Imagine you are a highly trained art restorer who specializes exclusively in Renaissance paintings. You’ve spent years studying the brushstrokes, palettes, a...
Imagine you are analyzing credit card transactions. A \500 purchase at the same store might be highly suspicious for a college student who typically spends \...
Imagine you are a quality control manager at a factory that makes premium watches. You have seen thousands of perfect watches. You know exactly what a "norma...
Most anomaly detection algorithms try to learn what "normal" looks like. They build a complex profile of your data's dense regions and then flag anything tha...
Imagine a credit card transaction for \$20,000 originating from Antarctica when the card owner lives in New York. Or a jet engine sensor reporting a vibratio...
Imagine you are a spy trying to smuggle a detailed map out of a secure facility. You can't carry the large map, but you can memorize a few key landmarks and ...
If you have ever tried to visualize a dataset with 100,000 rows using t-SNE, you probably had time to brew coffee, drink it, and perhaps write a novel while ...
Imagine trying to draw a map of the world, but instead of three dimensions (latitude, longitude, altitude), the world has 784 dimensions. This is the reality...
Imagine trying to take a photograph of a teapot. The teapot exists in three dimensions—it has height, width, and depth. But your photograph only has two dime...
Imagine you are looking at a dataset shaped like a donut—a tight inner circle of data points surrounded by a larger outer ring. If you ask the most popular c...
Imagine you are trying to group customers based on their spending habits. You try the popular K-Means algorithm, but it forces every customer into a perfect ...
Imagine you are an urban planner analyzing population data. You have a dataset containing the GPS coordinates of every house in a region. You want to identif...
Imagine you are looking at a satellite image of a city at night. You don't need to know beforehand exactly how many neighborhoods exist to identify them. You...
Imagine trying to organize a library of 10,000 books without knowing any genres beforehand. If you used K-Means clustering, you would have to guess: "I think...
Imagine you are the CEO of a global coffee chain. You have the GPS coordinates of 10,000 customers who order delivery every morning, and you have the budget ...
