When I started learning Pytorch, one of the most confusing things to me was the torch.utils.data.Dataset and torch.utils.data.DataLoader. There were certain rules to define these things and I alway...

In this article, I am going to give a beginner-friendly introduction to generalized linear models (GLMs). As a prerequisite, you should have a pretty clear idea about about OLS and the assumptions ...

This article contains code snippets for “R” programming language. I am fairly new to “R” and documenting the code snippets helps me to understand it better. Just a word of caution, the snippets are...

This post is a continuation of my previous post Prerequisites for Convex Optimization 1. Here, I am going to cover more pre-requisite topics for understanding convex optimization. As I mentioned in...

Convex Optimization is a huge topic with thousands of research works published in this domain. Before the Deep learning revolution started, they were one of the coolest approaches to solving optimi...

Lagrange multipliers is a general concept in optimization theory that is applicable to any high dimensional surface. The drawback with such generalization is we can not visualize more than 3 dimens...

This post is largely inspired by the following lectures: Probabilistic Graphical Models Lecture Series by Daphne Koller MLSS 2013 Tübingen Lectures by Christopher Bishop ...

I know it is dumb to create a reference guide to distributions because it is too large of a topic to keep a reference on. I mean there are entire books written on these distributions. Also, some di...