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hello iamp;#39;m john linz and in this video i will be discussing word vectors for natural language processing as well as the skip gram model to construct said word vectors now the reason why we create word effect models to create these word vectors is because computers donamp;#39;t actually understand words they can only understand numerical values so the challenge is to convert these words into vectors or some sort of numerical representation so that not only computers understand them but they understand how itamp;#39;s relevant to surrounding words with that said letamp;#39;s get into it so first we have to discuss one hot encoding now one hot encoding is a way to represent words using one hot encoding vectors um now one hot encoding vectors donamp;#39;t carry any uh intrinsic meaning to the words it simply tells the computer what each word is it identifies unique words so in this example here we have a corpus with three words apple orange and banana and since there are three u
