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 | 48 | Subscribers | Read | patricknicolas.substack.com |
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Latest Issues
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Presentation: Geometric World Models Beyond Tokens
Presentation slides \- We aren’t just forecasting pixels. World models map physical laws into latent space to enable genuine reasoning, while Geometric Deep Learning bakes the actual structure of the physical world directly into the neural ...
A Guided Tour of the Joint-Embedding Predictive Architecture
Large Language Models struggle to understand the physical world because they only see data as a flat string of words. In contrast, the Joint-Embedding Prediction Architecture (JEPA) acts as a "World Model" by processing information in a dee...
Benchmarking Topological Deep Learning
While TDA and Deep Learning rely on a wide range of mathematical structures and lifting/transforming algorithms, TopoBench simplifies the entire research cycle. It automates the design and evaluation process by providing a ready-to-use pipe...
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...
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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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