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Why AI Researchers Are Suddenly Obsessed With Whirlpools (Ep. 293)
VortexNet uses actual whirlpools to build neural networks. Seriously.
By borrowing equations from fluid dynamics, this new architecture might solve deep learning's toughest problems—from vanishing gradients to long-range dependencies.
Today we explain how vortex shedding, the Strouhal number, and turbulent flows might change everything in AI.
Sponsors
This episode is brought to you by Statistical Horizons
At Statistical Horizons, you can stay ahead with expert-led livestream seminars that make data analytics and AI methods practical and accessible.
Join thousands of researchers and professionals who’ve advanced their careers with Statistical Horizons.
Get $200 off any seminar with code DATA25 at https://statisticalhorizons.com
References
https://samim.io/p/2025-01-18-vortextnet/
View all episodes
By Francesco Gadaleta
4.2
7171 ratings
VortexNet uses actual whirlpools to build neural networks. Seriously.
By borrowing equations from fluid dynamics, this new architecture might solve deep learning's toughest problems—from vanishing gradients to long-range dependencies.
Today we explain how vortex shedding, the Strouhal number, and turbulent flows might change everything in AI.
Sponsors
This episode is brought to you by Statistical Horizons
At Statistical Horizons, you can stay ahead with expert-led livestream seminars that make data analytics and AI methods practical and accessible.
Join thousands of researchers and professionals who’ve advanced their careers with Statistical Horizons.
Get $200 off any seminar with code DATA25 at https://statisticalhorizons.com
References
https://samim.io/p/2025-01-18-vortextnet/
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