---
title: "The Perceptron"
description: "The Perceptron is a good July 1 starting point because it gives learners the original linear-classifier idea before modern depth, attention, or scale enter the story."
canonical_url: "https://fanout.sh/daily/2026-07-01-perceptron"
md_url: "https://fanout.sh/daily/2026-07-01-perceptron.md"
last_updated: "2026-07-01"
access: "public"
---

# The Perceptron

The Perceptron is a good July 1 starting point because it gives learners the original linear-classifier idea before modern depth, attention, or scale enter the story.

## Paper details

- Authors: Frank Rosenblatt

- Venue: Psychological Review 1958

- Track: AI Research

- Difficulty: Very Easy

- Reading time: 30 min

- Original paper: https://bpb-us-e2.wpmucdn.com/websites.umass.edu/dist/a/27637/files/2016/03/rosenblatt-1957.pdf

## What you will learn

- How a weighted input rule turns examples into a learned decision boundary.

- Why early neural-network optimism centered on simple trainable units.

- What limitations later architectures had to overcome.

## Continue exploring

[Explore Neural Networks](https://fanout.sh/knowledge-graph?node=ai-research%3Amodule%3Aneural-network-from-scratch): Start Fanout's neural-network path and connect the perceptron to neurons, weights, gradients, and modern layers.

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