Gaussian Noise and Mean Squared Error

 Parameter estimation in linear regression from 2 perspectives.

Hierarchical Models in Numpyro

 Exploring pooling and hierarchical models with Numpyro by estimating the free throw percentage of NBA players

Gaussian Processes and Regression

 A explanation of Gaussian processes and Gaussian process regression, starting with simple intuition and building up to inference. I sample from a GP in native Python and test GPyTorch on a simple simulated example.

Dealing with Overconfidence in Neural Networks: Bayesian Approach

 I trained a classifier on images of animals and gave it an image of myself, it's 98% confident I'm a dog. This is an exploration of a possible Bayesian fix. Code available too

Notes on the Beta and Dirichlet Distributions

 The Beta and Dirichlet distributions are related to each other in a similar way to the Binomial and Multinomial distributions. This post explains the relationship between these 4 distributions using a simple example and some code.

Bayesian Changepoint Detection of COVID-19 Cases in Pyro

 Used Pyro and a Bayesian changepoint model to detect the date that COVID-19 cases started to flattern in different countries.

Ordinary VS Bayesian Linear Regression

 Walkthrough of the intuition behind Bayesian regression and a comparison with ordinary linear regression using a practical example in Pyro.

First Steps with Word Embeddings

 This post explains word2vec, GloVe and fasttext in detail and shows how to use pre-trained models for each in Python.