# Frequently Asked Questions About Google's Machine Learning Glossary

This document asks and answers a few leading questions about
Google's Machine Learning Glossary.

## How do I propose a new term?

Click the **Send Feedback** button at the top or bottom of the Machine Learning
Glossary. Then, tell us what term(s) are missing.

## Can I report a confusing definition?

Absolutely! Click the **Send Feedback** button and tell us.

## Will you email me a response to my feedback?

Unfortunately not, but we really do review all feedback.

## Who writes the definitions?

A Google team of technical writers, researchers, and software engineers writes
and reviews each definition.

## How often do you add new definitions?

We release batches of new terms three to four times per year. Additionally, we
frequently make minor changes to existing definitions.

## Do you provide subglossaries?

Yes, you can filter the glossary by choosing a topic from the **Glossary**
drop-down in the top navigation bar. For example, the **Clustering**
subglossary focuses on the terms relevant to, well, clustering.

## Who is the target audience?

We aim the "Fundamentals" terms (those marked with a 🐣 icon) at
machine learning newcomers. At the other extreme, we aim certain advanced terms
at experienced machine learning practitioners.

## Why does my organization define terms differently than your glossary?

That's a pretty common problem in relatively new fields like machine learning.
We'll just offer our apologies and promise that machine learning terminology
will probably never be completely standardized.

## Why do some definitions read like encyclopedia entries?

A single sentence definition suffices for some terms, but other terms require
extensive explanations, images, comparisons, examples, math formulas, and so on.