• dlnd_face_generation_starter.ipynb
• README.md
• requirements.txt
• tests.py

Open the notebook file, dlnd_face_generation_starter.ipynb and follow the instructions. This project is organized as follows:

• Data Pipeline: implement a data augmentation function and a custom dataset class to load the images and transform them.
• Model Implementation: build a custom generator and a custom discriminator to make your GAN
• Loss Functions and Gradient Penalty: decide on loss functions and whether you want to use gradient penalty or not.
• Training Loop: implement the training loop and decide on which strategy to use