Hands-on graph neural networks using Python: practical techniques and architectures for building powerful graph and deep learning apps with PyTorch
Graph neural networks are a highly effective tool for analyzing data that can be represented as a graph, such as social networks, chemical compounds, or transportation networks. The past few years have seen an explosion in the use of graph neural networks, with their application ranging from natural...
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Birmingham, UK
Packt Publishing Ltd.
2023
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| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9781804617526/?ar |
| Zusammenfassung: | Graph neural networks are a highly effective tool for analyzing data that can be represented as a graph, such as social networks, chemical compounds, or transportation networks. The past few years have seen an explosion in the use of graph neural networks, with their application ranging from natural language processing and computer vision to recommendation systems and drug discovery. Hands-On Graph Neural Networks Using Python begins with the fundamentals of graph theory and shows you how to create graph datasets from tabular data. As you advance, you'll explore major graph neural network architectures and learn essential concepts such as graph convolution, self-attention, link prediction, and heterogeneous graphs. Finally, the book proposes applications to solve real-life problems, enabling you to build a professional portfolio. The code is readily available online and can be easily adapted to other datasets and apps. By the end of this book, you'll have learned to create graph datasets, implement graph neural networks using Python and PyTorch Geometric, and apply them to solve real-world problems, along with building and training graph neural network models for node and graph classification, link prediction, and much more. |
| Beschreibung: | Includes bibliographical references and index |
| Umfang: | 1 Online-Ressource (354 Seiten) illustrations |
| ISBN: | 9781804610701 1804610704 9781804617526 |
Internformat
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| id | ZDB-30-ORH-092524699 |
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| language | English |
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| spelling | Labonne, Maxime VerfasserIn aut Hands-on graph neural networks using Python practical techniques and architectures for building powerful graph and deep learning apps with PyTorch Maxime Labonne Birmingham, UK Packt Publishing Ltd. 2023 1 Online-Ressource (354 Seiten) illustrations Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes bibliographical references and index Graph neural networks are a highly effective tool for analyzing data that can be represented as a graph, such as social networks, chemical compounds, or transportation networks. The past few years have seen an explosion in the use of graph neural networks, with their application ranging from natural language processing and computer vision to recommendation systems and drug discovery. Hands-On Graph Neural Networks Using Python begins with the fundamentals of graph theory and shows you how to create graph datasets from tabular data. As you advance, you'll explore major graph neural network architectures and learn essential concepts such as graph convolution, self-attention, link prediction, and heterogeneous graphs. Finally, the book proposes applications to solve real-life problems, enabling you to build a professional portfolio. The code is readily available online and can be easily adapted to other datasets and apps. By the end of this book, you'll have learned to create graph datasets, implement graph neural networks using Python and PyTorch Geometric, and apply them to solve real-world problems, along with building and training graph neural network models for node and graph classification, link prediction, and much more. Python (Computer program language) Neural networks (Computer science) Machine learning Artificial intelligence Python (Langage de programmation) Réseaux neuronaux (Informatique) Apprentissage automatique Intelligence artificielle artificial intelligence |
| spellingShingle | Labonne, Maxime Hands-on graph neural networks using Python practical techniques and architectures for building powerful graph and deep learning apps with PyTorch Python (Computer program language) Neural networks (Computer science) Machine learning Artificial intelligence Python (Langage de programmation) Réseaux neuronaux (Informatique) Apprentissage automatique Intelligence artificielle artificial intelligence |
| title | Hands-on graph neural networks using Python practical techniques and architectures for building powerful graph and deep learning apps with PyTorch |
| title_auth | Hands-on graph neural networks using Python practical techniques and architectures for building powerful graph and deep learning apps with PyTorch |
| title_exact_search | Hands-on graph neural networks using Python practical techniques and architectures for building powerful graph and deep learning apps with PyTorch |
| title_full | Hands-on graph neural networks using Python practical techniques and architectures for building powerful graph and deep learning apps with PyTorch Maxime Labonne |
| title_fullStr | Hands-on graph neural networks using Python practical techniques and architectures for building powerful graph and deep learning apps with PyTorch Maxime Labonne |
| title_full_unstemmed | Hands-on graph neural networks using Python practical techniques and architectures for building powerful graph and deep learning apps with PyTorch Maxime Labonne |
| title_short | Hands-on graph neural networks using Python |
| title_sort | hands on graph neural networks using python practical techniques and architectures for building powerful graph and deep learning apps with pytorch |
| title_sub | practical techniques and architectures for building powerful graph and deep learning apps with PyTorch |
| topic | Python (Computer program language) Neural networks (Computer science) Machine learning Artificial intelligence Python (Langage de programmation) Réseaux neuronaux (Informatique) Apprentissage automatique Intelligence artificielle artificial intelligence |
| topic_facet | Python (Computer program language) Neural networks (Computer science) Machine learning Artificial intelligence Python (Langage de programmation) Réseaux neuronaux (Informatique) Apprentissage automatique Intelligence artificielle artificial intelligence |
| work_keys_str_mv | AT labonnemaxime handsongraphneuralnetworksusingpythonpracticaltechniquesandarchitecturesforbuildingpowerfulgraphanddeeplearningappswithpytorch |