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 | 62 | Founded | 2 years ago | Last Issue | 15 days ago |
| Active | |||||
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patricknicolas.substack.comLatest Issues
Building a Simple World Model 1: Tabular Reinforcement Learning
๐กWorld models Tutorial - Latent space dynamics using deep reinforcement learning Part 1
Latent dynamics may be intimidating, but easily understood by building a simple Deep Reinforcement Learning-based world model from scratch. In this ar...
Geometric World Models Tutorial: Vector Fields
๐กInteractive tutorial for world models - Latent space dynamics
The latent space of Geometric World Models relies on smooth manifolds whose geometry captures the intrinsic physical constraints, invariances, and topologies of the physical w...
JEPA: Breakthrough or Band-Aid?
Every emerging AI paradigm draws both enthusiastic supporters and detractors; the Joint Embedding Predictive Architecture (JEPA) is no exception.
Every emerging AI paradigm draws both enthusiastic supporters and detractors; the Joint Embed...
Hands-on Lie Geometry for Data Scientists
๐๐ถ๐ฒ ๐๐ฒ๐ผ๐บ๐ฒ๐๐ฟ๐ is key to ๐๐ฒ๐ผ๐บ๐ฒ๐๐ฟ๐ถ๐ฐ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด, ๐๐ผ๐ฟ๐น๐ฑ ๐บ๐ผ๐ฑ๐ฒ๐น๐. Introducing ๐๐ถ๐ฒ ๐ด๐ฟ๐ผ๐๐ฝ๐ with ๐ถ๐ป๐๐๐ถ๐๐ถ๐๐ฒ ๐ฒ๐ ๐ฎ๐บ๐ฝ๐น๐ฒ๐
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 realityโwithout any built-in k...
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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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