Thought-about the Magna Carta of the Info Age, Claude Shannon’s seminal 1948 paper posed a groundbreaking query:
How can we quantify communication?
This query laid the inspiration for data idea, revolutionising know-how in methods nonetheless felt immediately. Shannon’s insights underpin how we measure, retailer, and transmit data, contributing to breakthroughs in sign processing, information compression (e.g., Zip information, CDs), the Web, and synthetic intelligence. Past know-how, his work has influenced numerous fields resembling neurobiology, statistical physics, and laptop science (e.g., cybersecurity, cloud computing, and machine studying).
On this article, we deal with three key metrics: entropy, cross-entropy, and KL divergence, together with their basis in self-information. These ideas bridge likelihood idea with real-world functions. They function frequent sensible instruments for evaluation and optimisation utilized in information science and machine studying.
I’ll introduce these metrics after which discover an fascinating use case — message size optimisation, utilizing a toy instance of climate…
