Generative Deep Learning with TensorFlow

Week 1 introduces the concept of neural style transfer, exploring techniques to extract content and style from images and combine them into new ones. You'll also delve into fast neural style transfer and reference materials for further understanding.

Week 2 focuses on AutoEncoders, starting with the basics and gradually progressing to more complex models. From simple MNIST datasets to Fashion MNIST and CNN AutoEncoders, you'll learn to build, train, and evaluate AutoEncoder models.

Week 3 delves into Variational AutoEncoders, providing an overview of their architecture and code. You'll explore how to generate entirely new data and compare anime faces against reference images using VAEs.

Week 4 covers Generative Adversarial Networks (GANs), including their architecture, training loop, and the creation of lifelike faces. You'll build your own GAN and gain insights into various GAN experiments.