deep learning for text and sequences using word embeddings another popular and powerful way to associate vector with word is the use of dense word vectorsalso called word embeddings whereas the vectors obtained through one-hot encoding are binarysparse (mostly made of zeros)and very high-dimensional (same dimensionality as the number of words in the vocabulary)word embeddings are lowdimensional floating-point vectors (that isdense vectorsas opposed to sparse vectors)see figure unlike the word vectors obtained via one-hot encodingword embeddings are learned from data it' common to see word embeddings that are -dimensional -dimensionalor , -dimensional when dealing with very large vocabularies on the other handone-hot encoding words generally leads to vectors that are , -dimensional or greater (capturing vocabulary of , tokensin this casesoword embeddings pack more information into far fewer dimensions one-hot word vectorssparse high-dimensional hardcoded word embeddingsdense lower-dimensional learned from data figure whereas word represent ations obtained from one-hot encoding or hashing are sparsehigh-dimensionaland hardcodedword embeddings are denserelat ively lowdimensionaland learned from data there are two ways to obtain word embeddingslearn word embeddings jointly with the main task you care about (such as document classification or sentiment predictionin this setupyou start with random word vectors and then learn word vectors in the same way you learn the weights of neural network load into your model word embeddings that were precomputed using different machine-learning task than the one you're trying to solve these are called pretrained word embeddings let' look at both licensed to