UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.
WHAT YOU BECOME
Perfect for these scenarios
Predict molecular properties to accelerate pharmaceutical research
Identify suspicious patterns in transaction networks for finance
Build graph-based user-item models for personalized suggestions
Detect communities and predict connections in user networks
MEASURED GAIN
Proven benefits and measurable impact
Cut implementation time for GNNs with pre-built modules
Optimized performance for large-scale graph datasets
State-of-the-art architectures boost prediction results
WHAT YOU GET
Files, tags and the three-step install
Tip: Read the documentation and the code before first use, so you know what it does and which permissions it needs.
NEXT SCRIPTS
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