Deep Learning Image Generation with GANs and Diffusion Model

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Neuralearn Dot AI

10:06:58

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1. Introduction

1. Welcome.mp4

01:41

2. General Introduction.mp4

04:37

3. What youll learn.mp4

03:17

4. Link to the Code.html

2. Variational Autoencoder

1. Understanding Variational Autoencoders.mp4

17:36

2. VAE training and Digit Generation.mp4

45:09

3. Latent Space Visualizations.mp4

15:23

3. Deep Convolutional Generative Adversarial Neural Network

1. How GANs work.mp4

21:50

2. The GAN loss.mp4

18:02

3. Improving GAN training.mp4

23:21

4. Face Generation with GANs.mp4

50:04

4. Wasserstein GAN

1. Understanding WGANs.mp4

19:33

2. Improved Training of Wasserstein GANs.mp4

06:14

3. WGANs in practice.mp4

19:58

5. High quality face generation with ProGan

1. Understanding ProGANs.mp4

28:01

2. ProGANs in practice.mp4

58:08

6. Image super resolution with SRGan

1. Understanding SRGANs.mp4

25:21

2. SRGan in practice.mp4

25:34

7. Face mask removal with CycleGAN

1. Understanding Cyclegans.mp4

11:09

2. Building CycleGANs.mp4

34:56

3. Training and Testing Cyclegan for mask removal.mp4

37:43

8. Diffusion Models

1. Understanding Diffusion Models.mp4

29:00

2. Building the Unet Model.mp4

14:10

3. Timestep embeddings.mp4

21:55

4. Including Attention.mp4

42:19

5. Training.mp4

22:20

6. Sampling.mp4

09:37

Description

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

Who is this for?

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.

More details

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 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!!!
Who this course is for:
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.

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Neuralearn Dot AI

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We provide world class courses in Mathematics for Deep Learning (Linear Algebra, Calculus, Probability, Statistics, Optimization), Core Deep Learning Theory (Going from the basics of Machine Learning up to most recent state of art Deep Learning Algorithms) and Practical Deep Learning applied in fields like Computer vision and Natural Language Processing, using modern tools like TensorFlow, PyTorch, HuggingFace, KubeFlow, …

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