Graph Neural Networks in Action (Hardcover)

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.

General

Imprint:	Manning Publications
Country of origin:	United States
Release date:	April 2024
Authors:	Keita Broadwater • Namid Stillman
Dimensions:	235 x 187 x 22mm (L x W x T)
Format:	Hardcover
Pages:	350
ISBN-13:	978-1-61729-905-6
Categories:	Books > Computing & IT > General Promotions
LSN:	1-61729-905-7
Barcode:	9781617299056

Is the information for this product incomplete, wrong or inappropriate? Let us know about it.

Does this product have an incorrect or missing image? Send us a new image.

Is this product missing categories? Add more categories.

Review This Product

No reviews yet - be the first to create one!