Master GAN with TensorFlow: Create Art with Neural Networks

Unlock the creative potential of Generative Adversarial Networks (GANs) and Neural Style Transfer in this hands-on course, designed to guide you through the most advanced techniques in AI-driven image generation and art creation. Using TensorFlow, we will dive into the core concepts of GANs and explore their various architectures, providing you with practical skills to implement them from scratch. In the first half of the course, you'll master GANs by implementing several popular architectures:Vanilla GAN: Understand the basics of GANs and how the generator and discriminator interact.

DCGAN (Deep Convolutional GAN): Learn how to generate high-quality images using convolutional layers. Wasserstein GAN (WGAN): Discover how WGAN improves stability and reduces mode collapse in GAN training. Conditional GAN (CGAN): Create conditional models that allow for more control over generated images.

Pix2Pix GAN: Learn how to convert images from one domain to another, such as turning sketches into photos. Cycle GAN: Master the art of unpaired image-to-image translation, perfect for tasks like photo enhancement or style transfer. In the second part of the course, we delve into the fascinating world of Neural Style Transfer:Vanilla Neural Style Transfer: Learn how to blend the content of one image with the style of another.

Feed Forward Style Transfer: Understand the advantages of using fast neural networks for style transfer. Arbitrary Style Transfer: Generate any artistic style on any content image, enabling limitless creativity. GauGAN: Create realistic images using a simple sketch, by applying a powerful neural network trained for art generation.