Question: I just met a terminology called "embedding" in a paper regarding deep learning. The context is "multi-modal embedding"

My guess: embedding of something is extract some feature of sth,to form a vector.

I couldn't get the explicit meaning for this terminology and that stops me from fully understanding the author's idea and model mechanism

I check the dictionary and search on line,but the explanation is based more on the real life meaning rather than meaning as a machine learning terminology.

And that raise a more generalized and frequently met question, when you find some machine learning terminology/word that you can't understand well, where can you get the solution, some specific way to google? join a machine learning group? raise a question in stack exchange?

Answer: 
In the context of machine learning, an embedding is a low-dimensional, learned continuous vector representation of discrete variables into which you can translate high-dimensional vectors. Generally, embeddings make ML models more efficient and easier to work with, and can be used with other models as well.

Typically, when I stumble upon jargon I'm not familiar with I first turn to Google, and if it can't be found I ping my colleagues and data science forums.
**For me embedding is used to represent big sparse matrix into smaller dimensions, where each dimension(feature) represent a meaningful association with other elements in the embedding matrix.**

Consider an example of NLP. Where each sentence broken down into words(also called token). Such set of different words make a vocabulary for NLP. Generally vocabulary have millions of words. All such words can be uniquely represented as OneHotEncoding.

> 
> **Demerits of OneHotEncoding representation of words:**
> 
> 
> 1. In case of large vocabulary, OneHotEncoding representation needs a
>  big chunk of memory and computationally become very expensive.
> 2. OneHotEncoding is used to represent categorical values, where each entity is 
>  independent to other one, whereas words in vocabulary represent some association in 
>  terms of similar meanings or in some other way. OneHotEncoding not utilizing that 
>  capability for NLP.
> 
> 
> 

In order to overcome both the issues, we use word Embedding, where each word represented in lesser dimension, where each dimension represent some sort of features and hence each dimension will have some values.