What will you learn?
- Understand and apply transferable learning
- Understand and use state-of-the-art convoluted neural networks such as VGG, ResNet and Inception
- Understand and use an object detection algorithm like SSD
- Understanding and applying neural style transfer
- Understand the latest computer vision topics
- Class activation cards
- GANs (Generative Advertising Networks)
- Object Localization Implementation Project
Required:
Learn how to create, train, and use CNN using some libraries (preferably Ask 2)
Understand the underlying theoretical concepts behind convolution and neural networks
Ask for good programming skills, preferably Data Science and Nampi Stack
Attribute:
Latest update: Instead of SSD, I'll show you how to use RetinaNet, which is better and more modern. I'll show you how to use pre-trained models on Google Clab and how to train yourself with custom data sets.
This is one of the most interesting courses I have ever taken and it really shows how much faster and longer learning has passed over the years.
When I first started my deep learning course, I never thought I'd do two courses on complex neural networks.
I think what you will find, this course is completely different from the previous one, you will be impressed with how much content we want to cover.
Let's briefly explain what this course is for:
With modern, new builds like VGG, ResNet, and Inception, we're going to bridge the gap between basic CNN architectures that you already know and love (in the name of a movie, which is great!).
We're going to apply it to pictures of blood cells, and create a system with a better medical expert than you or me. This leads to an interesting idea: future doctors are robots, not humans.
In this course, you will learn how to convert CNN into an object recognition system, which can not only categorize images but also detect anything in the image and determine its label.
You can imagine that this function is a basic requirement for self-driving vehicles. (Be able to track cars, pedestrians, bicycles, traffic lights, etc. in real time)
We see an advanced algorithm called SSD, which is faster and more accurate than its predecessor.
Another popular computer vision feature that uses CNN is neural style transfer.
Here you take a picture called Content Image and another image called Style Image, and you combine them all together to create a brand new image, that is, you have assigned a painter the first image. Should be painted with the style of the material. The other Unlike a human artist, it can be done in seconds.
I also introduce you to the now-famous Generative Adverse Networks (GAN) architecture, where you can learn some of the techniques behind which neural networks are used to create sophisticated, photo-realistic images.
We are also currently implementing object localization, which is an important first step in implementing a complete object detection system.
I hope you're curious about CNN's advanced applications, see you in the classroom!
Miracle Facts:
One of the main topics of this course is that we are moving from CNN to a system that includes CNN.
Instead of focusing on CNN's detailed internal work (which we've already done), we focus on the highest quality building blocks. Remove? Almost zero math.
Another result? There are no complicated low-level codes like Tensorflow, Theano or PyTorch (though in some optional exercises they can be for advanced learners). Most courses will be in Kerala which means a lot of annoying repetitive content is written for you.
"If you can't do it, you don't understand."
Or as the great physicist Richard Feynman has said, "What I cannot create, I cannot understand.
My courses are the only courses where you can learn how to apply machine learning algorithms from scratch.
Other courses teach you how to plug in your data library, but do you really need help with 3 lines of code?
After doing the same thing with 10 data sets, you realize that you can't learn 10 things. You learn 1 story and repeat the same 3 lines of code 10 times ...
Suggested preparations:
Learn how to create, train, and use CNN using some libraries (preferably Ask 2)
Understand the underlying theoretical concepts behind convolution and neural networks
Ask for good programming skills, preferably Data Science and Nampi Stack
In what setting should I take your course ?:
See the lecture "Machine Learning and the AI Advance Roadmap" (available in the FAQ for all of my courses, including a free sample course).
This course is for anyone:
Students and professionals who want to take their deep knowledge and deep learning of computer philosophy to the next level
Anyone who wants to learn more about object detection algorithms like SSD and YOLO
Anyone who wants to learn how to write code for a neural style transition
Who wants to use Transfer Training?
Which would like to reduce training time and make faster progressive compositions
Here is the download links
https://www.udemy.com/course/advanced-computer-vision/
https://drive.google.com/file/d/1_nDq22eU99-bOTqHZe9N9SGhwLzCBOv7/view?usp=sharing
* If your in trouble watch the video thanks! *
Torrent software for windows -> Torrent Downloader
you can join over whats app group -> FREE COURSES 2022
