Graph Neural Network

Graph Neural Network, available at $22.99, has an average rating of 4.33, with 26 lectures, based on 521 reviews, and has 1812 subscribers.

You will learn about 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 This course is ideal for individuals who are Engineering Graduate Students or Computer Science Graduate Students or Data Scientists or Python developers interested to learn Graph Neural Network or Deep learning engineers or Machine learning engineers or Signal Processing Engineers or Neural Network Enthusiasm It is particularly useful for Engineering Graduate Students or Computer Science Graduate Students or Data Scientists or Python developers interested to learn Graph Neural Network or Deep learning engineers or Machine learning engineers or Signal Processing Engineers or Neural Network Enthusiasm.

Enroll now: Graph Neural Network

Summary

Title: Graph Neural Network

Price: $22.99

Average Rating: 4.33

Number of Lectures: 26

Number of Published Lectures: 26

Number of Curriculum Items: 26

Number of Published Curriculum Objects: 26

Original Price: CA$29.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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.

Course Curriculum

Chapter 1: Graph Terminology & Representation

Lecture 1: Graph Definition

Lecture 2: Storing Graph Information

Lecture 3: Graph Degree and Laplacian of Graph

Lecture 4: Definition of Learning in Graph Representation Learning

Lecture 5: Drawback in existing graph learning models

Lecture 6: Workshop – Using Torch and Torch Geometric for defining a graph

Chapter 2: From Convolutional Neural Network to Graph Neural Network

Lecture 1: Review on Convolution Operation

Lecture 2: Graph Convolution (Signal Processing Point of View) Part A

Lecture 3: Graph Convolution (Signal Processing Point of View) Part B

Lecture 4: Message Passing Framework

Chapter 3: Introducing Different Graph Embedding Methods

Lecture 1: Graph Embedding Problem Statement

Lecture 2: DeepWalk Algorithm

Lecture 3: Workshop – RandomWalk using karateclub library

Lecture 4: Node2Vec Algorithm

Lecture 5: Workshop – Node2Vec Using Karateclub

Lecture 6: Workshop – Node2Vec Using Pytorch Geometric (Part A)

Lecture 7: Workshop – Node2Vec Using Pytorch Geometric (Part B)

Lecture 8: GNN Motivation

Lecture 9: Simplifying Graph Convolution Network

Lecture 10: Workshop – SGC (Part A)

Lecture 11: Workshop – SGC (Part B)

Lecture 12: Graph Convolution Network (GCN)

Lecture 13: Graph Attention Network

Chapter 4: Inductive and Transductive Graph Embedding

Lecture 1: Review on Popular GNN Embedding Methods

Lecture 2: Transductive vs Inductive Embedding Methods

Lecture 3: GraphSAGE

Instructors

Younes Sadat-Nejad
An academic researcher by day, an entrepreneur by night.

Rating Distribution

1 stars: 7 votes
2 stars: 19 votes
3 stars: 84 votes
4 stars: 187 votes
5 stars: 224 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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