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 section we learned about the basic building blocks such as convolutional layers, pooling 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 Convolutional 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 that 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 section, you'll be able to read some of the research papers from the literature on computer vision and I hope that you might find it satisfying as well. You don't have to do this as a 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.

The LeNEt-5 network which came 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 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.