Fascinated by Machine Learning from a young age.

I've always been fascinated by technology even before I knew what programming was. My fascination started as a kid who always loved tinkering with computers, including editing HTML and CSS files and little shell scripts.

My first encounter with programming happened during my first undergraduate degree in Computer Science, where I learned the basics of the C Programming Language. I've been fascinated by the ability to program inanimate objects ever since.

It was also at this time that I took up university-level statistics and calculus, laying the mathematical groundwork for what would become a lifelong passion.

Even as an undergraduate student in Architecture, my love for programming followed me. I studied Python on my own and delved deep into an emerging field called Computational Design—an interdisciplinary field that combines Architecture, Design, and Computer Science.

In addition to being fascinated by programming during my undergraduate years, what fascinated me even more was the rapidly growing field of Machine Learning. I remember tenaciously following developments from the best AI companies in the world like DeepMind and OpenAI.

I still vividly remember being so fascinated by OpenAI's developments at a time when I was still a Python beginner. I was even more fascinated when they showcased their AI (OpenAI Five) as capable of playing one of the most complex esports in Dota 2.

Around 2017, 3Blue1Brown released his seminal series on the mathematics behind deep learning and neural networks. It has since been a very strong impetus for me to take programming even more seriously than I already had.

I remember being so impressed with the idea that something as simple as perceptrons, when combined, can model almost anything we can imagine. I've always been equally fascinated by the fact that the overall effect of a neural network is that it basically acts as a universal function approximator—meaning theoretically that if you can represent anything as input that a machine can understand, then a neural network can be designed to approximate the function that generates that input.

After coming across that series, for the first time, I found out that AI/Machine Learning involved some of the most fascinating topics I have ever come across: Programming, Neuroscience, Multivariable Calculus, and Linear Algebra.

Fast forward to today, I have successfully studied the foundations of Software Engineering (or as Machine Learning Engineers would call them— Software 1.0).

I have since become confident due to a combination of having studied complex programming topics and having sufficient knowledge on the foundations of ML (Python, Calculus, Linear Algebra) that I am now ready to take the next stage of my life—studying Machine Learning, aka, Software 2.0.