While lost in a book recently, I stumbled upon the idea of the Unreasonable Effectiveness of Mathematics in Natural Sciences. Intrigued, I did a quick Google search and found Hamming’s take on it, opening up a whole new perspective.
Back in school and college, I loved math, not just for the easy grades but because it wasn’t about mindless memorization. Watching a Physics professor effortlessly weave equations like poetry fueled my fascination. Algebra, Analytical Geometry, Trigonometry, and Calculus—those were my playgrounds. I even spent hours wrestling with partial differential equations during my engineering days.
Post-graduation, my math escapades took a backseat as I dove into the world of hardware and then software. Programming, especially in applications, didn’t demand much math. But then Lambda Calculus caught my eye, broadening my understanding. Lately, I’ve been bumping into calculus in the realms of machine learning, each encounter reshaping my idea of its essence. It’s like reconnecting with an old friend, discovering new facets of its charm. This journey, full of surprises and revelations, has made me appreciate the richness of mathematics.
My interest in Mathematics is back. But now it is mostly Vectors, Vector Algebra, Vector Calculus, and Mathematics for Machine Learning.
References:
The Unreasonable Effectiveness of Mathematics in the Natural Sciences
#Work-in-progress
Note:
This article was converted to a conversational style using ChatGPT.
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