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.
Yann LeCun Interview
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Convolutional Neural Networks
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Foundations of Convolutional Neural Networks
Learn to implement the foundational layers of CNNs (pooling, convolutions) and to stack them properly in a deep network to solve multi-class image classification problems.
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Andrew NgCo-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain
Head Teaching Assistant - Kian KatanforooshAdjunct Lecturer at Stanford University, deeplearning.ai, Ecole Centrale Paris
Teaching Assistant - Younes Bensouda MourriMathematical & Computational Sciences, Stanford University, deeplearning.ai
Hi Yann, you've been such a leader for Deep Learning for so long,
thanks a lot for doing this with us. >> Well, thanks for
having me. >> So,
you've been working on neural nets for a long time.
I would love to hear your personal story of how you got started in AI, how did you
networking with neural networks? >> So, I was always interested in
intelligence, in general, the origins of intelligence in humans.
Got me interested into human evolution when I was a kid.
>> That was in France?
>> It was in France, yeah.
I was in middle school or something and
I was interested in technology, space, etc.
My favorite movie was 2001: A Space Odyssey.
You had intelligent machines, space travel, and
human evolution as kind of the themes that was what I was fascinated by.
And the concept of intelligent machines I think really kind of appealed to me.
And then I studied electrical engineering.
And when I was at school, I was maybe in second year of engineering school,
I stumbled on a book, which was actually a philosophy book.
It was a debate between Noam Chomsky, the computational linguist at MIT,
and Jean Piaget who is a cognitive psychologist sort
of psychology of child development in Switzerland.
And it was basically a debate between nature and nurture,
where Chomsky arguing for the fact that language has a lot of innate structure,
and Piaget saying a lot of it is learned, and etc.
And on the side of Piaget was a transcription of a person who,
each of these guys sort of brought their teams of people to argue for their side.
And on the side of Piaget was Seymour Papert from MIT, who had worked
on the perceptron model, one of the first machines capable of running.
And I never heard of the perceptron, and I read this article that say,
machine capable of running, that sounds wonderful.
And so I started going to several university libraries and searching for
everything I could find that talked about the perceptron and realized there was
a lot of papers from the 50s, but it kind of stopped at the end of the 60s,
with a book that was co-authored by the same Seymour Papert.
>> What year was this?
>> So this was in 1980,
roughly? >> Right.
>> And so I did a couple of projects with
some of the math professor in my school on kind of neural nets, essentially.
But there was no one there I could talk to who had worked on this,
because the field basically had disappeared in the meantime, right?
Since 1980, nobody was working on this.
And experimented with this a little bit,
writing kind of simulation software of various kinds, reading about neuroscience.
When I finished my engineering studies, I studied chip design.
I'm good at site design at the time, so it's something completely different.
And when I finished I really wanted to do research on this and
I figured out already that at the time the important question was how you train
neural nets with multiple layers.
It was pretty clear in the literature of the 60s that that was the important
question that had been left unsolved and their idea of hierarchy and everything.
I'd read Fukushima's article on the neocognitron, right?
Which was this sort of hierarchical architecture very similar to now what we
now call convolutional nets, but without really backprop style learning algorithms.
And I met people who were in a small independent club in France.
They were interested in what they called at the time, Automata Networks.
And they gave me a couple papers,
the people on functional networks which is not very popular anymore.
But it's the first associative memories with neural net and that paper can revive
the interest of some research committees into neural net in the early 80s.
Where by mostly physicists and condense matter physicists and
a few psychologists, it was still not okay for engineers and
computer scientists to talk about neural nets.
And they also should be another paper that had just been distributed
as a pre-print, whose title was Optimal Perceptual Inference.
And this was the first paper on Boltzmann machines by Geoff Hinton and
Terry Sejnowski.
It was talking about hidden units.
It was talking about, basically, the part of learning,
multilayer neural nets are more capable than just classifiers.
So I said,
I need to meet these people [LAUGH]. >> Wow.
>> Because they're only
interested in the right problem.
And a couple of years later, after I started my PhD, I participated in
a workshop in Le Juch that was organized by the people I was working with.
And Terry was one of the speakers at the workshop, so
I met him at that time. >> It was like early 80s now.
>> This is 1985, early 1985.
