Graph SAGE - Inductive Representation Learning on Large Graphs | GNN Paper Explained
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In this video, I do a deep dive into the Graph SAGE paper!
The first paper that started pushing the usage of GNNs for super large graphs.
You'll learn about:
✔️All the nitty-gritty details behind Graph SAGE
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
✅ Graph SAGE paper: https://arxiv.org/abs/1706.02216
✅ Chris Olah on LSTMs: https://colah.github.io/posts/2015-08-Understanding-LSTMs/
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⌚️ Timetable:
00:00 Intro
00:38 Problems with previous methods
04:30 High-level overview of the method
06:10 Some notes on the related work
07:13 Pseudo-code explanation
12:03 How do we train Graph SAGE?
15:40 Note on the neighborhood function
17:40 Aggregator functions
23:30 Results
28:00 Expressiveness of Graph SAGE
30:10 Mini-batch version
35:30 Problems with graph embedding methods (drift)
40:30 Comparison with GCN and GAT
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
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If these videos, GitHub projects, and blogs help you,
consider helping me out by supporting me on Patreon!
The AI Epiphany ► https://www.patreon.com/theaiepiphany
One-time donation:
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Much love! ❤️
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#graphsage #gnns #graphtheory
Видео Graph SAGE - Inductive Representation Learning on Large Graphs | GNN Paper Explained канала The AI Epiphany
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
In this video, I do a deep dive into the Graph SAGE paper!
The first paper that started pushing the usage of GNNs for super large graphs.
You'll learn about:
✔️All the nitty-gritty details behind Graph SAGE
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
✅ Graph SAGE paper: https://arxiv.org/abs/1706.02216
✅ Chris Olah on LSTMs: https://colah.github.io/posts/2015-08-Understanding-LSTMs/
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
⌚️ Timetable:
00:00 Intro
00:38 Problems with previous methods
04:30 High-level overview of the method
06:10 Some notes on the related work
07:13 Pseudo-code explanation
12:03 How do we train Graph SAGE?
15:40 Note on the neighborhood function
17:40 Aggregator functions
23:30 Results
28:00 Expressiveness of Graph SAGE
30:10 Mini-batch version
35:30 Problems with graph embedding methods (drift)
40:30 Comparison with GCN and GAT
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
💰 BECOME A PATREON OF THE AI EPIPHANY ❤️
If these videos, GitHub projects, and blogs help you,
consider helping me out by supporting me on Patreon!
The AI Epiphany ► https://www.patreon.com/theaiepiphany
One-time donation:
https://www.paypal.com/paypalme/theaiepiphany
Much love! ❤️
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
💡 The AI Epiphany is a channel dedicated to simplifying the field of AI using creative visualizations and in general, a stronger focus on geometrical and visual intuition, rather than the algebraic and numerical "intuition".
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
👋 CONNECT WITH ME ON SOCIAL
LinkedIn ► https://www.linkedin.com/in/aleksagordic/
Twitter ► https://twitter.com/gordic_aleksa
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👨👩👧👦 JOIN OUR DISCORD COMMUNITY:
Discord ► https://discord.gg/peBrCpheKE
📢 SUBSCRIBE TO MY MONTHLY AI NEWSLETTER:
Substack ► https://aiepiphany.substack.com/
💻 FOLLOW ME ON GITHUB FOR COOL PROJECTS:
GitHub ► https://github.com/gordicaleksa
📚 FOLLOW ME ON MEDIUM:
Medium ► https://gordicaleksa.medium.com/
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#graphsage #gnns #graphtheory
Видео Graph SAGE - Inductive Representation Learning on Large Graphs | GNN Paper Explained канала The AI Epiphany
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