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Graph Machine Learning Essentials: Foundations, Hands-On Implementation, Graph Neural Networks, PyTorch Geometric, and Applied Use Cases - Brossura
Libro 71 di 78: Self-Learning Management Series
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Foundations, Hands-On Implementation, Graph Neural Networks, PyTorch Geometric, and Applied Use Cases. Introduction to graph machine learning, hands-on implementations, and applied use cases.
Learn Graph Machine Learning, Graph Neural Networks, and PyTorch Geometric in One Practical Guide
Graph Machine Learning Essentials is a structured and easy-to-follow guide for ML engineers, data scientists, researchers, and technology professionals who want to understand how machine learning works on graph-structured data. From graph fundamentals and node embeddings to message passing, GNN architectures, and real-world applications, this book helps readers build practical knowledge they can use with confidence.
Key Features of the Book
- Strong foundations in graph machine learning – Understand graphs, graph learning tasks, node embeddings, message passing, and graph neural network basics in simple language.
- Advanced GNN concepts made approachable – Explore modern architectures and practical issues such as scalability, oversmoothing, and over-squashing without feeling overwhelmed.
- Real-world applications across industries – See how graph machine learning can support social network analysis, recommender systems, fraud detection, cybersecurity, bioinformatics, and knowledge graphs.
- Built for learning and retention – Reinforce concepts with end-chapter quizzes, in-chapter QR-based questions, practical examples, and programming assignments as online resources.
This book is designed to help readers not only understand graph machine learning but also apply it to real-world data problems. Whether you are working with connected data in finance, recommendation systems, cybersecurity, biology, or AI research, this guide gives you a practical path forward.
You will learn how to represent data as graphs, choose the right graph learning task, implement graph neural networks, and handle common challenges that arise with large and complex graphs.
If you want a beginner-friendly yet well-structured introduction to graph machine learning, this book will help you build the confidence to start working with graph data and graph neural networks in practice.
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Graph Machine Learning Essentials: Foundations, Hands-On Implementation, Graph Neural Networks, PyTorch Geometric, and Applied Use Cases (Paperback or Softback)
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Paperback. Condizione: new. Paperback. 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 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 terminologyImplement graph neural networks using PyTorch GeometricWork on real-world graph learning problems across industriesHandle practical challenges such as large graphs and oversmoothing This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9781636517254
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Paperback. Condizione: new. Paperback. 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 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 terminologyImplement graph neural networks using PyTorch GeometricWork on real-world graph learning problems across industriesHandle practical challenges such as large graphs and oversmoothing This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Codice articolo 9781636517254
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Paperback. Condizione: new. Paperback. 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 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 terminologyImplement graph neural networks using PyTorch GeometricWork on real-world graph learning problems across industriesHandle practical challenges such as large graphs and oversmoothing This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Codice articolo 9781636517254
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Graph Machine Learning Essentials | Foundations, Hands-On Implementation, Graph Neural Networks, PyTorch Geometric, and Applied Use Cases
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Taschenbuch. Condizione: Neu. Graph Machine Learning Essentials | Foundations, Hands-On Implementation, Graph Neural Networks, PyTorch Geometric, and Applied Use Cases | Pintu Kumar (u. a.) | Taschenbuch | Englisch | 2026 | Vibrant Publishers | EAN 9781636517254 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. Codice articolo 135915588
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 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 terminologyImplement graph neural networks using PyTorch GeometricWork on real-world graph learning problems across industriesHandle practical challenges such as large graphs and oversmoothing. Codice articolo 9781636517254
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