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

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

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!

AMAGING FACTS:

· 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