---
title: "Dropout: A Simple Way to Prevent Neural Networks from Overfitting"
description: "Dropout is a compact lesson in turning randomness into regularization. Read it to see how masking neurons during training discourages brittle co-adaptation and approximates an ensemble without paying the cost of running many networks at inference time."
canonical_url: "https://fanout.sh/daily/2026-07-23-dropout"
md_url: "https://fanout.sh/daily/2026-07-23-dropout.md"
last_updated: "2026-07-23"
access: "public"
---

# Dropout: A Simple Way to Prevent Neural Networks from Overfitting

Dropout is a compact lesson in turning randomness into regularization. Read it to see how masking neurons during training discourages brittle co-adaptation and approximates an ensemble without paying the cost of running many networks at inference time.

## Paper details

- Authors: Srivastava et al.

- Venue: JMLR 2014

- Track: ML Math

- Difficulty: Very Easy

- Reading time: 35 min

- Original paper: https://jmlr.org/papers/v15/srivastava14a.html

## What you will learn

- How randomly masking units makes features useful across many different network configurations.

- Why dropout can be understood as training an ensemble of thinned networks with shared weights.

- How keep probability and test-time scaling connect stochastic training to deterministic inference.

## Continue exploring

[Explore Dropout](https://fanout.sh/knowledge-graph?node=ai-research%3Aconcept%3Adropout): Open Fanout's dropout concept node and connect the paper to regularization, overfitting, model averaging, and neural-network training.

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