So I met Terry Sejnowski in 1985 in the workshop in France in Le Juch and
a lot of people were there, founders of early neural net, jump up field and,
a lot of people working on theoretical neuroscience and stuff like that.
It was a fascinating workshop.
I met also, a couple of people from Bell Labs who eventually hired me at Bell Labs,
but this was several years before I finished my PhD.
So I talked to Terry Sejnowski and I was telling him about what I was working on
which was some version of backprop at the time.
This is before backprop was a paper and
Terry was working on net talk at the time.
This was before the Rumelhart,
Hinton, Williams paper on backprop had been published.
But he was friends with Geoff, this information was circulating,
so he was already working on trying to make this work for net talk,
but he didn't tell me. >> I see.
>> And he went back to US and
told Geoff there is some kid in France who's working on the same stuff
we're working on. >> I see.
>> [LAUGH] And then a few months later,
in June, there was another conference in France where Geoff was a keynote speaker.
And he gave a talk on Boltzmann machines.
Of course, he was working on the backprop paper.
And he gave this talk, and
then there was 50 people around him who wanted to talk to him.
And the first thing he said to the organizer is,
do you know this guy, Yann LeCun?
And it's because he had read my paper in the proceedings that was written
in French.
And he could sort of read French and he could see the math and
he could figure out what sort of backprop, and so we had lunch together and
that's how we became friends. >> I see, well.
>> [LAUGH]
>> So that's because multiple groups
independently reinvented or invented backprop pretty much.
>> Right, well, we realized that the whole
idea with Chain Rule or what the optimal control people call the joint state
method which is really the context in which backprop was really invented.
This in context of optimal control back in the early 60s.
This idea that you could use graded descent basically with multiple stages
is what backprop really is and that popped up in various contexts at various times.
And but I think the Rumelhart, Hinton, Williams paper is
the one that popularized it. >> I see, yeah, no, cool, yeah.
And then fast forward a few years, you wound up at AT&T Bell Labs,
where you invented, among many things, the net, which we talk about in the course.
And I remember when way back, I was a summer intern at AT&T Bell Labs,
where I worked with Michael Kerns and a few others, and
of hearing about your work even back then.
So tell me more about your AT&T, the net, experience.
>> Okay, so what happened is,
I actually started working on convolutional net when I was A postdoc,
University of Toronto, chief intern.
I did the first experiment, I wrote the code there, and
I did the first experiments there that showed that,
if you had a very small data set.
The data set I was training on, there was no or anything like that back then.
So I drew a bunch of characters with my mouse.
I had an Amiga, a personal computer, which was the best computer ever.
And I drew a bunch of characters and then used that.
I did augmentation to kind of increase it, and
then used that as a way to test performance.
And I compared things like fully connected nets,
locally connected nets without shared weights.
And then shared weight networks.
Which was basically the first comment.
And that worked really well for relatively small data sets, could show that you get
better performance and no over-training with conventional architecture.
And when I go to Bell Labs in October 1988,
the first thing I did was first, scale up the network,
because we had faster computers a few months before I go to Bell Labs.
My boss at the time, Larry Jackal, who became a department head of
said we should order a computer for you before you come.
Where do you want?
I say well, here Toronto, there is which was the stuff.
It'd be great if we had one.
And they ordered one and I had one for myself.
At University of Toronto it was one for the entire department, right?
One just for me, right?
And so Larry told me he said, you know at Bell Labs you don't get famous by saving
money. >> [LAUGH]
>> So that was awesome, and they had been
working already for awhile on character recognition.
They had this enormous data set called USDS that had 5,000 training samples.
[LAUGH] And so immediately I trained a net, which was in the net one, basically.
And trained it on this data set and
got really good results, better results than the other methods.
They had tried on it, and that other people had tried on it is that so
that, we knew we had something fairly early on.
This was within three months of me joining Bell Labs.
And so that was the first version of commercial net where
we had a convolution with stride, and we did not have separate and
pulling layers. >> Mm-hm.
>> So each convolution was actually
directly.
And the reason for
this is that we just could not afford to have a convolution at every location.
There was just too much computation. >> I see.
>> [COUGH] So, the second version had
a separate convolution and pulling the air in something.
I guess that's the one that's called one really.
