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- A Crash Course on Graph Neural Networks — Part 3
A Crash Course on Graph Neural Networks — Part 3
Implementation included.
Last week, we covered part 2 of the crash course on graph neural networks.
Here’s part 3 of it: A Crash Course on GNNs (Implementation Included) – Part 3.
Part 3 extends the ideas we discussed in Part 1 and Part 2 to cover more advanced methods for graph learning, along with several feature engineering techniques for graph neural networks, with implementation.
Yet again, the deep dive is quite beginner-friendly, and we cover all the basics to understand graph neural networks and how they work.
Finally, we learn how to implement them.
Read them here:
Why care?
Almost every big-tech ML company I know uses graph ML in some form or another.
Expertise in this area is becoming equally (or even more) important than traditional deep learning if you aim for these positions.
The reason why there has been a growing interest in graph ML is simple.
Traditional deep learning typically relies on data formats that are tabular, image-based, or sequential (like language) in nature.
However, with time, we have also realized the inherent challenges of such traditional approaches. One such challenge is their inability to naturally model complex relationships and dependencies between entities that are not easily captured by fixed grids or sequences.
More specifically, a significant proportion of our real-world data often exists (or can be represented) as graphs:
Entities (nodes) are connected by relationships (edges).
Connections carry significant meaning, which, if we knew how to model, can lead to much more robust models.
The field of Graph Neural Networks (GNNs) intends to fill this gap by extending deep learning techniques to graph data.
As a result, they have been emerging as a technique to learn smartly from data.
Hope you will learn something new today.
Read the two parts here:
We cover:
Background of GNNs and their benefits.
Type of tasks for GNNs.
Data challenges in GNNs.
Frameworks to build GNNs.
Advanced architectures to build robust GNNs.
Feature engineering methods.
A practical demo.
Insights and some best practices.
Read the two parts here:
Have a good day!
Avi
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