DCGAN implementation from scratch

I was inspired to make these videos by this specialization: https://bit.ly/3SqLuA6

In this video we build a generative adversarial network based on convolutional neural networks and train it on the CelebA dataset. This is a huge improvement from the previous simple fully connected GAN implemented in previous videos.

DCGAN paper:
https://arxiv.org/abs/1511.06434

CelebA dataset used in video:
https://www.kaggle.com/dataset..../504743cb487a5aed565

GitHub Repository:
https://github.com/aladdinpers....son/Machine-Learning

GAN Playlist:
https://youtube.com/playlist?l....ist=PLhhyoLH6IjfwIp8

PyTorch Playlist:
https://www.youtube.com/playli....st?list=PLhhyoLH6Ijf

OUTLINE:
0:00 - Introduction
0:26 - Quick Paper Recap
4:31 - Implementation of Discriminator
9:38 - Implementation of Generator
15:27 - Weight initialization and test model
19:09 - Setup of training
31:36 - Training on MNIST
32:20 - Modifications to CelebA dataset
33:52 - Training on CelebA and ending