In the previous section, we looked at turning images into tensors for machine learning, and in this section, we will look at turning the output values, the categories, into tensors for machine learning.
We will cover output classes, what it means to make a discrete prediction, the concept of one-hot encoding; and then we'll visualize what one-hot encoding looks like as an image, and then we'll recap with a data preparation cookbook, which you should use to be able to deal with all kinds of image data for machine learning.
But for now, let's talk about output. When we're talking about digits, there's 0 through 9, so there's ten different classes, and not classes in the object-oriented sense, but classes in the label sense. Now, with these labels being from 0 to 9 as individual digits, the predictions we want to make...
