Hi, guys, and welcome to the course, this course is called modern computer vision, and they named
it modern computer vision because it is in fact the most modern up to the computer vision, of course,
online right now.
It's a very comprehensive course because it encapsulates all of the open TV, all of the cool, open
TV, classical computer vision theory, as well as all the deep learning modern day terry.
And that's a wide topic.
As you can see, this is a very big course.
It's twenty seven hours, in fact, and I may add content to one little one.
So now let's get started.
So firstly, what exactly is computer vision?
Well, it's an interdisciplinary feel that aims to enable computers or software to gain an understanding
of what is being seen in images and videos.
So many of you may have seen this movie.
It's one of my favorite movies.
Terminator two Judgment Day where Arnold is a robot, the T100 robot.
I believe what too many do, I should say, and this is basically ahead of what he's seeing.
And it was the first computer vision idea I've ever had.
I was a kid when I watched this movie and I imagined thinking, Oh, you can just have cameras and the
camera can make a robot, see.
However, a robot never understands what it actually sees.
That's where all this complex, deep learning software models come into play.
So let's take a look at something else.
Is computer vision artificial intelligence?
Well, yes, it is a big subset of artificial intelligence, which encapsulates a lot of different fields,
including robotics machine learning, which is also data science in a way than deep learning.
Convolutional neural networks are a big part of computer vision.
All of this will make sense to you later on in the course.
Don't worry about these big words right now.
Hope I don't confuse you.
So let's take a look at exactly what computer vision is.
It's an amalgamation of many different fields you can see.
It consists of augmented reality, mathematics and physics, electrical engineering, artificial intelligence,
machine learning, cognitive science, image processing, computer graphics, as well as many other
little fields too.
So hopefully that helps you.
However, you would have been exposed to computer vision applications right now, and you may not have
even known it.
So let's take a look at what some of the things computer vision can do.
So you may be familiar with all these snapshots of Instagram filters.
That is a form of computer vision because they're actually using your input image and then running a
model over it.
To transform the image is optical character recognition, which many of you would have seen if you've
scanned documents and then he automatically can recognize the text in those documents.
License plate reading, which is another form of OCR self-driving cars.
Well, you may not have actually seen these first hand just yet.
They're not on the market PC, although Tesla does have some that does like reverse parking and bunch
of a bunch of highway cool stuff.
So there's also a sports analysis.
You probably would have seen some of these things that cricket is a hockey prediction here, but it
tracks the ball and tells you where it's going to end up for VW decisions.
Then there's a lot of things where you can map players to some sort of geo referencing and have all
of the area that player has covered on the field and a top down view, as well as things like facial
recognition to unlock your phone.
He would have actually seen and used up a lot of things.
And there's actually so much more.
They have things like image recognition, object detection, segmentation API.
These are all examples of it.
Here you can see a lot combining this artistic style of this image onto my image and getting this cool
product here.
Segmentation here, classification, localization and object detection there.
Then you can do things like image similarity.
If you have a big bunch of images and you want to find groups that are clusters that are similarly,
you can use computer vision algorithms for that deepfakes as well.
Here's an example of Nicolas Cage's fierce being overlaid onto his.
And you can see it's a bit creepy and it's a bit dangerous.
Deepfakes are a very hot topic.
Now you can look at body pose estimation as well.
We can actually identify where each limb is and the angle it's oriented to, as well as image generation
to generate fake anime characters or many other fake types of images.
So the computer vision applications are endless.
This is a sinister robot for a very cool computer vision company has put up and is basically hundreds
of more of these.
You can imagine a whole wide computer vision applications.
So why should you do this course?
And what exactly are you going to learn?
Well, this course is separated into two sections.
There's the classical computer vision or.
In KBE section, where we took an in-depth look at all of the traditional computer vision algorithms
that have been developed from the 1970s to even modern day.
It's a lot of work still being done, so we cover all of those things.
Do all of those things are separate to deep learning because deep learning has changed the game?
Deep learning has basically allowed us to create very complex, very cool image models that can do so
many different things like object detection that you're seeing here, as well as tracking.
So who should do this course?
Well, anyone who has a strong interest in computer vision can do this course.
However, the main target I think for this course would be college students, and this can take any
level basically be a CMC or Ph.D., all of those guys.
