Geometric Deep Learning, World Models use complex math to overcome limitations of traditional deep learning. We make that math practical through hands-on tutorials.
| Platform |  | Pricing | Freemium | Publishes | Weekly |
|---|---|---|---|---|---|
| Issues | 59 | Founded | 2 years ago | Last Issue | 4 days ago |
| Active | |||||
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Hands-on Lie Geometry for Data Scientists
𝗟𝗶𝗲 𝗚𝗲𝗼𝗺𝗲𝘁𝗿𝘆 is key to 𝗚𝗲𝗼𝗺𝗲𝘁𝗿𝗶𝗰 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴, 𝘄𝗼𝗿𝗹𝗱 𝗺𝗼𝗱𝗲𝗹𝘀. Introducing 𝗟𝗶𝗲 𝗴𝗿𝗼𝘂𝗽𝘀 with 𝗶𝗻𝘁𝘂𝗶𝘁𝗶𝘃𝗲 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀
Lie Geometry is a critical element of Geometric Deep Learning and con...
The Geometric Future of World Models
Geometric Deep Learning (GDL) aggregates and modularizes the mathematical concepts that enable reasoning in the latent space of World Models.
Have you ever wondered how World Models can represent our observed reality—without any built-in k...
Fractal Dimension for Configuring Convolutional Networks
Geometric Deep Learning / Geometry
Challenged by the configuration of Convolutional Neural Networks for complex images?
Q & A Answers
Answers to Q&A of Hands-on Geometric Deep Learning articles
You have questions … We have answers
Persistent Homology for the Rest of Us
Persistent homology offers an elegant & robust framework for separating structural features from noise, revealing the presence of loops, cycles, singularities.
Persistent homology has numerous applications in material science (namomaterial...
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Authors
The writers behind this newsletter.
Patrick Nicolas is a 30-year software engineering veteran and consultant specializing in Geometric Deep Learning and World Models, author of Scala for Machine Learning, and writer of Geometric Learning in Python.
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