So we published a couple papers on this at competitions in Nips.
And so, interesting story, did you ever talk to Nips about this work?
And Jeffrey Ton was in the audience, and then you know I came back to my seat,
I was sitting next to him and he said, there's one bit of information in your
talk which is that, if you do all the sensible things,
it actually works. >> [LAUGH]
>> Then that showed the after deadline of
work went on to make history because it became widely adopted.
These ideas became widely adopted for
reading cheques and- >> Yeah,
the bigger value adopted within AT&T but not very much outside.
And I think it's a little difficult for
me to really understand why, but the simple factor [INAUDIBLE].
So this was back in the late 80s, and there was no Internet.
We had email, we had FTP, but there was no Internet, really.
No two labs were using the same software or hardware platform, right?
Some people are at some workstations, others had other machines,
some people were using PCs or whatever.
There was no such thing as Python or MATLAB or anything like that, right?
People are writing their own code.
I had spent a year and a half basically writing,
me and when he was still a student.
We're working together, and we spent a year and
a half basically just writing a neural net simulator.
And at the time because there was no match-up with Python.
You had to kind of write your own interpreter, right?
To kind of control it.
So we want our own list of interpreter.
And so all the networks written in list using a numerical back hand.
Very similar to what we have now with blocks that you can interconnect and
instead of many differentiation and all that stuff that we;re familiar now,
with torsion by torsion, tensile flow and all those things.
So then we developed a bunch of applications.
We got together with a group of engineers.
Very smart people.
Some of them were like theoretical
physicists who kind of turned engineer at the Bell Labs.
Chris Dodgers was one of them who then had to
distinguished career at Microsoft research afterwards.
And Krieg Nolan.
But keep on and we're collaborating with them to kind of make this
technology practical. >> I see.
>> And so
together we developed this characterization systems.
And that meant integrating, convolutional net with things like,
similar to things like we now call CRFs for interpreting sequences of
characters not just individual address. >> Yeah,
right to the net paper had partially under neural network and
partially under atomic machinery >> Right, to pull it together?
>> Yeah, that's right.
And so the first half on the paper is on convolutional nets, and
the paper is mostly cited for that.
And then the second half, very few people have read it,
[LAUGH] and it's about sort of sequence level, discriminative running, and
basically structure prediction with that normalization.
So it's very similar to CRF, in fact. >> Fascinating
>> You know with PTCRFS over the years.
So that was very successful, except that the day we were
celebrating the deployment of that system in major bank,
we worked with this group that I was mentioning that was kind of
doing the engineering of the whole system.
And then another product group in a different part of the country
that belonged to a subsidiary of AT&T called NCR.
So this is the- >> [CROSSTALK]
>> National Cash Register, right.
They also build large ATM machines, and
they build large check reading machines for banks.
So they were the customers, if you want.
They were using our check billing systems.
And they had deployed it in a bank.
I can't remember which bank it was.
They deployed those, so there were ATM machines in a French book.
So they could read the check you would deposit, and we were all at
a fancy restaurant celebrating the department of this thing where,
when the company announced that it was breaking itself up.
So this was 1995 and
AT&T announced that it was breaking itself up into two companies.
So there was AT&T, and then there was Lucen Technologies, and NCR.
So NCR was spun off, and Lucent Technologies was spun off.
And the engineering group went with Lucent Technologies, and the product group,
of course, went with NCR.
And the sad thing is that the AT&T lawyers in their infinite wisdom
assigned the patents, there was a patent on covolutional net
which is thankfully expired. >> I see [LAUGH].
>> [LAUGH] Expired in 2007.
About ten years ago.
And they signed patent to NCR, but there was nobody in NCR who actually
knew even what a convolutional net was really.
And so the patent was in the hands of people who had no idea what they had.
And we were in a different company that now could not really develop
the technology, and our engineering team was in a separate company,
because we went with AT&T and engineering went with Lucent, and
the product group went with NCR.
So it was a little depressing [LAUGH]. >> So in addition to your early work,
when your networks were Part, you kept persisting on neural networks
even when there was some sort of winter for neural net.
So what was like that? >> Well, so I persisted and
didn't persist in some ways.
I was always convinced that eventually those techniques would come back to
the fore, and sort of people would figure out how to use them in practice, and
it would be useful.
