---
title: "A Mathematical Theory of Communication"
description: "Shannon's information theory paper is hard but worth anchoring early in the calendar. Entropy, bits, uncertainty, and channel capacity sit underneath compression, coding, language modeling, and evaluation intuition."
canonical_url: "https://fanout.sh/daily/2026-07-02-mathematical-theory-of-communication"
md_url: "https://fanout.sh/daily/2026-07-02-mathematical-theory-of-communication.md"
last_updated: "2026-07-02"
access: "public"
---

# A Mathematical Theory of Communication

Shannon's information theory paper is hard but worth anchoring early in the calendar. Entropy, bits, uncertainty, and channel capacity sit underneath compression, coding, language modeling, and evaluation intuition.

## Paper details

- Authors: Claude Shannon

- Venue: Bell System Technical Journal 1948

- Track: ML Math

- Difficulty: Hard

- Reading time: 65 min

- Original paper: https://people.math.harvard.edu/~ctm/home/text/others/shannon/entropy/entropy.pdf

## What you will learn

- How entropy gives uncertainty a quantitative unit.

- Why communication can be separated into source, channel, noise, and code.

- How information-theoretic language appears later in ML objectives.

## Continue exploring

[Explore Information Theory](https://fanout.sh/knowledge-graph?node=ai-research%3Amodule%3Amath-fundamentals&detail=ai-research%3Aconcept%3Aentropy-and-information-theory): Open Fanout's math fundamentals topic for entropy, information theory, cross-entropy, and model objectives.

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