This project contains some examples on building GAN networks:

1- MNIST_GAN.ipynb: In this notebook, we'll be building a generative adversarial network (GAN) trained on the MNIST dataset to be able to generate new handwritten digits!

2- DCGAN_Example.ipynb: this notebook is an example of building a GAN using convolutional layers in the generator and discriminator.using Street View House Numbers (SVHN) dataset.

3- CycleGAN_Example.ipynb: this notebook is an example of a CycleGAN that reads in an image from a set and transform it so that it looks as if it belongs in set . Specifically, using images of Yosemite national park taken either during the summer of winter. The seasons are our two domains!

4- face_generation.ipynb: this project creates DCGAN on a dataset of faces. Your goal is to get a generator network to generate new images of faces that look as realistic as possible! traininig using CelebFaces Attributes Dataset (CelebA)

Environment: Install a few required pip packages, which are specified in the requirements text file (including OpenCV). pip install -r requirements.txt