An embedding is a compressed representation of data such as text or images as continuous vectors in a lower-dimensional space. Embeddings should capture the semantic relationships and similarities between the original uncompressed objects while reducing the dimensionality of the data.
Embeddings are created by training a deep learning model to generate continuous vector representations of complex objects, such as words or images. For example, Word2Vec is an unsupervised learning method that uses a shallow neural network to learn word embeddings.
Embeddings are similar to latent space in that they both are a compressed representation of higher dimensional data that maintains the relationships in the original data. Embeddings are essentially points or coordinates in the latent space.