Every now and then, you need embeddings when training machine learning models. But what exactly is such an embedding and why do we use it? Basically, an embedding is used when we want to map some representation into another dimensional space. Doesn't make things much clearer, does it? So, let's consider an example: we want to train a recommender system on a movie database (typical Netflix use case). We have many movies and information about the ratings of users given to movies.