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.