Embedding layer: Difference between revisions

[[Machine learning]] often deals with [[high-dimensional vector]]s for [[categorical feature]]s, which can be challenging to process and analyze directly. To solve this problem, [[machine learning models]] often incorporate an [[embedding layer]], which transforms the [[input]] data into a lower dimensional space where it's easier to interpret. This embedding layer plays a major role in many machine learning algorithms such as [[neural network]]s and has [[applications]] across various fields from [[natural language processing]] to [[image recognition]].

==Explain Like I'm 5 (ELI5)==

An embedding layer is like a magic machine that simplifies complex ideas into something simpler to comprehend. It does this by looking for patterns in the larger objects and creating smaller versions with all their important information so computers can comprehend what those things are and how best to handle them. We use embedding layers in order to help computers comprehend language, recognize pictures, and suggest items based on our past behavior.