neural network paradigm has surged in popularity for its ability to uncover hidden patterns within vast datasets. This transformative technology has spurred global innovations, particularly through the evolution of deep neural networks (DNNs). Convolutional neural networks (CNNs) have revolutionized computer vision, while recurrent neural networks (RNNs) and their advanced variants have automated natural language processing tasks such as speech recognition, translation, and content generation.Traditional DNNs primarily handle Euclidean data, yet many real-world problems involve non-Euclidean data complex relationships and interactions naturally represented as graphs. This challenge has driven the rise of graph neural networks (GNNs), an approach that extends deep learning into new domains.GNNs are powerful models designed to work with graph-structured data, where nodes represent individual data points and edges denote the relationships between them. Several variants have emerged:Graph Convolutional Networks (GCNs): These networks learn from a node s local neighborhood by aggregating information from adjacent nodes, updating the node s representation in the process.Graph Attentional Networks (GATs): By incorporating attention mechanisms, GATs focus on the most relevant neighbors during aggregation, enhancing model performance.Graph Recurrent Networks (GRNs): These networks combine principles from RNNs with graph structures to capture dynamic relationships within the data.GNNs are applied in a variety of advanced use cases, including node classification, link prediction, graph clustering, anomaly detection, recommendation systems, and also in natural language processing and computer vision. They help forecast traffic patterns, analyze molecular structures, verify programs, predict social influence, model electronic health records, and map brain networks.
- Hardcover
Anbieter: Revaluation Books, Exeter, Vereinigtes KönigreichRevaluation Books
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EUR 278,80
EUR 14,63 VersandVersand von Vereinigtes Königreich nach USAAnzahl: 2 verfügbar
Hardcover. Zustand: Brand New. 437 pages. 9.26x6.11x9.21 inches. In Stock.
Verlag: Vibrant Publishers Aug 2026, 2026
- Softcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 61,00
EUR 61,23 VersandVersand von Deutschland nach USAAnzahl: 2 verfügbar
Taschenbuch. Zustand: Neu. Neuware - What if the most important information in your data lies not in individual rows and columns, but in the connections between them Graph machine learning helps uncover patterns hidden in these relationships.Graph Machine Learning Essentials is a practical and accessible guide to understanding h…ow machine learning works with graph-structured data, where entities are connected through relationships.Designed for software engineers, ML engineers, data scientists, research scholars, professionals, cybersecurity analysts, and students, the book introduces graph machine learning in a clear and structured way. It begins with the fundamentals of graph theory and moves into core graph learning tasks such as node classification, edge prediction, and graph classification. Readers learn how graphs are represented in data structures, how node and edge embeddings work, and why traditional machine learning approaches do not directly apply to graph data.The book gradually builds toward graph neural networks, message passing, and advanced GNN architectures while explaining practical challenges such as graph construction, scalability, oversmoothing, and over-squashing. Concepts are connected to real-world applications across domains such as recommender systems, fraud detection, cybersecurity, bioinformatics, transportation networks, and knowledge graphs.The book includes two helpful appendices-one reviewing essential machine learning concepts and the other introducing PyTorch Geometric to help readers get started quickly.After reading this book, you will be able to: - Understand key graph machine learning concepts and terminology- Implement graph neural networks using PyTorch Geometric- Work on real-world graph learning problems across industries- Handle practical challenges such as large graphs and oversmoothing.
