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Graph Machine Learning: Learn about the latest advancements in graph data to build robust machine learning models

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Marque : GENERIC
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Enhance your data science skills with this updated edition featuring new chapters on LLMs, temporal graphs, and updated examples with modern frameworks, including PyTorch Geometric and DGLFree with your book: DRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesMaster new graph ML techniques through updated examples using PyTorch Ge...

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Description produit

Marque
GENERIC
Titre
Graph Machine Learning: Learn about the latest advancements in graph data to build robust machine learning models
Éditeur
Packt Publishing
Type de produit
paperback
Présentation du livre
paperback
Date de sortie
7/18/2025 12:00:00 AM
Langue d'origine
English
ISBN
1803248068
Dimensions
7.5 x 0.98 x 9.25 inches
Nombre de pages
434 pages
Langue
English
Résumé
Enhance your data science skills with this updated edition featuring new chapters on LLMs, temporal graphs, and updated examples with modern frameworks, including PyTorch Geometric and DGLFree with your book: DRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesMaster new graph ML techniques through updated examples using PyTorch Geometric and Deep Graph Library (DGL)Explore GML frameworks and their main characteristicsLeverage LLMs for machine learning on graphs and learn about temporal learningPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionGraph Machine Learning, Second Edition builds on its predecessor’s success, delivering the latest tools and techniques for this rapidly evolving field. From basic graph theory to advanced ML models, you’ll learn how to represent data as graphs to uncover hidden patterns and relationships, with practical implementation emphasized through refreshed code examples. This thoroughly updated edition replaces outdated examples with modern alternatives such as PyTorch and DGL, available on GitHub to support enhanced learning.The book also introduces new chapters on large language models and temporal graph learning, along with deeper insights into modern graph ML frameworks. Rather than serving as a step-by-step tutorial, it focuses on equipping you with fundamental problem-solving approaches that remain valuable even as specific technologies evolve. You will have a clear framework for assessing and selecting the right tools.By the end of this book, you’ll gain both a solid understanding of graph machine learning theory and the skills to apply it to real-world challenges.*Email sign-up and proof of purchase required -What you will learnImplement graph ML algorithms with examples in StellarGraph, PyTorch Geometric, and DGLApply graph analysis to dynamic datasets using temporal graph MLEnhance NLP and text analytics with graph-based techniquesSolve complex real-world problems with graph machine
Auteur
Aldo Marzullo, Enrico Deusebio, Claudio Stamile
Date de parution
7/18/2025 12:00:00 AM