DCGAN implementation from scratch

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/504743cb487a5aed565ce14238c6343b7d650ffd28c071f03f2fd9b25819e6c9 ❤️ 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 0:26 - 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