Six months in the past, whereas optimizing a manufacturing ML mannequin that was consuming an excessive amount of reminiscence, I stumbled upon an answer in an unlikely place: a knowledge compression paper from the Nineteen Seventies. This revelation despatched me down a rabbit gap exploring the deep, usually ignored connection between information compression and machine studying.
What I found was profound: compression algorithms and machine studying fashions are basically fixing the identical elementary downside via completely different lenses. This perception not solely helped me remedy my instant engineering problem however remodeled my understanding of each fields.
On this article, I’ll discover the fascinating and surprisingly intimate relationship between compression algorithms and machine studying, revealing how these seemingly distinct domains are manifestations of the identical underlying rules. By understanding this connection, you’ll acquire beneficial insights that may elevate your work in both subject.
At their core, each compression and machine studying revolve across the similar central problem: predicting patterns in information utilizing chance distributions.
