---
title: "Meaning & Key Takeaway of: If you only have 10 examples of…"
description: "\"If you only have 10 examples of something, it's going to be hard to make deep learning work. If you have 100,000 things you care about, records or what..."
image: "https://sms4smile.com/quotes/i/d23/jeff-dean-10577_deep_if-you-only-have-10-examples-of-somethin_1800890.jpg"
---

#  If you only have 10 examples of something, it's going to…

![“If you only have 10 examples of something, it's going to be hard to make deep learning work. If you have 100,000 things you care about, records or whatever, that's the kind of scale where you should really start thinking about these kinds of techniques.” quote by Jeff Dean](https://sms4smile.com/quotes/i/d23/jeff-dean-10577_deep_if-you-only-have-10-examples-of-somethin_1800890.jpg)   [Download](https://sms4smile.com/quotes/i/d23/jeff-dean-10577_deep_if-you-only-have-10-examples-of-somethin_1800890.jpg) [Open image](https://sms4smile.com/quotes/i/d23/jeff-dean-10577_deep_if-you-only-have-10-examples-of-somethin_1800890.jpg)

> “If you only have 10 examples of something, it's going to be hard to make deep learning work. If you have 100,000 things you care about, records or whatever, that's the kind of scale where you should really start thinking about these kinds of techniques.”

[Jeff Dean](https://sms4smile.com/quotes/author/jeff-dean/)

## About This Quote

This interpretation was drafted with AI assistance. It is one reading of the quote, not the author's own explanation.

Deep learning needs large data; small datasets limit model performance, so seek scale for effective AI.

**In simple terms:** Big data is essential for deep learning success.

Key Takeaway

Gather more data or use alternative methods.

Themes

AI deep learning data scale model training technology

Mood

analytical pragmatic

Type

technical educational

When to use this quote

- research projects
- startup product development
- academic studies
- industry AI pipelines
- data collection initiatives

Key Concepts

big data algorithmic efficiency resource constraints

Questions to Reflect On

- How can you obtain more data responsibly?
- What alternatives exist for low‑data scenarios?

A Different Perspective

Large data may be costly or unavailable; consider privacy and bias.

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