Understanding GANs: Generative Adversarial Networks explained by Buzzy!

In this video Buzzy explains about GAN or Generative Adversarial Network in simple terms with real examples. You will learn about 6 types of common GANs and other uncommon types of Generative Adversarial Networks within 6 minutes. In the world of artificial intelligence and machine learning, GANs or Generative Adversarial Networks have become one of the most fascinating and promising advancements. GANs are a type of deep neural network that can generate new data samples similar to the training data. But how do they work? In this video, we&explore the different types of GANs including Vanilla GAN, Conditional GAN (CGAN), Wasserstein GAN (WGAN), Deep Convolutional GANs (DCGAN), CycleGAN, StyleGAN, Self-Attention Generative Adversarial Network (SAGAN), BigGAN, DiscoGAN, PixelRNN, lsGAN, and Stacked GAN. We&dive into the details of each type and explain how they work. We&also discuss the applications of GANs such as text-to-image synthesis and image-to-image translation. Moreover, we&cover how GANs have revolutionized the field of artificial intelligence and what the future holds for this technology. Whether you&a beginner or an advanced learner in AI, this video will help you understand the basics of GANs and their importance in the field of AI. So, if you&curious about GANs, be sure to watch this video! Video Chapters: 00:00 Introduction by Leena 00:18 What is GAN? 00:33 Generator and Discriminator 00:45 Example of GAN (Artist) 01:12 Example of GAN (Currency Notes...