Applied Deep Learning on Graphs Leverage graph data for business applications using specialized deep learning architectures
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Form:Einzelkauf Download
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Sprache:Englisch
Fr. 46.00
inkl. gesetzl. MwSt.Beschreibung
Produktdetails
Format
ePUB 3
Kopierschutz
Nein
Family Sharing
Ja
Text-to-Speech
Ja
Erscheinungsdatum
27.12.2024
Verlag
Packt PublishingSeitenzahl
250 (Printausgabe)
Dateigröße
4974 KB
Sprache
Englisch
EAN
9781835885970
Gain a deep understanding of applied deep learning on graphs from data, algorithm, and engineering viewpoints to construct enterprise-ready solutions using deep learning on graph data for wide range of domains
Key Features- Explore graph data in real-world systems and leverage graph learning for impactful business results
- Dive into popular and specialized deep neural architectures like graph convolutional and attention networks
- Learn how to build scalable and productionizable graph learning solutions
- Purchase of the print or Kindle book includes a free PDF eBook
- Discover how to extract business value through a graph-centric approach
- Develop a basic understanding of learning graph attributes using machine learning
- Identify the limitations of traditional deep learning with graph data and explore specialized graph-based architectures
- Understand industry applications of graph deep learning, including recommender systems and NLP
- Identify and overcome challenges in production such as scalability and interpretability
- Perform node classification and link prediction using PyTorch Geometric
For data scientists, machine learning practitioners, researchers delving into graph-based data, and software engineers crafting graph-related applications, this book offers theoretical and practical guidance with real-world examples. A foundational grasp of ML concepts and Python is presumed.
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