I’m currently a third year student at Vellore Institute of Technology pursuing a BTech in Computer Science. This is a page devoted to my technical research and projects. I’m an experienced technical content writer, web developer and a machine learning enthusiast. I love to explore challenges using a balance of both creative and technical expertise. I’d appreciate it if you could check some of my projects out. You can find more on my LinkedIn profile or my GitHub repository as given in the Contact Page.


Customizable login and registration templates made with Flask that offers a choice of DB to be used between MySQL and MongoDB. Check it out over here!

I’ve always loved web development but I never really had an idea to create a good project with a purpose until this inspiration came along. Comprising of three frontend templates, this project offers the basic layout of a login and registration template with a fully functioning backend.


A cli tool to implement image processing filters on any image or sample matrix taken as input.
Check how it works here!

Fun Backstory – Initially, I didn’t start this project because I wanted to create something cool. In fact, earlier this semester I had taken a subject called Digital Image Processing which I absolutely love. However, there were always too many calculations involved and I was never able to understand some concepts because all of their practical implementation were built in functions which didn’t necessarily provide the logic to show how it’s being done.

Eventually, a fortunate combination of frustrated laziness and motivation drove me to sit and understand the math before deciding to make the filters from scratch myself. Soon enough, a small script which started off as sly alternative to perform manual convolution grew into something slightly bigger and before long, I finally made something I could be proud of.


Recently I came across a very fascinating paper called ‘Efficent BackProp’ which explained a lot of concepts within machine learning and the underlying theoretical aspects behind it. Considering I’m relatively quite new to this field, this paper inspired me to start a small blog series explaining the topics involved which could also document my learning progress as well.
You can check out the series from the links given below!

Understanding Efficient BackProp Part 1 – Learning and Generalization
Understanding Efficient BackProp Part 2 – Standard backpropagation and practical optimization
Understanding Efficient BackProp Part 3 – Learning Rates and the Convergence of Gradient Descent