Hello and welcome back.
This week the first thing we'll do is show you a number of case studies of the factor
convolutional neural networks.
So why look at case studies?
Last week we learned about the basic building blocks such as convolutional
layers, proving layers and fully connected layers of conv nets.
It turns out a lot of the past few years of computer vision research has been on
how to put together these basic building blocks
to form effective convolutional neural networks.
And one of the best ways for
you to get intuition yourself is to see some of these examples.
I think just as many of you may have learned to write codes by reading other
people's codes, I think that a good way to get intuition on how to build
conv nets is to read or to see other examples of effective conv nets.
And it turns out that a net neural network architecture that works well on
one computer vision task often works well on other tasks as well such as maybe on
your task.
So if someone else is training neural network as speak it out in your network
architecture is very good at recognizing cats and dogs and people but
you have a different computer vision task like maybe you're trying to sell
self-driving car.
You might well be able to take someone else's neural network architecture and
apply that to your problem.
And finally, after the next few videos, you'll be able to read some
of the research papers from the theater computer vision and
I hope that you might find it satisfying as well.
You don't have to do this as a class but I hope you might find it satisfying to be
able to read some of these seminal computer vision research paper and
see yourself able to understand them.
So with that, let's get started.
As an outline of what we'll do in the next few videos,
we'll first show you a few classic networks.
The LeNEt-5 network which came from, I guess, in 1980s,
AlexNet which is often cited and the VGG network and
these are examples of pretty effective neural networks.
And some of the ideas lay the foundation for modern computer vision.
And you see ideas in these papers that are probably useful for your own.
And you see ideas from these papers that were probably be useful for
your own work as well.
Then I want to show you the ResNet or conv residual network and
you might have heard that neural networks are getting deeper and deeper.
The ResNet neural network trained a very,
very deep 152-layer neural network that has some very interesting tricks,
interesting ideas how to do that effectively.
And then finally you also see a case study of the Inception neural network.
After seeing these neural networks, l think you have much better intuition about
how to built effective convolutional neural networks.
And even if you end up not working computer vision yourself,
I think you find a lot of the ideas from some of these examples,
such as ResNet Inception network, many of these ideas are cross-fertilizing
on making their way into other disciplines.
So even if you don't end up building computer vision applications yourself, I
think you'll find some of these ideas very interesting and helpful for your work.