Graph machine learning essentials par pintu kumar (12 résultats)
Langue : anglais
Edité par Vibrant Publishers, 2026
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Vendeur : California Books, Miami, FL, Etats-UnisCalifornia Books
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Langue : anglais
Edité par Vibrant Publishers, 2026
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Langue : anglais
Edité par Vibrant Publishers, 2026
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PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000.
Langue : anglais
Edité par Vibrant Publishers, 2026
- Couverture rigide
Vendeur : PBShop.store UK, Fairford, GLOS, Royaume-UniPBShop.store UK
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HRD. Etat : New. New Book. Shipped from UK. Established seller since 2000.
Langue : anglais
Edité par Vibrant Publishers, 2026
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Hardcover. Etat : new. Hardcover. 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.
Edité par Vibrant Publishers, 2026
- Couverture souple
- impression à la demande
Vendeur : Grand Eagle Retail, Bensenville, IL, Etats-UnisGrand Eagle Retail
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Paperback. Etat : 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.
Langue : anglais
Edité par Vibrant Publishers, 2026
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Etat : New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
Langue : anglais
Edité par Vibrant Publishers, 2026
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Vendeur : CitiRetail, Stevenage, Royaume-UniCitiRetail
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Hardcover. Etat : new. Hardcover. 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.
Langue : anglais
Edité par Vibrant Publishers, 2026
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Taschenbuch. Etat : 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 Ba…d Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
Langue : anglais
Edité par Vibrant Publishers, 2026
- Couverture rigide
- impression à la demande
Vendeur : preigu, Osnabrück, Allemagnepreigu
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
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Buch. Etat : Neu. Graph Machine Learning Essentials | Foundations, Hands-On Implementation, Graph Neural Networks, PyTorch Geometric, and Applied Use Cases | Pintu Kumar (u. a.) | Buch | Englisch | 2026 | Vibrant Publishers | EAN 9781636517278 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gp…sr[at]libri[dot]de | Anbieter: preigu Print on Demand.
Edité par Vibrant Publishers, 2026
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Vendeur : AussieBookSeller, Truganina, VIC, AustralieAussieBookSeller
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Paperback. Etat : 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.
Edité par Vibrant Publishers, 2026
- Couverture souple
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Vendeur : CitiRetail, Stevenage, Royaume-UniCitiRetail
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Paperback. Etat : 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.
