Deep learning is exciting because it learns these complex features of images.
And as we discussed earlier.
They've had tremendous impact over the recent years in a variety of
computer vision applications.
Let me show you a couple of early examples.
So, on the top of the slide here, what you see is an example of identifying
traffic signs based on neural networks.
So these are a data of German traffic signs and
the idea is for every image, identify what sign it is.
And they were able to get 99.5% accuracy using a deep neural network,
which is pretty cool.
On the bottom there, you see an example that came out of some work from Google on
identifying the house numbers based on what's called Street View data.
This is the data that Google uses driving around cars and
photographing all sorts of streets around the world.
And you see the images are pretty complex, and
still they're able to get 97.8% accuracy on the per character level.
Now these were exciting results.
But the one that changed everything.
The really excited field happened in 2012.
So for many years, there was a image competition called ImageNet.
And in 2012, the ImageNet competition included 1.2 million
training images from about 1,000 different categories.
And the idea was can you classify this image.
Is it not just a dog, but is it a golden retriever or a labrador?
Very, very fine level detail.
Now there's many teams competing.
These are the top 3 teams.
A team called OXFORD_VGG, which got pretty decent accuracy.
So if you look at their top five guesses can you get the right thing
in five guesses.
They were getting only about 25% error.
There's a thing called ISI that did a little bit better.
And those are using traditional techniques like the SIFT[1],
a little bit more elaborate, kinda like that.
Now that year, is a team called SuperVision.
That team used a deep, neural network and had a huge gain over the competitors and
that performance really sparked a lot of excitement of using deep neural networks
in computer vision because if they have to just use hand coded features,
you'd learn them automatically.
Now that neural network that won the competition with the SuperVision team was
called the AlexNet neural network and I'm showing here an image from their paper,
and that neural network involved 8 layers, 60 million parameters, and
was only possible because of new training algorithms the could
deal with lots of images and lots parameters, and
the GPU implementation that would really scale to large data sets.
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