Think Graph Neural Networks (GNN) are hard to understand? Try this two part series..

[Graph Neural Networks part 1/2]: This tutorial is part one of a two parts GNN series.
Graphs helps us understand and visualize the relationship and connection information in a natural and close to human behavior. Graph Neural networks are solving various machine learning problems where CNN or convolutional neural networks can not be applied.

This video is designed for the early technology adopters who want to learn graphs and graph neural networks in shortest possible amount of the time...

In this video you will get all the required technical details and necessary technical explanations related to graph and graph neural networks so that you can code them in Python using NetworkX and PyG package, and apply your knowledge immediately to your own technical problems.

This tutorial is divided into two parts with the following topics:

Part 1 (This Video Tutorial):
-----------------------------
- Fundamentals of Graph
- Mathematics of Graph
- Introduction to NetworkX Python Package
- Graph Programming with NetworkX
- Introduction to GNN
- Relationship between GNN and CNN
- Introduction to PyG (pytorch_geometric)
- Graph Visualization Tools - yEd
- Various Graph Data Manipulation

Part 2 (   • Do you want to know Graph Neural Netw...  ):
-----------------------------
- Graph representations
-- Adjacency Matrix
-- Feature Matrix
-- Incidence Matrix
-- Degree Matrix
-- Laplacian Matrix
- Bag of Nodes
- Node Embedding and Node Embedding Space
- Applying Convolution to Graph similar to Image
- Message Passing
- Understanding Graph Datasets available in PyG
- Node Classification using MLP & GNN
- NetworkX and tSNE visualization of Graphs
- GNN Explainer

▬▬▬▬▬▬ ⏰ TUTORIAL TIME STAMPS ⏰ ▬▬▬▬▬▬
- (00:00) Tutorial Introduction
- (00:40) Part 1 Tutorial Content
- (02:22) Part 2 Tutorial Content
- (03:40) Resources & Acknowledgement
- (04:54) Graph Data Use Cases
- (09:41) Fundamentals of Graph
- (20:55) Mathematics of Graph
- (32:35) Coding Graph with NetworkX Library
- (46:17) Neighbors in Graph
- (48:20) Path_graph Type
- (49:00) Directed Graph
- (51:32) Adjacency Matrix
- (55:37) MultiDirected Graph
- (59:12) MultiEdge Attributes
- (01:00:52) MultiGraph
- (01:05:22) Sudoku Graph
- (01:07:15) Grid Graph
- (01:10:20) Graph Neural Networks (GNN)
- (01:16:28) GNN + CNN = GCN
- (01:22:44) PyG Introduction
- (01:23:36) What is a Tensor?
- (01:27:19) Datasets in PyG
- (01:39:36) Graph View in yEd
- (01:45:15) Create Graph in PyG
- (01:52:28) Recap

Google colab notebooks used in this tutorial:
-https://github.com/prodramp/DeepWorks...

Part 1 PDF document:
https://github.com/prodramp/DeepWorks...

Please visit:
------------------
- Prodramp LLC | https://prodramp.com | @prodramp
-   / prodramp  

Content Creator: Avkash Chauhan (@avkashchauhan)
-   / avkashchauhan  

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