So I always had that in the back of my mind.
But in 1996, when AT&T broke itself up, and
all of our work on character recognition, basically,
was kind of broken up because the part of the group went in separate way,
I was also promoted to department head, and I had to figure out what to work on.
And this was the early days of the Internet, we're talking 1995.
And I had the idea somehow that one big problem about
the emergence of the Internet was going to be to bring
all the knowledge that we had on paper to the digital world.
And so I started, actually, a project called DjVu, D-J-V-U,
which was to compress scanned documents, essentially,
so they could be distributed over the Internet.
And this project was really fun for a while, and had some success, although AT&T
really didn't know what to do with it. >> Yeah, I remember that, really helping
dissemination of online research papers. >> Yeah, that's right, exactly.
And we scanned the entire proceedings of NIPS, and we made them available online-
>> I see, I remember that.
>> To kind of demonstrate how that worked.
And we could compress high resolution pages to just a few kilobytes.
>> So ConvNet,
starting from some of your much earlier work has now come and
pretty much taken over the field of computer vision, and
starting to encroach significantly into even other fields.
So just tell me about how you saw that whole process.
>> [LAUGH] So
to tell you how I thought this was going to happen early on.
So first of all, I always believed that this was going to work.
It required fast computers and lots of data, but I always believed, somehow,
that this was going to be the right thing to do.
What I thought, originally, when I was at Bell Labs, that there was going to be some
sort of continuous progress along these directions as machines got more powerful.
And we were even designing chips to run convolutional nets at Bell Labs, but
now those are actually in hospital graph separately had two different chips for
running convolutional nets really efficiently.
And so we thought there was going to be a kind of a pick up of this, and
kind of growing interest and sort of continuous progress for it.
But in fact, because of the sort of interest for neural nets,
sort of dying in the mid-90s, that didn't happen.
So there was kind of a dark period of six or seven years between 1995 roughly and
2002 when basically nobody was working on this.
In fact, there was a little bit of work.
There was some work at Microsoft in the early 2000s on using
convolutional nets for Chinese character recognition.
>> Group, yeah, exactly.
And there was some other small work for face detection and
things like this in France, and in various other places, but it was very small.
I discovered actually recently that a couple groups that came
up with ideas that are essentially very similar to convolutional nets, but
never quite published it the same way for medical image analysis.
And those were mostly in the context of commercial systems.
And so it never quite made it to the population.
I mean, it was after our first work on convolutional nets, and they were
not really aware of it, but it sort of developed in parallel a little bit.
So several people got kind of similar ideas several years interval.
But then I was really surprised by how fast
interest picked up after the ImageNet- >> 2012
>> In 2012, so it's the end of 2012.
It was kind of a very interesting event at ECCV,
in Florence, where there was a workshop on ImageNet.
And they already knew that had won by a large margin.
And so everybody was waiting for talk.
And most people in the computer vision community had no idea what a convolutional
net was.
I mean, they heard me talk about it.
I actually had an invited talk at CVPR in 2000 where I talked about it,
but most people had not paid much attention to it.
Senior people did, they knew what it was, but
the more junior people in the community were really, had no idea what it was.
And so just gives his talk, and he doesn't explain what a convolutional net is
because he assumes everybody knows, right?
because he comes from a so he says, here's how everything is connected,
and how we transform the data and what results we get.
Again, assuming that everybody knows what it is.
And a lot of people are incredibly surprised.
And you could see the opinion of people changing as he was kind of giving
his talk, very senior people in the field. >> So you think that workshop was
a defining moment that swayed a lot of the computer vision community.
>> Yeah, definitely.
>> That's right, yeah.
>> That's the way it happened, yeah,
right there. >> So today, you retain a faculty position
at NYU, and you also lead FAIR, Facebook AI Research.
I know you have a pretty unique point of view on how corporate research should
be done.
Do you want to share your thoughts on that?
>> Yeah, so I mean, one of the beautiful
things that I managed to do at Facebook in the last four years is that I was given
a lot of freedom to setup FAIR the way I thought was the most appropriate,
because this was the first research organization within Facebook.
Facebook is a sort of engineering-centric company.
And so far was really focused on sort of survival or short-term things.
