From Graph Representation Learning to Graph Neural Network (Complete Introductory Course to GNN)

Graph Representation Learning

Graph Neural Network (GNN)

Graph Analysis

Graph Embedding

DeepWalk

Node2Vec

Graph Convolution Network (GCN)

Graph Attention Network (GAT)

Simplifying Graph Convolution (SGC)

Inductive and Transudative Learning

GraphSAGE

Pytorch Geometric

Convolution

Introductory background on machine learning and deep learning

Introductory background on signal processing and data analysis

Algebra

Python

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.

Eeering Graduate Students

Computer Science Graduate Students

Data Scientists

Python developers interested to learn Graph Neural Network

Deep learning eeers

Machine learning eeers

Signal Processing Eeers

Neural Network Enthusiasm






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