My momma at all times stated “Life was like a field of candies. You by no means know what you’re gonna get.”
— F. Gump (fictional thinker and entrepreneur)
That is the second article in a sequence on info quantification — a vital framework for information scientists. Studying to measure info unlocks highly effective instruments for enhancing statistical analyses and refining resolution standards in machine studying.
On this article we deal with entropy — a elementary idea that quantifies “on common, how stunning is an final result?” As a measure of uncertainty, it bridges chance principle and real-world purposes, providing insights into purposes from information range to decision-making.
We’ll begin with intuitive examples, like coin tosses and roles of cube 🎲, to construct a strong basis. From there, we’ll discover entropy’s various purposes, comparable to evaluating resolution tree splits and quantifying DNA range 🧬. Lastly, we’ll dive into enjoyable puzzles just like the Monty Corridor drawback 🚪🚪🐐 and I’ll confer with a tutorial for optimisation of the addictive WORDLE recreation 🟨🟩🟩⬛🟩.
