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Letamp;#39;s talk about how to handle text in a deep model. Imagine that you want to classify a document. Is it talking about politics, or business, or science maybe? Youamp;#39;re going to have to look at the words in that document to figure that out. Words are really difficult. There are lots of them and most of them you never, ever see. In fact the ones that you rarely see, tend to be the most important ones. If you know that your document contains duh, you donamp;#39;t know much at all. But if you know that it contains retinopathy, you know that itamp;#39;s probably a medical document. Retinopathy appears with a frequency of 0.0001% in English. Itamp;#39;s even possible that youamp;#39;ve never seen that word yourself before. For deep learning, rare events like that are a problem. We like to have lots of training examples to learn from.
