Generative adversarial networks (GANs) are a type of deep learning model used for generative modeling. GANs involve training two neural networks - a generator and a discriminator. The generator produces synthetic data while the discriminator evaluates it as real or fake. This adversarial process trains the generator to produce increasingly realistic samples. GANs frame generative modeling as an adversarial game to learn the training data distribution in an unsupervised manner.

1. Introduction to Generative
Adversarial Networks (GANs)
Prof. Neeraj Bhargava
Kapil Chauhan
Department of Computer Science
School of Engineering & Systems Sciences
MDS University, Ajmer

2. Introduction
 Generative Adversarial Networks, or GANs for short,
are an approach to generative modeling using deep
learning methods, such as convolutional neural
networks.
 Generative modeling is an unsupervised learning task
in machine learning that involves automatically
discovering and learning the regularities or patterns in
input data in such a way that the model can be used to
generate or output new examples.

3. Cont..
 GANs are a clever way of training a generative model
by framing the problem as a supervised learning
problem with two sub-models:
 The generator model that we train to generate new
examples, and the discriminator model that tries to
classify examples as either real (from the domain) or
fake (generated).

4. Supervised Learning
 In Supervised Learning, we train the machine using
data that is well “labeled”.
 It means the data is already tagged with the correct
answer. A supervised learning algorithm learns from
labeled training data and predicts outcomes for
unforeseen data.

5. Cont..
 There are two subcategories of supervised learning,
viz- Regression and Classification.
Classification means to group the output into a class.
 On the other hand, Regression Algorithms are used to
predict continuous values such as price, salary, and
age.

6. Unsupervised Learning
 In Unsupervised Learning is a machine learning
technique, where the model does not need any
supervision.
 Instead, we need to allow the model to work on its own
to discover information. It mainly deals with the
unlabelled data.

7. Cont..
 Density estimation, dimensionality reduction, and
clustering and some of the main applications of
unsupervised learning.

8. Comparison:

9. Assignment
 Explain Generative Adversarial Networks in deep
learning with example.