Things we learn here include image data exploration, transfer learning, custom datasets, comparing ML models, saving/loading models and model data, conditional setup for different work environments.

In this project, election data is collected, explored and cleaned. Visualization functions are refactored to be used on a dashboard later on.

The cleaned data is loadsed and the plot generating functions are refactored here. Then a dashboard is created using dash.

This tutorial covers the fundamentals of Bayesian approaches to time series, model construction, and practical implementation, using real-world data for hands-on learning.

Explore Bayesian Poisson regression for modeling count data with Julia and Turing.jl. This tutorial includes model setup, implementation, and performance assessment with a practical example.

Applying Turing.jl package in Julia for a probabilistic approach to a classification problem on a real-world dataset.