Hands-on Geometric Deep Learning
Patrick R. NicolasDive into hands-on Geometric Deep Learning! From manifolds and graph neural networks to Lie groups and point clouds, we blend theory with practical Python tools like PyTorch Geometric & Geomstats.
| Platform |  | Pricing | Only free issues | Publishes | Weekly |
|---|---|---|---|---|---|
| Issues | 46 | Subscribers | Read | patricknicolas.substack.com |
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Latest Issues
Recent posts by this newsletter. Browse the email archive.
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Graphs Deserve Some Attention
Graph Attention Models offer a hybrid solution to the limitations of spectral and spatial graph processing addressing shortcoming of graph convolutional networks (fixed weight and transductive learning strategy) and graphSAGE (variable size...
Mathematics of Abstract World Models
While LLMs have made massive strides, they remain hindered by high training costs and a lack of grounded, experiential reasoning. This has sparked a surge in World Model research—specifically Abstract World Models—which leverage advanced ge...
Visualization Tools for Geometric Deep Learning
Have you ever found it challenging to translate complex AI architectures or machine learning outcomes into a clear, compelling narrative?
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Authors
The writers behind this newsletter.
Geometric Deep Learning for ML Practitioners – Topology & Diff. Geometry. I have over 25 years of experience in software engineering with focus on data science and recently Geometric Deep Learning, and Graph Neural Networks.
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