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所在平台: Udemy |
课程主页: https://www.udemy.com/course/graph-neural-network/
课程评论：没有评论
课程名称：图神经网络 课程概述：近年来，图神经网络（GNN）由于其强大的表达能力和出色的性能，越来越受到各个领域的关注。图结构使我们能够捕捉具有复杂结构和关系的数据，而GNN则为我们提供了研究和建模这种复杂数据表示的机会，适用于分类、聚类、链路预测和稳健表示等任务。虽然GNN的起源可以追溯到1997年，但直到几年前（大约2017年），图上的深度学习才开始受到广泛关注。由于这一概念相对较新，大多数知识主要通过会议和期刊论文获取，当我开始学习GNN时，很难找到合适的入门材料。因此，我决定构建这个课程，旨在系统化学习材料，为GNN提供一个快速的全面入门课程。该课程将提供学习图神经网络的完整入门材料。完成本课程后，您将对该主题有较全面的理论和实践理解，包括数学与代码。如果您想开始学习图神经网络，或者希望能够在PyTorch Geometric中实现图神经网络模型，那么本课程将非常适合您。
In recent years, Graph Neural Network (GNN) has gained increasing popularity in various domains due to its great expressive power and outstanding performance. Graph structures allow us to capture data with complex structures and relationships, and GNN provides us the opportunity to study and model this complex data representation for tasks such as classification, clustering, link prediction, and robust representation. While the first motivation of GNN's roots traces back to 1997, it was only a few years ago (around 2017), that deep learning on graphs started to attract a lot of attention. Since the concept is relatively new, most of the knowledge is learned through conference and journal papers, and when I started learning about GNN, I had difficulty knowing where to start and what to read, as there was no course available to structure the content. Therefore, I took it upon myself to construct this course with the objective of structuring the learning materials and providing a rapid full introductory course for GNN. This course will provide complete introductory materials for learning Graph Neural Network. By finishing this course you get a good understanding of the topic both in theory and practice.This means you will see both math and code.If you want to start learning about Graph Neural Network, This is for you.If you want to be able to implement Graph Neural Network models in PyTorch Geometric, This is for you.
