Data Science #33

In this issue: interpretable time-series modelling using Gaussian processes; new breakthrough brings matrix multiplication closer to ideal; singular value decomposition and it's use in recommendation systems; why line chart baselines can start at non-zero; building interactive embedding visualizations; and more.

The sponsor of this issue is Galileo.

• Interpretable time-series modelling using Gaussian processes
• New breakthrough brings matrix multiplication closer to ideal
• On singular value decomposition and it's use in recommendation systems
• [Sponsored] NVIDIA, Databricks, HP, and more share GenAI tips

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• How to use PostgreSQL for geoanalytics tasks
• Temporal autocorrelation in GAMs and the mvgam package
• Why line chart baselines can start at non-zero
• [Tool] Daft: Distributed data engine for Python/SQL designed for the cloud
• Building interactive embedding visualizations

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