Deep Learning :Adv. Computer Vision (object detection+more!)

Transfer Learning, TensorFlow Object detection, Classification, Yolo object detection, real time projects much more..!!

4.95 (414 reviews)



7 hours


Jan 2021

Last Update
Regular Price

What you will learn

computer vision

deep learning



Latest update: I will show you both how to use a pretrained model and how to train one yourself with a custom dataset on Google Colab.

This course is a complete guide for setting up TensorFlow object detection api, Transfer learning and a lot more

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.

Here is the details about the project.

Here we will star from colab understating because that will help to use free GPU provided by google to train up our model.

We’re going to bridge the gap between the basic CNN architecture you already know and love, to modern, novel architectures such as ResNet, and Inception.

We will understand object detection modules in detail using both tensorflow object detection api as well as YOLO algorithms.

We’ll be looking at a state-of-the-art algorithm called RESNET and MobileNetV2 which is both faster and more accurate than its predecessors.

One best thing is you will understand the core basics of CNN and how it converts to object detection slowly.

I hope you’re excited to learn about these advanced applications of CNNs Yolo and Tensorflow, I’ll see you in class!


· This course give’s you full hand’s on experience of training models in colab GPU.

· 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,YOLO, 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

Who this course is for:

· 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

· Anyone who is starting with computer vison


Deep Learning :Adv. Computer Vision (object detection+more!)
Deep Learning :Adv. Computer Vision (object detection+more!)
Deep Learning :Adv. Computer Vision (object detection+more!)
Deep Learning :Adv. Computer Vision (object detection+more!)




Googal Colab and TensorFlow

Course Objective

CNN Components

Coding Exercise: Train CNN model with your own images


Thakur29 December 2020

1)More elaboration was expected for object detection part. I would like the author to add tensorflow or pytorch based code to completely train yolo and how to do inference. I will wait further on the said feed back. 2) two more examples of transfer learning with tf and keras would have helped a lot. I request the author to add the same. Positives: 1) short and crisp videos helping a lot :). keep it up.

Jeremy2 December 2020

Love this course. Instructor is very knowledgeable and teaches very clearly. It was my first ever computer vision course. I had no idea about basics of OpenCV or any other tool used in the project. He taught everything so patiently, function by function that I have planned to change my domain of work. I'm looking forward for his other courses and trying to master this domain. Once again, Thanks for this lovely tutorial, a hand holding guide to get one started in computer vision. Absolutely recommended course!.

Stephen2 December 2020

Course completely fulfills the expectations and provides with solid knowledge basis to kick-off with practicing python in computer vision in case one has basic familiarity with the field. There is a brief but effective intro to convolutional neural nets and deep learning which is also very handy.Thank you Jay

Sean2 December 2020

This is an excellent course designed for OpenCV covering all the major aspects of Image and Video Processing. A lot of details of Python programming are provided by using real time coding examples. Deep Learning modules come as an added advantage with this course. Fundamental techniques and datasets are used very well with explanatory examples. I highly appreciate this course and personally thanks Jay for his sincere efforts.

Patrick2 December 2020

i come with none knowledge of computer vision and deep learning, after i finish this course i feel i just gain basic about this topic. thanks a lot of

Richard2 December 2020

the delivery in this course is one of the best have seen so far, the use of the assessment is really nice this helps reinforce what you have learnt in any section, would encourage everyone not to skip the assessment. after going through this course I had to buy another course from Jay

Timothy30 November 2020

I knew nothing about DL and AI before starting this course. Now, I am very confident in solving problems.

Thomas29 November 2020

The words 'Thank you' don't even begin to express my gratitude for the experience that I've had just by attending your online course. You have been an excellent teacher. And because of your liveliness, care and understanding you have undoubtedly made python a really interesting that once seemed sort of boring for me. I am really grateful to you and now have a liking towards python.

Eric29 November 2020

This course is exactly what I was looking for. The instructor does an impressive job making students understand they need to work hard in order to learned. The examples are clear, and the explanations of the theory is very interesting.


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