Graph Neural Network
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 26 lectures (4h 29m) | Size: 1.73 GB
From Graph Representation Learning to Graph Neural Network (Complete Introductory Course to GNN)
What you'll learn:
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
Requirements
Introductory background on machine learning and deep learning
Introductory background on signal processing and data analysis
Algebra
Python
Description
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.
