Generative adversarial networks (GANs) are deep learning architectures that use two neural networks (Generator and Discriminator), competing one against the other. The generator tries to create realistic looking fake data (e.g. images) and the discriminator tries to classify whether the data is real or fake. After a few thousand (or million) epochs, the generator trained model can be used to create new fake data that can pass for real data.

This tutorial the implementation of GAN using Keras in Python. It uses fully connected dense layers for both the generator and discriminator. It also explains the use of trained model in generating realistic looking fake handwritten digits.

References from the video:
https://www.thispersondoesnotexist.com/
http://www.wisdom.weizmann.ac.il/~vis...

Code generated in the video can be downloaded from here: https://github.com/bnsreenu/python_fo...