Embeddings are a way of representing data as points in space where the locations of those points in space are semantically meaningful. They are dense numerical representations of real-world objects and relationships, expressed as a vector. In the context of neural networks, embeddings are low-dimensional, learned continuous vector representations of discrete variables. Embeddings make it easier to do machine learning on large inputs like sparse vectors representing words. Ideally, an embedding captures some of the semantics of the input by placing semantically similar inputs close together in the embedding space.
Embeddings can be learned and reused across models. They are often used to represent complex data types, such as images, text, or audio, in a way that machine learning algorithms can easily process. Once learned, embeddings can be used as features for other machine learning models, such as classifiers or regressors. Several types of embeddings can be used in machine learning.
Embeddings have a wide range of applications, including finding nearest neighbors, input into another model, and visualizations. They are used in a variety of different fields including NLP, recommender systems, and computer vision.
