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 | 34 | Subscribers | Read | patricknicolas.substack.com |
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Latest Issues
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Slimming the Graph Neural Network Footprint
Practitioners of large-scale graph neural network training inevitably encounter GPU memory constraints. This article introduces and assesses critical training configurations for mitigating excessive GPU memory usage.
Demystifying the Math of Geometric Deep Learning
Learn the essential mathematical concepts behind Geometric Deep Learning without getting lost in equations - concepts only and no PhD required!
Graph Convolutional or SAGE Networks? Shootout
The right tool for the job! GraphSAGE and Graph Convolutional Network (GCN) are the most commonly used Graph Neural Networks architectures. It is critical to understand the advantages and limitations of each model in order to apply to a spe...
Topological Lifting of Graph Neural Networks
At some point you’ve probably hit the limits of standard Graph Neural Networks. What if you could lift your graph to a richer topological domain to explore, and ultimately improve node classification or link prediction?
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Authors
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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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