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What is an embedding in ML?

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. 

How are embeddings created?

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.

How are embeddings related to latent space?

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.

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