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Handson Graph Neural Networks Using Python 1st Maxime Labonne

SKU: BELL-48489360

$ 31.00 ~~$ 45.00~~ (-31%)

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Handson Graph Neural Networks Using Python 1st Maxime Labonne instant download after payment.

Publisher: Packt Publishing Ltd.

File Extension: PDF

File size: 35.45 MB

Pages: 354

Author: Maxime Labonne

ISBN: 9781804617526, 1804617520

Language: English

Year: 2023

Edition: 1st

Product desciption

Handson Graph Neural Networks Using Python 1st Maxime Labonne by Maxime Labonne 9781804617526, 1804617520 instant download after payment.

Graph neural networks are a highly effective tool for analyzing data that can be represented as a graph, such as social networks, chemical compounds, or transportation networks. The past few years have seen an explosion in the use of graph neural networks, with their application ranging from natural language processing and computer vision to recommendation systems and drug discovery.
Hands-On Graph Neural Networks Using Python begins with the fundamentals of graph theory and shows you how to create graph datasets from tabular data. As you advance, you'll explore major graph neural network architectures and learn essential concepts such as graph convolution, self-attention, link prediction, and heterogeneous graphs. Finally, the book proposes applications to solve real-life problems, enabling you to build a professional portfolio. The code is readily available online and can be easily adapted to other datasets and apps.
By the end of this book, you'll have learned to create graph datasets, implement graph neural networks using Python and PyTorch Geometric, and apply them to solve real-world problems, along with building and training graph neural network models for node and graph classification, link prediction, and much more.