MP4 | Video: h264, 1280×720 | 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.
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.
Who this course is for
Engineering Graduate Students
Computer Science Graduate Students
Data Scientists
Python developers interested to learn Graph Neural Network
Deep learning engineers
Machine learning engineers
Signal Processing Engineers
Neural Network Enthusiasm
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