Training updates a table of vectors so identifiers useful in similar contexts acquire useful geometric relationships. The selected vectors become continuous inputs that later neural network layers can process.
An embedding layer maps discrete identifiers such as tokens or categories to learned dense vectors.
Training updates a table of vectors so identifiers useful in similar contexts acquire useful geometric relationships. The selected vectors become continuous inputs that later neural network layers can process.