In the previous section, we learned a bit about what a tensor is. Now, we're going to use that knowledge to prepare image data as tensors for machine learning. First, we'll ask a question: why are we working with data in floating points? Then, we will learn the difference between samples and the data points at the end of them. Finally, we will normalize the data for use in machine learning.
So, why a floating point? Well, the real reason is that machine learning is fundamentally a math optimization problem, and when we're working with floating points, the computer is trying to optimize a series of mathematical relationships to find learned functions that can then predict outputs. So, preparing our data for machine learning does involve reformatting normal binary data, such as an image, into a series of floating point numbers, which isn...
