Most neural NLP applications do something different, however. Instead of using hand-built human-engineered features as the input to our classifier, we draw on deep learning's ability to learn features from the data by representing words as word2vec or GloVe embeddings (Chapter 6). For a text with n input words/tokens w 1 , ..., w n , the input vector will be the concatenated embeddings of the n words: [e w 1 ; ...; e w n ]. If we use the semicolon ';' to mean concatenation of vectors, the equation for our sentiment classifier will be (as sketched in Fig. 7.11 The idea of using word2vec or GloVe embeddings as our input representationand more generally the idea of relying on another algorithm to have already learned an embedding representation for our input words -is called pretraining. Using pretraining pretrained embedding representations, whether simple static word embeddings like word2vec or the more powerful contextual embeddings we'll introduce in Chapter 11, is one of the central ideas of deep learning. (It's also, possible, however, to train the word embeddings as part of an NLP task; we'll talk about how to do this Section 7.7 in the context of the neural language modeling task.)