Hands-on Geometric Deep Learning
Patrick R. NicolasGeometric 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 | 57 | Founded | 2 years ago | Last Issue | 12 days ago |
| Active | |||||
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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...
Adding Premium Section
Over the past year, this publication has become a deeply technical library for geometric deep learning and world models. I have recently upgraded the newsletter’s structure to optimize how you navigate our articles and resources. To sustain...
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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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