Why look at case studies?

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From the course by deeplearning.ai

Convolutional Neural Networks

18372 ratings

deeplearning.ai

Convolutional Neural Networks

18372 ratings

Course 4 of 5 in the Specialization Deep Learning

This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images. You will: - Understand how to build a convolutional neural network, including recent variations such as residual networks. - Know how to apply convolutional networks to visual detection and recognition tasks. - Know to use neural style transfer to generate art. - Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data. This is the fourth course of the Deep Learning Specialization.

From the lesson

Deep convolutional models: case studies

Learn about the practical tricks and methods used in deep CNNs straight from the research papers.

Why look at case studies?3:07

Classic Networks18:18

Why ResNets Work9:12

Networks in Networks and 1x1 Convolutions6:39

Inception Network Motivation10:14

Inception Network8:45

Meet the Instructors

Andrew Ng
CEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist,Baidu and founding lead of Google Brain
Head Teaching Assistant - Kian Katanforoosh
Lecturer of Computer Science at Stanford University, deeplearning.ai, Ecole CentraleSupelec
Teaching Assistant - Younes Bensouda Mourri
Mathematical & Computational Sciences, Stanford University, deeplearning.ai

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.

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