In this video we build a simple generative adversarial network based on fully connected layers and train it on the MNIST dataset. It's far from perfect, but it's a start and will lead us to implement more advanced and better architectures in upcoming videos.

❤️ Support the channel ❤️
https://www.youtube.com/channel/UCkzW5JSFwvKRjXABI-UTAkQ/join

Paid Courses I recommend for learning (affiliate links, no extra cost for you):
⭐ Machine Learning Specialization https://bit.ly/3hjTBBt
⭐ Deep Learning Specialization https://bit.ly/3YcUkoI
📘 MLOps Specialization http://bit.ly/3wibaWy
📘 GAN Specialization https://bit.ly/3FmnZDl
📘 NLP Specialization http://bit.ly/3GXoQuP

✨ Free Resources that are great:
NLP: https://web.stanford.edu/class/cs224n/
CV: http://cs231n.stanford.edu/
Deployment: https://fullstackdeeplearning.com/
FastAI: https://www.fast.ai/

💻 My Deep Learning Setup and Recording Setup:
https://www.amazon.com/shop/aladdinpersson

GitHub Repository:
https://github.com/aladdinpersson/Machine-Learning-Collection

✅ One-Time Donations:
Paypal: https://bit.ly/3buoRYH

▶️ You Can Connect with me on:
Twitter - https://twitter.com/aladdinpersson
LinkedIn - https://www.linkedin.com/in/aladdin-persson-a95384153/
Github - https://github.com/aladdinpersson

OUTLINE:
0:00 - Introduction
0:29 - Building Discriminator
2:14 - Building Generator
4:36 - Hyperparameters, initializations, and preprocessing
10:14 - Setup training of GANs
22:09 - Training and evaluation