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Now, neural networks provide some exciting results, however,
they do come with some challenges.
So, on the pro side, they really enable you to represent this non-linear
complex features and they have impressive results,
not just in computer vision, but in some other areas like speech recognition.
So systems like Siri on the phone and others use the neural
networks behind the scene, as well as some text analysis tasks.
And its potential for much more impact in the wide range of areas.
Now they do come with some challenges.
And to understand those challenges,
we need to talk about the workflow of training a neural network.
So you need to start with lots and lots and lots of data.
And that data has to be labeled.
Every image has to have, what dog was in the image, is it a labrador,
is it a poodle, is it a golden retriever, chihuahua and so on.
And that requires a lot of human annotation and that can be hard.
But we start to [INAUDIBLE] some of the images.
We feed it, we split them into training tasks or validation sets as we discussed,
and we learned that deep neural network, and that can take quite a while.
But once we validate, we realize that that complex eight layer structure of
60 million parameters wasn't exactly what we needed and
we need to revise it, or we need to adjust parameters or change how we learn it.
And we have to iterate again and again and again.
And in fact, to get that winning neural network, the one,
they really needed to connect various layers with different representations and
lots of complexes in the learning algorithm, so that was hard.
So although there's some great pros, neural networks,
they also come with some cons too.
They require lots of data to get great performance, they're computationally
expensive, even with GPUs they can be computationally expensive.
And they're extremely hard to tune, so you have a lot of choices, the more layers
you use, how many layers or parameters you use and that can be really hard.
So if you combine the computational choices and costs with so
many things too soon, you end up with an incredibly hard process for
figuring out what neural network to use.
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