Naukri Learning > Data Science & Analytics > Data Science >

Deep Learning: Advanced Computer Vision (GANs, SSD, +More!)

Deep Learning: Advanced Computer Vision (GANs, SSD, +More!)


₹12,800  ₹451  97% OFF

Buy Now

Course Highlights

  • Compatible on Mobile and TV

  • Earn a Cerificate on successful completion

  • Get Full Lifetime Access

  • Learn from Lazy Programmer Inc.

Mode of learning: Online self study

Course Overview

Who should do this course ?
  • Students and professionals who want to take their knowledge of computer vision and deep learning to the next level

  • Anyone who wants to learn about object detection algorithms like SSD and YOLO

  • Anyone who wants to learn how to write code for neural style transfer

  • Anyone who wants to use transfer learning

  • Anyone who wants to shorten training time and build state-of-the-art computer vision nets fast

What are the course deliverables ?
  • Understand and apply transfer learning

  • Understand and use state-of-the-art convolutional neural nets such as VGG, ResNet and Inception

  • Understand and use object detection algorithms like SSD

  • Understand and apply neural style transfer

  • Understand state-of-the-art computer vision topics

  • Class Activation Maps

  • GANs (Generative Adversarial Networks)

  • Object Localization Implementation Project

More about this course
  • This is one of the most exciting courses I've done and it really shows how fast and how far deep learning has come over the years. When I first started my deep learning series, I didn't ever consider that I'd make two courses on convolutional neural networks . I think what You'll find is that, this course is so entirely different from the previous one, you will be impressed at just how much material we have to cover. Let me give you a quick rundown of what this course is all about: We're going to bridge the gap between the basic CNN architecture you already know and love, to modern, novel architectures such as VGG , ResNet , and Inception (named after the movie which by the way, is also great!) We're going to apply these to images of blood cells, and create a system that is a better medical expert than either you or I. This brings up a fascinating idea: that the doctors of the future are not humans, but robots. In this course, You'll see how we can turn a CNN into an object detection system, that not only classifies images but can locate each object in an image and predict its label. You can imagine that such a task is a basic prerequisite for self-driving vehicles . (It must be able to detect cars, pedestrians, bicycles, traffic lights, etc. in real-time) We'll be looking at a state-of-the-art algorithm called SSD which is both faster and more accurate than its predecessors. Another very popular computer vision task that makes use of CNNs is called neural style transfer . This is where you take one image called the content image, and another image called the style image, and you combine these to make an entirely new image, that is as if you hired a painter to paint the content of the first image with the style of the other. Unlike a human painter, this can be done in a matter of seconds. I will also introduce you to the now-famous GAN architecture ( Generative Adversarial Networks ), where you will learn some of the technology behind how neural networks are used to generate state-of-the-art, photo-realistic images. Currently, we also implement object localization , which is an essential first step toward implementing a full object detection system. I hope you're excited to learn about these advanced applications of CNNs, I'll see you in class! AWESOME FACTS: One of the major themes of this course is that we're moving away from the CNN itself, to systems involving CNNs. Instead of focusing on the detailed inner workings of CNNs (which we've already done), we'll focus on high-level building blocks. The result? Almost zero math . Another result? No complicated low-level code such as that written in Tensorflow , Theano , or PyTorch (although some optional exercises may contain them for the very advanced students). Most of the course will be in Keras which means a lot of the tedious, repetitive stuff is written for you. Suggested Prerequisites: Know how to build, train, and use a CNN using some library (preferably in Python) Understand basic theoretical concepts behind convolution and neural networks Decent Python coding skills, preferably in data science and the Numpy Stack TIPS (for getting through the course): Watch it at 2x. Take handwritten notes. This will drastically increase your ability to retain the information. Write down the equations. If you don't, I guarantee it will just look like gibberish. Ask lots of questions on the discussion board. The more the better! Realize that most exercises will take you days or weeks to complete. Write code yourself, don't just sit there and look at my code. WHAT ORDER SHOULD I TAKE YOUR COURSES IN?: Check out the lecture "What order should I take your courses in?" (available in the Appendix of any of my courses, including the free Numpy course)

Curriculum (99 Chapters)

    Show moreBuy Now


    Average Rating

    Write a Review

    Frequently Asked Questions

    How can I take this course?
    This course is delivered by an instructor through online videos and other documents only. You will be redirected to our partner site for making the payment and then accessing this course. You do not have to go to any classroom in any place for taking this course. You need an internet connection and a computer/laptop/mobile/tablet only.
    Is there a specific time within which I should complete my courses?
    Courses do not expire, and therefore, there is no time frame to complete your courses. Once you purchase this course, you can view it as many times as you want. You can do the course as fast or slow as you want. You can also view it from any machine in the world, whenever you want - forever.
    Do I get a certificate on taking this course?
    On completing the course, you will get a course completion certificate from Udemy.
    Do I get a refund in case I do not like this course?
    We have 7 days exchange policy. Please send email to in case you have any issues related to the courses you have purchased. We don’t provide any refunds. Please inform about course(s) that you want in exchange of the ones purchased earlier and our support team will help you. This policy will not be applicable if the participant has accessed more than 30% of the e-Learning content or has attended more than 1 class. Applicable only through courses purchased through Naukri Learning. For others, please contact the course provider directly.
    I have some other question. Where can I get help?
    Please send email to or call our toll-free number 1800-103-4702.
    CompareRemove All
    Browse Category

    Buy Safely with
    We support secure payment methods


    While Naukri Learning services have helped many customers over the years, we do not guarantee any interview calls or assure any job offers with any of our services.
    The services associated with Naukri Learning are only provided through the website You are advised to be cautious of calls/emails asking for payment from other web sites that claim to offer similar services under the name of We have no associates/agents other than the partner sites that have been specifically named on the homepage of the website We also recommend that you Security Guidelines and Terms and Conditions