Demystifying Algorithms, Math & Data: Practical Guides for the Curious Mind
When dealing with complex algorithms, clarity is king. I believe the most powerful ideas are the ones that can be explained clearly. My writing and tutorials focus on cutting through the jargon to reveal the core logic behind advanced concepts. As a computer science enthusiast and professional data analyst with a background in mathematics, Iām constantly learning and enjoy sharing my work, all with the goal of making these subjects accessible to everyone.
Screw memorising out of a textbook; letās understand the āwhyā and āhowā and focus on building a robust understanding.
Read my latest article here:
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Convolution, Kernels & Filters: A Beginnerās Guide to Edge Detection
Ever noticed how your brain just knows where one object ends and another begins? That instinctive separation is a fundamental part of how we perceive the world. And computers? Well, they just need a bit more help. In computer vision, edge detection is the process of teaching machines to recognise those boundaries, outlines, and transitions in intensity that define structure in an image. Read Moreā
My Philosophy: The Power of Intuition and āMental Modelsā
In a field as vast and complex as Data Science, itās easy to get lost in the endless details. My philosophy is to step back from the code and calculations and instead focus on building robust mental models ā a conceptual framework that help us truly understand how things work. Rather than just memorizing syntax or a set of instructions, I believe in building an intuitive grasp of the underlying logic and structure. This is where real mastery begins.
With a solid mental model in place, you develop a sense of intuition, a āgut feelingā for a problemās solution or an algorithmās behaviour. It allows you to move with confidence, diagnose issues quickly, and connect disparate ideas. My goal is to help you build these powerful mental maps, so you can stop simply following instructions and start creating with genuine understanding.
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Featured Articles
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Still copy-pasting into ChatGPT? Hereās how to turn your ideas into AI-powered apps
Youāre staring at ChatGPT. Youāve done this a hundred times before: typed a question, copied a response, pasted it into⦠Read More ā
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Youāre using ChatGPT wrong. Hereās how to prompt like a pro
Most people treat ChatGPT like a search engine. But with the right mindset and smarter prompting techniques, you can get⦠Read More ā
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22/7 and the Approximation of Irrational Numbers
Irrational numbers are somewhat difficult to work with. Unfortunately, theyāre also quite useful and crop up both in pure and⦠Read More ā
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Mountains, Cliffs, and Caves: A Comprehensive Guide to Using Perlin Noise for Procedural Generation
Unlock the power of Perlin Noise in procedural terrain creation. Learn how to implement it from scratch, adjust octaves, lacunarity,⦠Read More ā
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From Love to Logic: How Algorithms Decide OurĀ Matches
Explore the Gale-Shapley Algorithm and the Stable Matching Problem. Learn how algorithms create stable pairings, not just perfect matches. Read More ā
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The Bridges of Kƶnigsberg
The sun dipped low over the bustling City of Kƶnigsberg, casting golden reflections over the Pregel River. Its waters divided⦠Read More ā
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Information at a Glance: Do Your Charts Suck?
Letās face it: that report you worked on ā nobodyāsĀ actuallyĀ going to read it. In the best-case scenario, people might skim⦠Read More ā
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What if an Infinite Number of Spaceships Arrive at Hilbertās Hotel?
Suppose youāve just been hired as the new manager of Hilbertās Infinite Hotel. On your first day, you arrive at⦠Read More ā
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Solving the Travelling Salesman Problem Using a Genetic Algorithm
The Travelling Salesman Problem,Ā TSP, describes a scenario where a salesman wishes to visit a number of cities, while taking the⦠Read More ā