Requirements: .ePUB reader, 32.6 MB
Overview: A hands-on guide to powerful graph-based Deep Learning models! Learn how to build cutting-edge graph neural networks for recommendation engines, molecular modeling, and more. Graph Neural Networks in Action teaches you to create powerful Deep Learning models for working with graph data. You’ll learn how to both design and train your models, and how to develop them into practical applications you can deploy to production. 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. Inside this practical guide, you’ll explore common graph neural network architectures and cutting-edge libraries, all clearly illustrated with well-annotated Python code. 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. For Python programmers familiar with Machine Learning and the basics of Deep Learning.
Genre: Non-Fiction > Tech & Devices
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