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

What you'll 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

Requirements

Basic Knowledge of Python

Basic Knowledge of Tensorflow

Access to an internet connection, as we shall be using Google Colab (free version)

Description

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.YOU'LL ALSO GET:Lifetime access to This CourseFriendly and Prompt support in the Q&A sectionUdemy Certificate of Completion available for download30-day money back guaranteeEnjoy!!!

Overview

Section 1: Introduction

Lecture 1 Welcome

Lecture 2 General Introduction

Lecture 3 What you'll learn

Lecture 4 Link to the Code

Section 2: Variational Autoencoder

Lecture 5 Understanding Variational Autoencoders

Lecture 6 VAE training and Digit Generation

Lecture 7 Latent Space Visualizations

Section 3: Deep Convolutional Generative Adversarial Neural Network

Lecture 8 How GANs work

Lecture 9 The GAN loss

Lecture 10 Improving GAN training

Lecture 11 Face Generation with GANs

Section 4: Wasserstein GAN

Lecture 12 Understanding WGANs

Lecture 13 Improved Training of Wasserstein GANs

Lecture 14 WGANs in practice

Section 5: High quality face generation with ProGan

Lecture 15 Understanding ProGANs

Lecture 16 ProGANs in practice

Section 6: Image super resolution with SRGan

Lecture 17 Understanding SRGANs

Lecture 18 SRGan in practice

Section 7: Face mask removal with CycleGAN

Lecture 19 Understanding Cyclegans

Lecture 20 Building CycleGANs

Lecture 21 Training and Testing Cyclegan for mask removal

Section 8: Diffusion Models

Lecture 22 Understanding Diffusion Models

Lecture 23 Building the Unet Model

Lecture 24 Timestep embeddings

Lecture 25 Including Attention

Lecture 26 Training

Lecture 27 Sampling

Beginner Python Developers curious about Deep Learning.,People interested in using A.I and deep learning to generate images,People interested in generative adversarial networks (GANs) , other more advanced GANs and DIffusion Models,Practitioners interested in learning to building GANs and Diffusion models from scratch,Anyone who wants to master Image super-resolution using GANs,Software developers who want to learn how state of art Image generation models are built and trained using deep learning.