Generative Adversarial Network (GAN) from scratch - PyTorch

Generative Adversarial Network (GAN) from scratch | PyTorch, available at $24.99, has an average rating of 3, with 14 lectures, based on 1 reviews, and has 9 subscribers.

You will learn about Learn how the basic principles of generative models work Build & Implement a GAN from scratch (Generative Adversarial Network) in Pytorch and Tensorflow How to improve the training stability of GANs Under the hood understanding of the Generator and Discriminator Mechanism This course is ideal for individuals who are Data scientists willing to take their knowledge and skills to the next level in the area of GANs and Computer Vision or Research / Postgraduate Students willing to get a comprehensive overview of recent advancement made in the area of GANs or Deep Learning practitioners willing to apply GANs at work in production environments or Enthusiasts willing to stay up to date on GANs research and development or Deep learning beginners willing to master the building blocks of modern GANs or Anyone who wants to improve their deep learning knowledge It is particularly useful for Data scientists willing to take their knowledge and skills to the next level in the area of GANs and Computer Vision or Research / Postgraduate Students willing to get a comprehensive overview of recent advancement made in the area of GANs or Deep Learning practitioners willing to apply GANs at work in production environments or Enthusiasts willing to stay up to date on GANs research and development or Deep learning beginners willing to master the building blocks of modern GANs or Anyone who wants to improve their deep learning knowledge.

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Summary

Title: Generative Adversarial Network (GAN) from scratch | PyTorch

Price: $24.99

Average Rating: 3

Number of Lectures: 14

Number of Published Lectures: 14

Number of Curriculum Items: 14

Number of Published Curriculum Objects: 14

Original Price: ?1,199

Quality Status: approved

Status: Live

What You Will Learn

Learn how the basic principles of generative models work

Build & Implement a GAN from scratch (Generative Adversarial Network) in Pytorch and Tensorflow

How to improve the training stability of GANs

Under the hood understanding of the Generator and Discriminator Mechanism

Who Should Attend

Data scientists willing to take their knowledge and skills to the next level in the area of GANs and Computer Vision

Research / Postgraduate Students willing to get a comprehensive overview of recent advancement made in the area of GANs

Deep Learning practitioners willing to apply GANs at work in production environments

Enthusiasts willing to stay up to date on GANs research and development

Deep learning beginners willing to master the building blocks of modern GANs

Anyone who wants to improve their deep learning knowledge

Target Audiences

Data scientists willing to take their knowledge and skills to the next level in the area of GANs and Computer Vision

Research / Postgraduate Students willing to get a comprehensive overview of recent advancement made in the area of GANs

Deep Learning practitioners willing to apply GANs at work in production environments

Enthusiasts willing to stay up to date on GANs research and development

Deep learning beginners willing to master the building blocks of modern GANs

Anyone who wants to improve their deep learning knowledge

GANs have been one of the most fascinating developments in Deep Learning and Machine Learning recently.

Also now the technologies around GAN have become so mature, that more and more Industries and Companies are adopting GAN to solve many of the regular problems. (Down below I have mentioned fa ew of them). And hence, the implementation from scratch of various GAN architectures, has also become one of the most frequent take-home exercise given by Companies before recruitment for Computer Vision / Deep Learning positions.

This is a code-heavy course and with a focus on really understanding and being able to implement the underlying architecture of the super famous GANs.

It’s a comprehensive seven and half hours (7.5 Hours) of video course to Generative Adversarial Networks (GANs) with each line of code explained while implementing them.

The theories are explained in-depth and in a friendly manner.

In this course, I have covered the following six Architecture.

1. Conditional GAN

2. DCGAN

3. WGAN without Gradient Penalty

4. WGAN WITH Gradient Penalty

5. CycleGAN

6. BiCycleGAN

All the source codes in Python are given as an attachment to each section and also as a zipped file for all of them together.

My courses are the ONLY courses where you will learn how to implement Generative Modelsmachine learning algorithms from scratch

What Can Generative Models do?

Generating novel data samples such as images of non-existent people, animals, objects, etc. Not only images, but other types of media can be generated in this way as well (audio, text).

Image inpainting— restoring missing parts of images.

Image super-resolution — upscaling low-res images to high-res without noticeable upscaling artefacts.

Domain adaptation — making data from one domain resemble the data from the other domain (e.g. making a normal photo look like an oil painting while retaining the originally depicted content).

Denoising — removal of all kinds of noise from the data. For example, removing statistical noise from x-ray images fits medical needs, which will be described in our use cases.

GANs applications are able to solve different tasks:

Generate examples for Image Datasets

Image-to-Image Translation

Text-to-Image Translation

Semantic-Image-to-Photo Translation

Face Frontal View Generation

Generate New Human Poses

Photos to Emojis

Photograph Editing

Face Aging

Photo Blending

Super Resolution

Photo Inpainting

Clothing Translation

Video Prediction

3D Object Generation

By the end you’ll be able to

? Build and train not only the 6 Different GAN Networks covered in this course, but will be able to extend this knowledge to be able to implement various other GAN architecture.

Suggested Prerequisites:

Python

The concept of Gradient descent

Some familiarity with how to build a feedforward and convolutional neural network in PyTorch and TensorFlow

WHAT ORDER SHOULD I TAKE YOUR COURSES IN ?:

Mostly, each of the GAN architectures are independently developed. So basically you can follow each of the 6 GANs implementations independently. However, if you are rather new to the conceptes of Convolutional Neural Network and the very fundamentals of Deep Neural Network, then I suggest to start with DCGAN (which is the simplest among them all ).

Course Curriculum

Chapter 1: Conditional GAN From Scratch with PyTorch

Lecture 1: Conditional GAN Introduction – from Scratch with PyTorch

Lecture 2: Full Implementation – Conditional GAN From Scratch with PyTorch

Chapter 2: BiCycleGAN from Scratch with PyTorch

Lecture 1: Introduction BiCycleGAN from Scratch with PyTorch

Lecture 2: Full Implementation – BiCycleGAN from Scratch with PyTorch

Chapter 3: DCGAN from Scratch with TensorFlow – Generate fake Faces from CelebA Dataset

Lecture 1: 1-Introduction DCGAN with TensorFlow

Lecture 2: 2-Full Implementations – DCGAN from Scratch With TensforFlow CelebA_Dataset

Lecture 3: 3 Conv2dTranspose Explanations for DCGANs Generator Function Filter-Kernel_Size

Chapter 4: DCGAN From Scratch with PyTorch

Lecture 1: DCGAN From Scratch with PyTorch

Chapter 5: CycleGAN Paper Architecture Explanations

Lecture 1: CycleGAN Paper Architecture Explanations

Chapter 6: CycleGAN from Scratch with PyTorch

Lecture 1: CycleGAN from Scratch with PyTorch

Chapter 7: WGAN Architecture Paper Explanation

Lecture 1: WGAN Architecture Paper Explanation

Chapter 8: WGAN Without Gradient Penalty from Scratch with PyTorch

Lecture 1: WGAN Without Gradient Penalty from Scratch with PyTorch

Chapter 9: WGAN with Gradient_Penalty from Scratch with PyTorch

Lecture 1: Introduction WGAN with Gradient_Penalty from Scratch with PyTorch

Lecture 2: Full Implementation WGAN with Gradient_Penalty from Scratch with PyTorch

Instructors

Rohan Paul
Independent Data Scientist (NLP)

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Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!