Deep Learning: Advanced Computer Vision

Deep Learning: Advanced Computer Vision

VGG, ResNet, Inception, SSD, RetinaNet, Neural Style Transfer, GANs +More in Tensorflow, Keras, and Python

What you'll learn

  • Understand and apply transfer learning
  • Understand and use state-of-the-art convolutional neural nets such as VGG, ResNet and Inception
  • Class Activation Maps
  • GANs (Generative Adversarial Networks)
  • Object Localization Implementation Project
  • Understand and use object detection algorithms like SSD
  • Understand and apply neural style transfer
  • Understand state-of-the-art computer vision topics

Course content

Checkout this course preview here: https://www.udemy.com/course/advanced-computer-vision/ (Copy and paste this link to your URL address bar to open)

Description

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.

We 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.

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 shorten training time and build state-of-the-art computer vision nets fast
  • Anyone who wants to learn how to write code for neural style transfer
  • Anyone who wants to use transfer learning

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