Deep Learning Image Generation with GANs and Diffusion Model

Image generation has come a long way, back in the early 2010s generating random 64x64 images was still very new. Today we are able to generate high quality 1024x1024 images not only at random, but also by inputting text to describe the kind of image we wish to obtain. In this course, we shall take you through an amazing journey in which you'll master different concepts with a step by step approach.

We shall code together a wide range of Generative adversarial Neural Networks and even the Diffusion Model using Tensorflow 2, while observing best practices. You shall work on several projects like: Digits generation with the Variational Autoencoder (VAE), Face generation with DCGANs,then we'll improve the training stability by using the WGANs andfinally we shall learn how to generate higher quality images with the ProGAN and the Diffusion Model. From here, we shall see how to upscale images using the SrGAN and then also learn how to automatically remove face masks using the CycleGAN.

If you are willing to move a step further in your career, this course is destined for you and we are super excited to help achieve your goals. This course is offered to you by Neuralearn. And just like every other course by Neuralearn, we lay much emphasis on feedback.

Your reviews and questions in the forum, will help us better this course. Feel free to ask as many questions as possible on the forum. We do our very best to reply in the shortest possible time.