If you have a computer vision course or a computer vision project, there's a lot of resources out there
that will confuse you when you're getting started in computer vision.
That's why I angled this course, basically starting at classical computer vision and going onto deep
learning that builds a foundation and gives you a very good, proper knowledge of computer vision.
High school students and hobbyists can very well take this course.
You can get a lot out of it and build simple prototypes using the knowledge you gain in discourse.
And anyone with a computer or software engineering background who wants to get started in computer vision,
like data scientists or software engineers, they can do this course as well.
I think that's a big chunk of people who might want to be doing this course as well.
So about me?
OK, well, firstly, I was an electrical and computer engineer for seven years as a radio frequency
engineer doing cell site planning and optimization.
Then I did my masters in artificial intelligence at the University of Edinburgh.
That was in 2014, then working in computer vision for the last six, almost seven years now.
I've looked at several London startups and even co-founded my own company at one time.
However, we went bust, so that's OK.
It's a good learning experience.
I've created several courses Udemy courses on computer vision as well as on some other platforms as
well.
And no, I'm currently a senior computer vision engineer at a company called Davos.
So these are my Udemy courses.
I know you can see I have a decent instructor rating of 4.3.
It should be higher, but the reason it isn't higher, it's mainly because these two courses my big
computer vision courses.
Here, you can see how many reviews I've got on this.
They're a bit outdated.
This one was created in 2016.
It was basically an open CV course, and I updated it later on to OpenSea before.
However, I do cover a fair amount of open CV.
However, this course covers even more topics than that course and open CV.
And then this course was created this deep learning computer vision course in twenty eighteen, and
that's about four years ago now.
And even though it's still a rather useful course, I do reference it a lot in my project something
from time to time.
It's a bit outdated, and it doesn't have a lot of the modern object detection theory and projects in
it.
So basically redone updated that course and that course was only carries TensorFlow.
This course now uses PyTorch along with TensorFlow and Keras, so that's a big plus.
So basically, I'm just going to sum up why I made this course amid Middle-schoolers to basically update
those to all the Udemy computer vision courses and have combined both of them together, as well as,
I think a lot of the student feedback and a lot of the feedback was basically broken, outdated, cold
and hard, difficult to set up libraries and installations.
So to solve that, everything now is being hosted on a run on Google collab, so there's no messy installs
of virtual machine setups.
All the code is up to date as of 2022.
Things do break from time to time when PyTorch or TensorFlow of the their visions.
However, it's relatively easy to fix for me.
Now I can just go back and see what's what change in the library, make those updates and I'll keep
the code up to date for all of you guys and I cover all the key areas, at least what I consider all
the key areas in computer vision for modern day applications.
And as I said, it's what TensorFlow and PyTorch.
And so basically it's a big course with open CV and deep learning modules in one single 27 hour course.
So what are the requirements for doing discourse?
Well, you will need an internet connection just because to Google Cloud is a cloud platform.
You don't have to have a fast internet connection.
Any broadband connection would work even.
I mean, I see at least two megabit, but probably even less would.
Work is just going to be a bit slower when you're downloading or loading different things.
You don't need much, Matt.
However, it would help.
So some high school.
That would be beneficial, as well as programming, you don't necessarily need to understand Python
or programming in general to do this course.
All of the code is explained and highly commented.
So it's kind of self-explanatory.
I know a lot of the code later on, and deep learning part is a bit complicated.
However, I do explain it line by line.
So hopefully that should help.
And generally, I want you to have enthusiasm for it and computer vision because enthusiasm is what
will motivate you to do this course.
It's a long course, however, it's a very comprehensive course and you will get a lot out of it.
So this is just an overview of all the topics I covered in the open CV section.
I'm not going to read out all of these because it's just, I mean, you can read it yourself, so you
can just pause the video if you want to take a closer look of all of them.
And these are the deep learning topics as well that we cover.
However, each of these has like sometimes 10 to 20 sub lectures involved.
Some of them are much less so.
So it's basically covering everything you need to know for understanding deep learning, using the word
PyTorch and TensorFlow with Garrus.
So in the next section, I'll just take a deeper look at the course overview.
Again, I'm not going to read out every slide, every lecture title, but I'm going to go into the topics
a bit more.
So thank you for watching and I'll see you in that section.