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Название: Graph Neural Networks in Action (MEAP v4)
Автор: Keita Broadwater
Издательство: Manning Publications
Год: 2022
Страниц: 197
Язык: английский
Формат: pdf, epub
Размер: 15.1 MB
A hands-on guide to powerful graph-based Deep Learning models! Learn how to build cutting-edge graph neural networks (GNN) 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. If you are an intermediate user of Python, and have some Data Science or Machine Learning engineering experience, you should be good enough to dive in. I also assume you have basic knowledge of neural networks and linear algebra. For Python programmers familiar with Machine Learning and the basics of Deep Learning.
Автор: Keita Broadwater
Издательство: Manning Publications
Год: 2022
Страниц: 197
Язык: английский
Формат: pdf, epub
Размер: 15.1 MB
A hands-on guide to powerful graph-based Deep Learning models! Learn how to build cutting-edge graph neural networks (GNN) 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. If you are an intermediate user of Python, and have some Data Science or Machine Learning engineering experience, you should be good enough to dive in. I also assume you have basic knowledge of neural networks and linear algebra. For Python programmers familiar with Machine Learning and the basics of Deep Learning.