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Table Of Contents
The Deep Learning with Keras Workshop
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We build models so that we can learn something about the data we are training on and about the relationships between the features of the dataset. This learning can inform us when we encounter new observations. However, we must realize that the observations we interact within the real world and the format of the data that's needed to train machine learning models are very different. Working with text data is a prime example of this. When we read the text, we are able to understand each word and apply the context that's given by each word in relation to the surrounding words—not a trivial task. However, machines are unable to interpret this contextual information. Unless it is specifically encoded, they have no idea how to convert text into something that can be a numerical input. Therefore, we must represent the data appropriately, often by converting non-numerical data types—for example, converting text, dates, and categorical variables into...