In this video, we delve into the core concepts of Generative Adversarial Networks ( GAN ), going over different components and how to build a generative adversarial neural network , understanding how to train generative adversarial networks , exploring the underlying math and providing a step-by-step PyTorch implementation.
This video is an attempt to give you a thorough understanding of how do Generative Adversarial Networks work. By the end of video we will be building gan with pytorch and train our gan on mnist dataset and see results of that.
🔍 Video Highlights:
1. Overview of Generative Adversarial Networks : We first look at the general objective of GAN and its different components, discriminator and generator and what are their responsibilities.
2. Training Generator and Discriminator of GAN : We see how to train the generator and discriminator from their perspective of classification objectives.
3. Math behind Optimization of GAN : We break down entire math behind the value function that the models try to optimize in Generative Adversarial Networks and also go over the convergence proofs pertaining to the optimal discriminator and generator as a result of this optimization process.
4. PyTorch Implementation of Generative Adversarial Networks : We end by implementing a GAN with fc layers for training on MNIST dataset in PyTorch.
⏱️ Timestamps
00:00 Intro
00:36 Objective of using Generative Adversarial Networks
01:50 Components of GAN: Generator and Discriminator
04:53 Optimizing a Generative Adversarial Networks
12:22 Optimal Discriminator and Generator of GAN
18:57 PyTorch Implementation of Generative Adversarial Networks
25:33 Results of training GAN on MNIST
26:02 Outro
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Useful Resources:
Paper Link - tinyurl.com/exai-gan-paper
Implementation - tinyurl.com/exai-gan-github-im...
📌 Keywords:
#GenerativeAdversarialNetworks #GANsTutorial #GAN #GenerativeAI
Background Track - Fruits of Life by Jimena Contreras
Email - explainingai.official@gmail.com
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