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[Music] hi in this video we will apply narrow networks for text and lets first remember what is text you can think of it as a sequence of characters words or anything else and in this video we will continue to think of text as a sequence of words or tokens and lets remember how bad of words works you have every word and for every distinct word that you have in your data set you have a feature column and youre actually effectively vectorizing each word with one hot encoded vector that is a huge vector of zeros that has only one nonzero value which is in the column corresponding to that particular word so in this example we have very good and movie and all of them are vectorized independently and in this setting you actually for real-world problems you have like hundreds of thousands of columns and how do we get to bag of words representation you can actually see that we can sum up all those values all those vectors and we come up with bag of words vectorization that now corresponds