All Courses Python Courses

From Scratch, Generative Adversarial Network (GAN) | PyTorch

From Scratch, Generative Adversarial Network (GAN) | PyTorch

To learn Generative Adversarial Network implementation, you must complete a lengthy, mostly code-reliant course.

What you’ll learn

From Scratch, Generative Adversarial Network (GAN) | PyTorch

  • Learn the basics of generative modeling.
  • With Pytorch and Tensorflow, you can build a GAN (Generative Adversarial Network) from scratch.
  • How to improve the training stability of GANs
  • A Comprehensive Understanding of the Generator and Discriminator Mechanism


  • Deep learning and neural network flow fundamentals
  • Basic Python, Basic Understanding of CNN, Convolutional Neural Network


GANs are one of the most interesting new things in deep learning and machine learning right now.

Additionally, as GAN technology advances, more and more businesses and sectors are using it to address a variety of everyday issues. (I’ve listed a couple of them below.) So, one of the things employers ask candidates for computer vision and deep learning jobs to do before they are hired is to make several GAN designs from scratch.

The main goal of this course, which has a lot of code, is to understand the architecture of the very popular GANs and be able to use it.

This seven-and-a-half-hour video course goes over every line of code when using Generative Adversarial Networks (GANs).

Which courses should I take first?

Most of the GAN architectures were created separately. Consequently, you may follow each of the six GAN implementations separately. However, I advise beginning with DCGAN if you are unfamiliar with the foundations of deep neural networks and convolutional neural networks (which is the simplest of them all ).

Who this course is for:

  • Data scientists want to learn more about GANs and computer vision and get better at what they can do with them.
  • Research and graduate students who want to know what’s new in the field of GANs can read this book.
  • Practitioners of deep learning will be eager to use GANs in real-world settings.
  • Those interested in keeping up with the research and development of GANs
  • Beginners in deep learning who are eager to grasp the fundamentals of contemporary GANs
  • Anyone who wishes to increase their understanding of deep learning may