The fashion and this data set was created by [inaudible] and [inaudible].
I think it's really cool that you're already able to
implement a neural network to do
this fashion classification task.
It's just amazing that
large data sets like this are readily
available to students so that they can
learn and it make it really easy to learn.
And in this case we saw with just a few lines of code,
we were able to build a DNN that
allowed you to do this classification of clothing
and we got reasonable accuracy with it but it
was a little bit of a naive
algorithm that we used, right?
We're looking at every pixel in every image,
but maybe there's ways that we can make it
better but maybe looking at features of
what makes a shoe a shoe and what makes
a handbag a handbag. What do you think?
Yeah. So one of the ideas that make
these neural networks work much better is to
use convolutional neural networks,
where instead of looking at every single pixel and say,
"Oh, that pixel has value 87,
that has value 127."
So is this a shoe or is this a hand bag?
I don't know.
But instead you can look at a picture and say,
"Oh, I see shoelaces and a sole."
Then, it's probably shoe or say,
"I see a handle and rectangular bag beneath that."
Probably a handbag. So confidence hopefully,
we'll let the students do this.
Sure, what's really interesting about
convolutions is they sound
complicated but they're actually quite straightforward,
right?
It's a filter that you pass over
an image in the same way as if you're doing sharpening,
if you've ever done image processing.
It can spot features
within the image as you've mentioned.
With the same paradigm of just data labels,
we can let a neural network
figure out for itself that it should look for
shoe laces and soles or handles in bags and
just learn how to detect these things by itself.
So shall we see what impact that
would have on Fashion MNIST?
So in the next video, you'll learn about
convolutional neural networks and get to use it
to build a much better fashion classifier.