# My small corner of the internet

# Exploring multi-armed bandit baseline strategies

Our problem:
For the next 100 days, we will have 1 hour to play a video games.
We have 20 games, but have no idea which one we will enjoy the most.
How do we decide what to play each day?
We assume that the enjoyment we get from a single hour is random and comes from a beta distribution.
Each game has a different distribution.
Each hour we play of a game gives us an enjoyment value and helps build our knowledge of that game.

# Game of Life with convolution

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:

# Parallel general functions using Dask

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.

# Building elastic net models with PyTorch

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.

# Bayesian Linear Regression - Adaptive coefficients

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.

# Bayesian Linear Regression

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.

# Building a project in Kedro

Kedro is a python data science library that helps with:
creating reproducible, maintainable and modular data science code

# Neural prophet vs ARIMA - long AR models

How fast is fitting long AR models using Neural prophet?
In this quick test we will fit AR based models with different lags and see how long they take to fit.

# Trying out PyTorch Lightning

In this post I was trying out PyTorch Lightning to see if itâ€™s a library that should be used by default alongside PyTorch.
I will create the same nonlinear probabilistic network from before, but this time using Lightning.
Hence the first few steps are the same as previously shown.

# PyTorch: Linear regression to non-linear probabilistic neural network

This post follows a similar one I did a while back for Tensorflow Probability:
Linear regression to non linear probabilistic neural network