In the previous lesson,
you learned what the machine learning paradigm
is and how you use
data and labels and have a computer
in fair the rules between them for you.
You looked at a very simple example where it figured
out the relationship between two sets of numbers.
Let's now take this to the next level by
solving a real problem, computer vision.
Computer vision is the field of having a computer
understand and label what is present in an image.
Consider this slide.
When you look at it, you can interpret
what a shirt is or what a shoe is,
but how would you program for that?
If an extra terrestrial who had never seen
clothing walked into the room with you,
how would you explain the shoes to him?
It's really difficult, if not impossible to do right?
And it's the same problem with computer vision.
So one way to solve that is to
use lots of pictures of clothing and
tell the computer what that's a picture of and then have
the computer figure out the patterns
that give you the difference between a shoe,
and a shirt, and a handbag, and a coat.
That's what you're going to learn
how to do in this section.
Fortunately, there's a data set
called Fashion MNIST which gives
a 70 thousand images
spread across 10 different items of clothing.
These images have been scaled down to 28 by 28 pixels.
Now usually, the smaller the better
because the computer has less processing to do.
But of course, you need to retain
enough information to be sure that
the features and the object can still be distinguished.
If you look at this slide you can still tell
the difference between shirts, shoes, and handbags.
So this size does seem to be ideal,
and it makes it great for training a neural network.
The images are also in gray scale,
so the amount of information is also reduced.
Each pixel can be represented in values from zero to
255 and so it's only one byte per pixel.
With 28 by 28 pixels in an image,
only 784 bytes are needed to store the entire image.
Despite that, we can still see
what's in the image and in this case,
it's an ankle boot, right?