Master GANs from Scratch – Implement 11 Game–Changing Models

From Theory to Application: The Ultimate Beginner's Guide to Mastering GANs Hands-On with PyTorch
What you'll learn:
How Generative Adversarial Networks (GANs) work
Implementation of GANs from scratch using PyTorch
Deep analysis of GANs: opening the black box
Review of impactful research papers
Requirements:
Basic programming knowledge
Basic Machine Learning knowledge
Description:
While diffusion models are the current hype, Generative Adversarial Networks (GANs) remain state-of-the-art due to their speed and efficiency. Despite the buzz around diffusion, GANs are still widely used in industry, and research shows that with the same compute and data, GANs can produce samples as good as diffusion models (GigaGAN paper). This course will equip you with everything you need to master GANs, implement them from scratch using PyTorch, and stay competitive in the field of Generative AI.In this course, we will dive deep into 11 influential research papers that shaped the development of GANs. By building each model step by step, you'll gain hands-on experience in creating powerful GAN architectures, from the original GAN to advanced models.Why Choose This GAN Course?Hands-on PyTorch Implementation: Build GANs from the ground up with practical PyTorch tutorials.Review 11 Key Papers: Understand and implement seminal GAN models, from the original architecture to cutting-edge variants.Master GAN Loss Variants: Implement and train models using vanilla GAN, LSGAN, WGAN, WGAN-GP, and Feature Matching loss functions to solve real-world challenges.What You'll Achieve:Implement GANs from scratch using PyTorchTrain and evaluate models like ALI, LSGAN, WGAN, WGAN-GP, Pix2Pix, and CycleGAN to tackle real-world challenges.Master adversarial training techniquesApply GANs to solve real-world AI challengesEnroll Today and Start Building GANs from Scratch!Stay ahead of the curve in Generative AI by mastering GANs—faster, and just as powerful as diffusion models when properly trained. Join us now and get hands-on with cutting-edge GAN research and implementation!
Who this course is for:
To engineers and programmers
To students and researchers
To entrepreneurs, CEOs and CTOs
Machine Learning enthusiast
Homepage

https://www.udemy.com/course/generative-adversarial-networks-pytorch/