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.
Languages follow invisible rules we rarely notice—but what if we could describe them with math? This article breaks down formal grammars and shows how they underpin both human language and computer logic.
Let’s get started with perhaps the most fundamental mathematical object, the set.
In math, we often talk about collections of things. A set is just that: a collection of things.
The elements of a set could be anything: numbers, equations, lines, shapes, fruit or any other thing you can dream up. Let’s be a little more specific and give a more precise definition of a set.
Suppose we have a model that predicts the colour of a ball. We have 5 red balls and 5 blue balls and we ask our model to make a prediction of the colour of each of them. How can we evaluate our model’s success?
One approach would be to count the number of predictions that our model gets right. We count the number of red balls that the model predicts to be red (number of True Positives) and count the number of blue balls that our model predicts to be blue (the number of True Negatives).
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.
Strava offers a powerful Web API that allows users to access and analyse data from their activities. Whether you’re a fitness enthusiast looking to track your progress or a developer interested in building a simple app that leverages activity data, the Strava API provides a wealth of possibilities.
Looking at the images above, we see an example of an image posterization filter that gives images a cartoon-like appearance, but behind the scenes, this filter is actually using a machine learning algorithm known as clustering.
Before exploring into how this process works and seeing how we can implement it in Python, let’s take a look at why we might want to do this in the first place.
