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In the video on Generative adversarial Networks Tutorial, we will cover what is GAN, and understand Generators and Discriminators. In this deep learning for beginners tutorial, we will also experience a hands-on lab demo of how to use Generative Adversarial Networks with a celebrity face image dataset. Below are the topics covered in this tutorial:

00:00 Generative Adversarial Networks Tutorial
00:35 What is GAN?
01:10 What is Generator?
01:50 What is Discrimator?
03:40 Hands-on Lab Demo

What is Generative Adversarial Networks?

Generative Adversarial Networks (GANs) were introduced in 2014 by Ian J. Goodfellow and co-authors. GANs perform unsupervised learning tasks in machine learning. It consists of 2 models that automatically discover and learn the patterns in input data.

The two models are known as Generator and Discriminator.

They compete with each other to scrutinize, capture, and replicate the variations within a dataset. GANs can be used to generate new examples that plausibly could have been drawn from the original dataset.

What is a Generator?

A Generator in GANs is a neural network that creates fake data to be trained on the discriminator. It learns to generate plausible data. The generated examples/instances become negative training examples for the discriminator. It takes a fixed-length random vector carrying noise as input and generates a sample.

What is a Discriminator?

The Discriminator is a neural network that identifies real data from the fake data created by the Generator. The discriminator's training data comes from different two sources:

The real data instances, such as real pictures of birds, humans, currency notes, etc., are used by the Discriminator as positive samples during training.
The fake data instances created by the Generator are used as negative examples during the training process.
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