Model fitting and evaluation with JAX

By Katerina Hynkova•

Updated on November 13, 2024

This data app makes it easy to explore and analyze global socio-economic data with JAX. It includes practical examples to get started and offers tools for data filtering, correlation analysis, clustering, and model fitting—perfect for uncovering trends in demographics, economies, and social factors across different countries.

Use template ->

It uses the Countries_of_the_world.csv dataset, which includes key country data e.g. population, area, GDP, literacy rates, infant mortality, migration etc., these insights help to understand the demographic, economic, and social profiles of countries worldwide.

The main purpose of this data app is to showcase use cases for JAX in combination with correlation analysis, clustering, probabilistic inference, model fitting, and more.

JAX is a linear algebra library that's quick for numerical computations on high-end machines like GPUs and TPUs. Python already has a great XLA (accelerated linear algebra) library named NumPy. The code snippets below show examples for both NumPy and JAX. In the article below, the core features of JAX are explained in detail.

Katerina Hynkova

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