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.
> 
> The LSA community seems to have first used the word “embedding” in Landauer
> et al. (1997), in a variant of its mathematical meaning as a mapping from one space or mathematical structure to another. In LSA, the word embedding seems to have described the mapping from the space of sparse count vectors to the latent space of SVD dense vectors. Although the word thus originally meant the mapping from one space to another, it has metonymically shifted to mean the resulting dense vector in the latent space. and it is in this sense that we currently use the word.
> 
> 
> 

For the more generalized and frequently met questions, I'd like to recommend you read textbooks, especially classic ones.

Refrence:  

[Speech and Language Processing: An introduction to natural language processing](https://web.stanford.edu/%7Ejurafsky/slp3/ed3book.pdf)