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Graph Machine Learning Essentials: Foundations, Hands-On Implementation, Graph Neural Networks, PyTorch Geometric, and Applied Use Cases - Couverture rigide
À propos des auteurs
Pintu Kumar is a Ph.D. scholar at IIT Bombay specializing in graph machine learning. A PMRF fellow and Silver Medalist in Mathematics, he focuses on research, teaching, and making complex ideas accessible.
Vibrant Publishers is focused on presenting the best texts for learning about technology and business as well as books for test preparation. Categories include programming, operating systems and other texts focused on IT. In addition, a series of books helps professionals in their own disciplines learn the business skills needed in their professional growth.Vibrant Publishers has a standardized test preparation series covering the GMAT, GRE and SAT, providing ample study and practice material in a simple and well organized format, helping students get closer to their dream universities.
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Graph Machine Learning Essentials: Foundations, Hands-On Implementation, Graph Neural Networks, PyTorch Geometric, and Applied Use Cases
Edité par
Vibrant Publishers, 2026
ISBN 10 : 1636517277
ISBN 13 : 9781636517278
Neuf
Couverture rigide
Vendeur : California Books, Miami, FL, Etats-Unis
Évaluation du vendeur 4 sur 5 étoiles
Etat : New. N° de réf. du vendeur I-9781636517278
Acheter neuf
EUR 67,72
Livraison gratuite
Expédition nationale : Etats-Unis
Expédition nationale : Etats-Unis
Quantité disponible : Plus de 20 disponibles
Image d'archives
Graph Machine Learning Essentials : Foundations, Hands-On Implementation, Graph Neural Networks, PyTorch Geometric, and Applied Use Cases
Edité par
Vibrant Publishers Aug 2026, 2026
ISBN 10 : 1636517277
ISBN 13 : 9781636517278
Neuf
Couverture rigide
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : 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 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. N° de réf. du vendeur 9781636517278
Acheter neuf
EUR 87,03
Expédition à EUR 61,93
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
Quantité disponible : 2 disponible(s)