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Graph Neural Networks in Action

Af: Keita Broadwater, Namid Stillman Engelsk Hardback
SPAR
kr 88

Graph Neural Networks in Action

Af: Keita Broadwater, Namid Stillman Engelsk Hardback

A hands-on guide to powerful graph-based deep learning models!

Graph Neural Networks in Action is a great guide about how to build cutting-edge graph neural networks and powerful deep learning models for recommendation engines, molecular modeling, and more. You will learn how to both design and train your models, and how to develop them into practical applications you can deploy to production.

Ideal for Python programmers, you will also explore common graph neural network architectures and cutting-edge libraries, all clearly illustrated with well-annotated Python code.

The main features include:

  • Train and deploy a graph neural network
  • Generate node embeddings
  • Use GNNs at scale for very large datasets
  • Build a graph data pipeline
  • Create a graph data schema
  • Understand the taxonomy of GNNs
  • Manipulate graph data with NetworkX

Go hands-on and explore relevant real-world projects as you dive into graph neural networks perfect for node prediction, link prediction, and graph classification.

About the technology

Graph neural networks expand the capabilities of deep learning beyond traditional tabular data, text, and images. This exciting new approach brings the amazing capabilities of deep learning to graph data structures, opening up new possibilities for everything – from recommendation engines to pharmaceutical research.

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A hands-on guide to powerful graph-based deep learning models!

Graph Neural Networks in Action is a great guide about how to build cutting-edge graph neural networks and powerful deep learning models for recommendation engines, molecular modeling, and more. You will learn how to both design and train your models, and how to develop them into practical applications you can deploy to production.

Ideal for Python programmers, you will also explore common graph neural network architectures and cutting-edge libraries, all clearly illustrated with well-annotated Python code.

The main features include:

  • Train and deploy a graph neural network
  • Generate node embeddings
  • Use GNNs at scale for very large datasets
  • Build a graph data pipeline
  • Create a graph data schema
  • Understand the taxonomy of GNNs
  • Manipulate graph data with NetworkX

Go hands-on and explore relevant real-world projects as you dive into graph neural networks perfect for node prediction, link prediction, and graph classification.

About the technology

Graph neural networks expand the capabilities of deep learning beyond traditional tabular data, text, and images. This exciting new approach brings the amazing capabilities of deep learning to graph data structures, opening up new possibilities for everything – from recommendation engines to pharmaceutical research.

Forudbestilling - Få besked når varen er på lager!
Produktdetaljer
Sprog: Engelsk
Sider: 350
ISBN-13: 9781617299056
Indbinding: Hardback
Udgave:
ISBN-10: 1617299057
Udg. Dato: 2 okt 2024
Længde: 0mm
Bredde: 0mm
Højde: 0mm
Forlag: Manning Publications
Oplagsdato: 2 okt 2024
Forfatter(e) Keita Broadwater, Namid Stillman


Kategori Neurale net og fuzzy systemer


Sprog Engelsk


Indbinding Hardback


Sider 350


Udgave


Længde 0mm


Bredde 0mm


Højde 0mm


Udg. Dato 2 okt 2024


Oplagsdato 2 okt 2024

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