And Facebook was about to turn ten years old, had a successful IPO.
And was basically thinking about the next ten years, right?
I mean, Mark Zuckerberg was thinking, what is going to be important for
the next ten years?
And the survival of the company was not in question anymore.
So this is the kind of transition where a large company can start to think, or
it was not such a large company at the time.
Facebook had 5,000 employees or so, but it had the luxury to
think about the next ten years and what would be important in technology.
And Mark and his team decided that AI was going to be a crucial
piece of technology for connecting people, which is the mission of Facebook.
And so they explored several ways to kind of build an effort in AI.
They had a small internal group, engineering group,
experimenting with convolutional nets and stuff that were getting really good
results in face recognition and various other things, which peaked their interest.
And they explored the idea of hiring a bunch of young researchers, or
acquiring a company, or things like this.
And they settled on the idea of hiring someone senior in the field, and
then kind of setting up a research organization.
And it was a bit of a culture shock, initially, because
the way research operates in the company is very different from engineering, right?
You have longer time scales and horizon.
And researchers tend to be very conservative about the choice of places
where they want to work.
And I made it very clear very early on that research needs to be open,
that researchers need to not only be encouraged to publish, but
be even mandated to publish.
And also be evaluated on criteria that are similar to
what we used to evaluate academic researchers.
[COUGH] And so what Mark and Mike Schroepfer, the CTO of the company,
who is my boss now, said, they said, Facebook is a very open company.
We distribute a lot of stuff in open source.
Schroepfer, the CTO,
comes from the open source world. >> Mozilla.
>> He was from Mozilla before that,
and a lot of people came from that world.
So that was in the DNA of the company, so that made me
very confident that we could kind of set up an open research organization.
And then the fact that the company is not obsessively compulsive about IP as some
other companies are makes it much easier to collaborate with universities and
have arrangements by which a person can have a foot in industry and
a foot in academia. >> And you find that valuable,
yourself? >> Absolutely, yes.
Yeah, so if you look at my publications over the last four years,
the vast majority of them are publications with my students at
NYU. >> I see.
>> Because at Facebook,
I did a lot of organizing the lab, hiring,
set the direction and advising, and things like this.
But I don't get involved in individual research projects to get my name on
papers.
And I don't care to get my name on papers anymore, but it's-
>> It's not sending out someone else to do
your dirty work rather than doing all the dirty work yourself.
>> Exactly, and you never want to put
yourself, you want to stay behind the scene.
You don't want to put yourself in competition with people in your lab in
that case. >> I'm sure you get asked this a lot but
hoping you answer for all the people watching this video as well.
What advice do you have for someone wanting to get involved in the,
break into AI? >> [LAUGH] I mean,
it's such a different world now than when it was when I got started.
But I think what's great now is it's very easy for people to get involved at some
level, the tools that are available are so easy to use now, in terms of whatever.
You can have a run through on the cheap computer in your bedroom, [LAUGH] and
basically train your conventional net or your current net to do whatever,
and there's a lot of tools.
You can learn a lot from online material about this without, it's not very onerous.
So you see high school students now playing with this right?
Which is kind of great, I think and they certainly are growing interest
from the student population to learn about machine learning and AI and
it's very exciting for young people and I find that wonderful I think.
So my advice is, if you want to get into this, make yourself useful.
So make a contribution to an open source project, for example.
Or make an implementation of some standard algorithm that you can't find the code of
online, but you'd like to make it available to other people.
So take a paper that you think is important,
and then re-implement the algorithm, and then put it open source package,
or contribute to one of those open source packages.
And if the stuff you write is interesting and useful, you'll get noticed.
Maybe you'll get a nice job at a company you really wanted a job at,
or maybe you'll get accepted in your favorite PhD program or things like this.
So I think that's a good way to get
started. >> So
open source contributions is a good way to enter the community, give back to learn.
>> Yeah, that's right,
that's right. >> Thanks a lot Jan that was fascinating,
I've known you for many years and it's still fascinating to hear all these
details of all the stories that have gone in over the years.
>> Yeah, there's many stories like this
that, reflecting back at the moment when they happen you don't realize,
what importance it might take 10 or 20 years later.
>> Yeah, thank you.
>> Thanks.
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