Deep Learning Image Generation with GANs and Diffusion Model

Deep Learning Image Generation with GANs and Diffusion Model, available at $59.99, has an average rating of 3.8, with 34 lectures, based on 43 reviews, and has 702 subscribers.

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

Enroll now: Deep Learning Image Generation with GANs and Diffusion Model

Summary

Title: Deep Learning Image Generation with GANs and Diffusion Model

Price: $59.99

Average Rating: 3.8

Number of Lectures: 34

Number of Published Lectures: 34

Number of Curriculum Items: 34

Number of Published Curriculum Objects: 34

Original Price: $89.99

Quality Status: approved

Status: Live

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

Who Should Attend

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.

Target Audiences

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.

Image generation has come a long way, back in the early 2010s generating random 64×64 images was still very new. Today we are able to generate high quality 1024×1024 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 Modelusing 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 SrGANand

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!!!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Welcome

Lecture 2: General Introduction

Lecture 3: What youll learn

Lecture 4: Link to the Code

Chapter 2: Variational Autoencoder

Lecture 1: Link to Code

Lecture 2: Understanding Variational Autoencoders

Lecture 3: VAE training and Digit Generation

Lecture 4: Latent Space Visualizations

Chapter 3: Deep Convolutional Generative Adversarial Neural Network

Lecture 1: Link to Code

Lecture 2: How GANs work

Lecture 3: The GAN loss

Lecture 4: Improving GAN training

Lecture 5: Face Generation with GANs

Chapter 4: Wasserstein GAN

Lecture 1: Link to Code

Lecture 2: Understanding WGANs

Lecture 3: Improved Training of Wasserstein GANs

Lecture 4: WGANs in practice

Chapter 5: High quality face generation with ProGan

Lecture 1: Link to Code

Lecture 2: Understanding ProGANs

Lecture 3: ProGANs in practice

Chapter 6: Image super resolution with SRGan

Lecture 1: Link to Code

Lecture 2: Understanding SRGANs

Lecture 3: SRGan in practice

Chapter 7: Face mask removal with CycleGAN

Lecture 1: Link to Code

Lecture 2: Understanding Cyclegans

Lecture 3: Building CycleGANs

Lecture 4: Training and Testing Cyclegan for mask removal

Chapter 8: Diffusion Models

Lecture 1: Link to Code

Lecture 2: Understanding Diffusion Models

Lecture 3: Building the Unet Model

Lecture 4: Timestep embeddings

Lecture 5: Including Attention

Lecture 6: Training

Lecture 7: Sampling

Instructors

Neuralearn Dot AI
Helping millions of learners, master Deep Learning.

Rating Distribution

1 stars: 1 votes

2 stars: 3 votes

3 stars: 6 votes

4 stars: 11 votes

5 stars: 22 votes

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