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

If you're already well-versed in deep learning, you would know that convolutional neural networks (CNNs) are frequently used in computer vision tasks. They can detect objects in images, classify them, and even localize them. To take your computer vision skills to the next level, you might want to try "Deep Learning: Advanced Computer Vision (GANs, SSD, +More!)".

Course Overview

The course is all about advanced computer vision applications of CNNs. It covers high-level building blocks of systems involving CNNs, such as state-of-the-art architectures like VGG, ResNet, and Inception, popular object detection algorithm SSD, and other advanced topics such as Neural Style Transfer (NST) and Generative Adversarial Networks (GANs).

The course is offered on Udemy and is taught by programming and math enthusiast, Dr. Sundog Education by Frank Kane. It has a solid 4.66/5 rating from over 5,400 students.

Course Content

The course's headline promises VGG, ResNet, Inception, SSD, RetinaNet, Neural Style Transfer, GANs +More in Tensorflow, Keras, and Python, and it absolutely lives up to that promise. Here's what you can expect to learn in this course:

• VGG, ResNet, and Inception - In the course, you'll learn how to apply VGG, ResNet, and Inception architectures to images of blood cells. This will create a system that's better than your average medical expert. The course also shows that the doctors of the future could be robots that are experts in CNN systems.
• SSD and RetinaNet - In this course, you will learn how to turn a CNN into an object detection system that can not only classify but also locate each object in an image and predict its label. SSD and RetinaNet are two of the most popular algorithms in this task, and the course covers both.
• Neural Style Transfer (NST) - This computer vision task involves creating a new image based on two input images: a content image and a style image. You'll learn how to use a CNN to extract content and style features from input images and use them to create a unique output image.
• Generative Adversarial Networks (GANs) - The course also covers GAN architecture and how it's used to generate state-of-the-art, photorealistic images. You will learn the technology behind this and other advanced neural network applications.
• Object Localization and Detection - You'll learn how to implement object localization, which is the essential first step towards implementing an object detection system. The course covers modern algorithms that can detect objects, such as cars, pedestrians, bicycles, and traffic lights, in real-time.

Unique Features

This course is a perfect option if you're an advanced computer vision practitioner who wants to learn advanced CNN applications. One of the unique features of this course is that it focuses on high-level building blocks of CNN architectures. As a result, there's almost zero math involved and no complicated low-level code such as that written in Tensorflow, Theano, or PyTorch. Instead, the majority of the course is in Keras, a Python library that allows for fast experimentation.

This course provides benefits to both novice and experts. It's the only course where you will learn how to implement machine learning algorithms from scratch. Most courses show you how to plug in your data into a library. You become a coder capable of creating your own unique projects by learning how to use this library to create advanced computer vision applications with these new concepts.

Another unique aspect of this course is that it provides the code explanations with detail. There is no "wasted time typing" code on the keyboard. Instead, you learn how to write important details about the algorithms that other courses often leave out.

Suggested Prerequisites

If you're interested in taking this course, here are the 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.

It sounds like a lot, but if you're already an expert in computer vision and CNNs, you might not need to study as much before taking this course.

Course Structure and Syllabus

The course structure follows a straightforward pattern. There are eight sections, each containing several lectures. The sections and their respective lecture topics are as follows:

1. Setup and Installation. Learn to set up Python, Tensorflow and Keras, and download some publicly available datasets.
2. Blood Cell Classification. Apply CNN architectures (VGG, Inception and ResNet) to classify blood cell images.
3. Object Detection with SSD and RetinaNet. Two different object detection algorithms applied in CNNs to locate objects.
4. Understanding and Visualizing CNNs. Understand the mechanisms behind CNNs and learn techniques to visualize what your model has learned.
5. Convolutions and Deep Dream. Learn how to use convolutions to create trippy videos and images.
6. Neural Style Transfer. Understand how to implement Neural Style transfer which is creating a new image based on two input images or style transfer technique.
7. Generative Adversarial Networks (GANs). Understanding the techniques behind the GAN architectures that can generate photo-realistic images.
8. Object Localization and Detection with RetinaNet. Implementing state-of-the-art object detection algorithm RetinaNet on your custom dataset.

The course ends with a section on "what to do next." It offers suggestions for follow-up reading, including books, academic papers, and other online courses on CNNs and deep learning.

Course Statistics

Some statistics that might interest you about the course are that it has over 5,400 ratings with an average rating of 4.66 out of 5. In addition, more than 1,000 students have given a perfect score of 5. This is an indication that learners find this course valuable and that they're getting the knowledge they need to take their computer vision skills to the next level.

The lecture videos have a total runtime of over 16 hours. You can take the course at your own pace and complete it between two weeks and a month.

Is It Worth the Time and Money?

The answer is unequivocally Yes. If you're an expert in computer vision and CNNs but want to learn advanced CNN applications, then this course is worth investing your time and money into. The course is also a fantastic learning opportunity for those interested in pursuing a career in computer vision or machine learning.

The course has a lot of practical examples and is very hands-on. This means that you can apply what you learn to real-world CNN applications right away. This is not a course where you'll learn theory and not know how to apply it later.

Takeaways

The course is well-structured, covers current and modern techniques in computer vision, and is well-praised by learners, meaning it delivers on its promises. The course provides a solid foundation for advanced CNN applications, and you'll learn to implement real-world CNN applications from scratch. Therefore, we can conclude that this course is an excellent investment for anyone wanting to advance their computer vision skills.