Excel’s regex functions bring pattern recognition and text parsing to the spreadsheet. This article explores how Regex Pattern Matching works under the hood – from formal grammars and finite state machines to practical formulas for cleaning, extracting, and restructuring messy data.
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.
You’re staring at ChatGPT. You’ve done this a hundred times before: typed a question, copied a response, pasted it into some half-built project or document. Maybe it helped. Maybe it wasn’t quite what you were looking for. But here’s the thing no one tells you: that chat box you’re using? It’s not the product. It’s the demo.
Most people treat ChatGPT like a search engine. But with the right mindset and smarter prompting techniques, you can get clearer, more useful responses. This article shows you how.
Irrational numbers are somewhat difficult to work with. Unfortunately, they’re also quite useful and crop up both in pure and applied mathematics, and tons of places you may not expect. When written in decimal form, they result in an infinite sequence of numbers with no apparent pattern. If we round or truncate this number, we lose accuracy and introduce some level of error into any calculation.
Unlock the power of Perlin Noise in procedural terrain creation. Learn how to implement it from scratch, adjust octaves, lacunarity, persistence, and even extract real-world height distributions – to craft mountains, cliffs, and cave systems with precision and creativity.
Explore the Gale-Shapley Algorithm and the Stable Matching Problem. Learn how algorithms create stable pairings, not just perfect matches.
The sun dipped low over the bustling City of Königsberg, casting golden reflections over the Pregel River. Its waters divided the town into four distinct land masses, connected by seven foot-bridges that had become a curious point of both pride and frustration for its residents. By day, the bridges bustled with merchants and townsfolk, but as the evenings drew in, they became the source of a mysterious puzzle. Could someone, starting from any point on land, cross each of the seven bridges exactly once and return to where they began? It may sound simple, almost trivial, yet no matter how the townspeople tried, no one could find a solution.
Let’s face it: that report you worked on — nobody’s actually going to read it.
In the best-case scenario, people might skim through it, pausing briefly under the allure of a brightly-coloured diagram.
But if you’ve designed your diagrams properly, a brief glance is all someone should need to understand what the data is saying — at least at a high level.
Alan Turing, a pioneering figure in the world of Computer Science, is often celebrated for his work on breaking the Enigma code during World War II. However, one of his lesser-known achievements is creating one of the first chess algorithms — a small yet significant early step in the development of artificial intelligence systems.
