So, back in 2021, we were in the development phase of our final year project. I will start with a little bit of background on the project. It was a classic sentiment analysis study on political parties in the country. There were 3 group members in total, and neither of us had foreseen the kind of hardware difficulties we might face at this stage of development. The thing is, we had no money to invest in those high-end machines to do extensive machine learning tasks.

Many people have asked me this question that whenever they get started with data science, they get stuck with the manifold of tools available to them. Although there are handful of guides available out there concerning the problem such as "19 Data Science Tools for people who aren't so good at Programming" or "A Complete Tutorial to Learn Data Science with Python from Scratch", I would like to show what tools I generally prefer for my day-to-day data science needs. Read on if you are interested! Note: I usually work in Python and in this article I intend to cover the tools used in python ecosystem on Windows. I come from a software engineering background.

This is a succint tutorial aimed at helping you set up an AWS GPU instance so that you can train and test your PyTorch models in the cloud. If you don't own a GPU like me, this can be a great way of drastically reducing the training time of your models, so while your instance is furiously crunching numbers in some faraway Amazon server, you can peacefully experiment with and prototype new architectures from the comfort of a Starbucks couch. The cool part is that if you're a high school or college student, you can sign up for a Github Developer pack which will get you $150 worth of free AWS credits. That's around 167 hours or 7 days of compute time1, an amply sufficient amount for those fun weekend side projects and experiments. As usual, any code or script that appears on this page can be downloaded from my Blog Repository.