Short article - I was thinking you could apply convolution to solve the Game of Life logic. So I quickly built a class to do just that:

Dask is commonly used for data processing in parallel compute. However I wanted to quickly explore using dask for parallel processing of generic python functions.

This post will explore building elastic net models using the PyTorch library. I will compare various scenarios with the implementations in scikit-learn to validate them.

This post follows on from looking at Bayesian Linear Regression. Here we look at the ability of the above method to track non-stationary problems where the regression coefficients can vary with time.

In this post I talk about reformulating linear regression in a Bayesian framework. This gives us the notion of epistemic uncertainty which allows us to generate probabilistic model predictions. I formulate a model class which can perform linear regression via Bayes rule updates. We show the results are the same as from the statsmodels library. I will also show some of the benefits of the sequential bayesian approach.