A big part of machine learning is classification — we want to know what class (a.k.a. group) an observation belongs to. The ability to precisely classify observations is extremely valuable for various business applications like predicting whether a particular user will buy a product or forecasting whether a given loan will default or not.
I discovered this coding puzzle in numero 4 of Paged Out, on page 32.
I found it fun and wrote my own solution in Python: paintbrush.py
It reminded me of Project Euler problems.
I recommend to give it a go based on the Paged Out one-page article, without reading Alperen Keles answer or mine! 🙂
A new count-distinct algorithm:
We present a simple, intuitive, sampling-based space-efficient algorithm whose description and the proof are accessible to undergraduates with the knowledge of basic probability theory.
Donald Knuth likes it: https://www-cs-faculty.stanford.edu/~knuth/papers/cvm-note.pdf
Their algorithm is not only interesting, it is extremely simple.
Furthermore, it’s wonderfully suited to teaching students who are learning the basics of computer science.
I’m pretty sure that something like this will eventually become a standard textbook topic.
There is the CWEB implementation he produced: cvm-estimates.w (archive.org)
Source: https://jmason.ie/2024/05/21/165901a.html
Interesting HackerNews comments: https://news.ycombinator.com/item?id=40379175
Have you ever wondered how Shazam works? I asked myself this question a few years ago and I read a research article written by Avery Li-Chun Wang, the confounder of Shazam, to understand the magic behind Shazam. The quick answer is audio fingerprinting, which leads to another question: what is audio fingerprinting?
This article is a summary of the search I did to understand Shazam.
Connexion