Embedding.md (462B)
1 # Embedding 2 3 ML P722 4 5 **Definition:** Embeddings are a high dimensional dense representation of data. 6 7 When using one hot encoding we get a sparse output with only one 1 and the rest 0s. However, when using embeddings all representations are high dimensional and don't have sparsity. 8 9 Embeddings are generally trainable so while they are initialized, over time they will become more representative of the underlying data and how it relates to other embeddings.