Deep Learning Image Generation with GANs and Diffusion Model

Face Generation with WGANs, ProGANs and Diffusion Model. Image super-resolution with SRGAN, Mask removal with CycleGAN

3.95 (35 reviews)

Udemy

platform

English

language

Data Science

What you will learn

Understanding how variational autoencoders work

Image generation with variational autoencoders

Building DCGANs with Tensorflow 2

More stable training with Wasserstein GANs in Tensorflow 2

Generating high quality images with ProGANs

Building mask remover with CycleGANs

Image super-resolution with SRGANs

Advanced Usage of Tensorflow 2

Image generation with Diffusion models

How to code generative A.I architectures from scratch using Python and Tensorflow

Why take this course?

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 and

finally 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.

Enjoy!!!

Screenshots

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Reviews

Marco

September 12, 2023

Content-wise, this course is quite complete: even Diffusion Models are explained, which is a plus. However, there are major audio issues throughout the whole course and the presentation is not that good. (especially the drawn overlays: these mouse-written formulas are barely intellegible, are often in the way and hard to follow). Moreover, no links are shared about any of the papers mentioned in the course.

Jonathan

September 4, 2023

I have taken several other courses in GANs, but i must admit this course stands out as the best one I ever took. It’s really very simple and straightforward, and even explains much complex GANs and the recent diffusion models, while making them look really simple.

Shubham

July 23, 2023

This is a one of the best course of gan on internet. Instructor teach all concepts very well and he also provide quality material.

Arjun

July 22, 2023

Awesome course. Provides detailed explanations to basic and advanced gan architectures. Though the progan section was a little difficult at first, things became much clearer with the practical section I also like the fact that diffusion models were treated. Getting an indepth understand of this kind of model is very helpful in this fast changing field.

Nenily

July 22, 2023

This is the best gan course i've taken so far. Clear explanations and great content Doesn't only explain how gans work, but also illustrates with very practical examples Highly recommended

July 4, 2023

explanation is mostly haphazard, superficial, and rushed... no extra learning material - like links to papers, blogs, etc. code notebooks are not annotated poor presentation skills, both visually and verbally

Daniel

May 30, 2023

Muy buen curso. El ruido de fondo en algunos vídeos resulta algo molesto, pero en términos generales muy bien.

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