Graph Neural Networks in Action
Seiten
2025
Manning Publications (Verlag)
978-1-61729-905-6 (ISBN)
Manning Publications (Verlag)
978-1-61729-905-6 (ISBN)
- Noch nicht erschienen (ca. Juni 2025)
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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. Ideal for Python programmers, you will dive into graph neural networks perfect for node prediction, link prediction, and graph classification.
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.
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.
Keita Broadwater, PhD, MBA is a machine learning engineer with over ten years executing data science, analytics, and machine learning applications and projects. He is Chief of Machine Learning at candidates.ai, a firm which uses AI to enhance executive search. Dr. Broadwater has delivered DS and ML projects for all types of organisations, from small startups to Fortune 500 companies, and has developed and advised on graph-related projects in the industries of insurance, HR and recruiting, and supply chain.
| Erscheint lt. Verlag | 1.6.2025 |
|---|---|
| Verlagsort | New York |
| Sprache | englisch |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Technik | |
| ISBN-10 | 1-61729-905-7 / 1617299057 |
| ISBN-13 | 978-1-61729-905-6 / 9781617299056 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
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