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So we saw that deep learning had a tremendous part in the ImageNet
competition.
Which allowed them to take 1.5 minute image string deeply on your network and
get amazing performance to predict one of a thousand different categories.
So let's go ahead and show you a little demo of what kind
of categories we're talking about and how cool the predictions were.
So here's an example.
It was the AlexNet frame on that ImageNet data set,
which we then employed as a service that can be queried from this website.
And so every time I click on an image it gets sent to that service
which actually runs on a GPU, so it's fast and it comes back for prediction.
So if I click on this particular image here,
it gets sent to a service that actually hosts in on Amazon AWS.
It comes back for prediction here.
It's hidden, but when I click on it, it tells me what prediction is.
So if I show you this image, it might be unclear what that image is, but
if I click on it, it says parking meter, it turns out to be the right label.
The second best prediction was padlock, Which you can see kind of a padlock.
The parking meter, you got it right.
So that's really quite cool.
Let me show you another example.
For example, this one.
It get shipped off to the service on Amazon WS, comes back for prediction.
And here my prediction, screen, monitor, or [INAUDIBLE] it says it's a monitor, but
I don't know what the difference between a screen and a monitor is, but that's okay.
So there's various images here, I'm just gonna click on a few.
So for example, if I click on this one over here, it gets sent up.
It's really sure, it really thinks it's a spoonbill,
it turns out to be a spoonbill, which is great.
Lastly I'm gonna click on this one over here and
that image gets sent to that service uses a deep learning for GPU and
it says it's a beer bottle or pop bottle, the true label is beer bottle.
And now this all image is in the original ImageNet data set,
I'm sure an image that was not in the original ImageNet data set,
I click on this one here, it gets sent to that service.
On the AWS that we're hosting there.
Comes back for prediction, it says, Labrador retriever, this is my dog.
This is the lab in >> [LAUGH] And
this is the dog over here, in Dato.
And so, as you can see, even for images that were not in the original data set,
you can still get pretty interesting predictions.
Now in your capstone, you want to build a service now not for
predicting images here but for recommendations with deep learning, for
product images and text and host it as a service.
And you'll be able to get a website like this, that anybody can play with, use, and
really see the power of the machine learning that you've been learning about.
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