Generative Adversarial Network (GAN) from scratch | PyTorch
What you'll 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
- Basic Python, Basic Understanding of CNN, Convolutional Neural Network
- Basic conceptes of deep learning and Neural Network flow
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.
WGAN without Gradient Penalty
WGAN WITH Gradient Penalty
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 Models machine 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
Face Frontal View Generation
Generate New Human Poses
Photos to Emojis
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.
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 ).
Who this course is for:
- 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
Hi, my name is Rohan Paul and I am an Independent Data Scientist (specializing in applied Deep Learning ).
Kaggle Master. Ex Banker. YouTuber.
And previously I have worked as a Full Stack Software Engineer in NodeJS, MongoDB, ReactJS, and Angular Stack in Bangalore (India) Startup scene.
And before coming to the Tech world, I worked in International Banking across India and Australia as a Financial Credit Analyst and Model Builder.