Generative Adversarial Network are very popular in the field of Deep Learning. In this video, you will learn what GANs are and understand about Generators and Discriminators. You will understand how GANs works and look at the mathematical equation of GAN. Finally, you will learn the types od GANs and see the different applications of GANs. Let's begin. This Deep Learning course with Tensorflow certification training is developed by industry leaders and aligned with the latest best practices. You’ll master deep learning concepts and models using Keras and TensorFlow frameworks and implement deep learning algorithms, preparing you for a career as Deep Learning Engineer. In this Deep Learning course with Keras and Tensorflow certification training, you will become familiar with the language and fundamental concepts of artificial neural networks, PyTorch, autoencoders, and more. Upon completion, you will be able to build deep learning models, interpret results, and build your own deep learning project. Key features: - 34 hours of Blended Learning - Real-life industry-based projects - 24/7 support with dedicated project mentoring sessions - Flexibility to choose classes - Dedicated mentoring session from our Industry expert faculties Learn more at: https://bit.ly/2QbgDJu

2. What’s in it for you?
• What are Generative Adversarial Networks
• Generator
• Discriminator
• How GANs work?
• Types of GANs
• Applications of GANs

3. What are Generative Adversarial Networks?
Generative Adversarial Networks consist of two models that compete
with each other to analyze, capture and copy the variations within a
dataset
Generator
Discriminator

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5. Generator
The Generator in GAN learns to create fake data by incorporating
feedback from the discriminator
Generator network
RandomInput
Fake Image

6. Generator Training
Real Images Sample
Generator Sample Discriminator
Discriminator
Loss
RandomInput
GeneratorLoss
Backpropagation

7. Discriminator
The Discriminator in GAN is a classifier that identifies real data from
the fake data created by the Generator
Fake Image
Real Images
Discriminator
Network

8. Discriminator Training
Real Images Sample
Generator Sample
Discriminator
Discriminator
Loss
RandomInput
GeneratorLoss
Backpropagation

9. How GANs Work?
Real
Images
Generator
Network
Discriminator
Network
Predicted Labels
Noise vector
Fakes images
GANs consist of 2 networks, a Generator G(x) and a Discriminator D(x)
Real / Fake notes

10. How GANs Work?
Real
Images
Generator
Network
Discriminator
Network
Predicted Labels
Noise vector
Fakes images
The Generator learn the distribution of data and is trained to increase the
probability of the Discriminator network to make mistakes
Real / Fake notes

11. How GANs Work?
Real
Images
Generator
Network
Discriminator
Network
Predicted Labels
Noise vector
The Discriminator estimates the probability that the sample it got is from the
training data and not from the Generator
Real / Fake notes
Fakes images

12. How GANs Work?
The mathematical formula for working on GANs can be
represented as:
V(D, G) = Ex~Pdata(x) [logD(x)] + Ez~p(z) [log(1 – D(G(z))]
Where,
G = Generator
D = Discriminator
Pdata(x) = distribution of real data
p(z) = distribution of generator
x = sample from Pdata(x)
z = sample from P(z)
D(x) = Discriminator network
G(z) = Generator network

13. How GANs Work?
Steps for training GAN
• Define the problem
• Choose the architecture of GAN
• Train Discriminator on real data
• Generate fake data for Generator
• Train Discriminator on fake data
• Train Generator with the output of Discriminator

14. Types of GANs
Simplest type of GAN where the
Generator and Discriminator are
simple multi-layer perceptrons
Vanilla GANs
DCGANs comprise of ConvNets and
are more stable and generate higher
quality images
Deep Convolutional GANs
(DCGANs)

15. Types of GANs
CGANs use extra label
information to generate better results
Conditional GANs
(CGANs)
SRGANs generate a photorealistic
high-resolution image when given a
low-resolution image
Super Resolution GAN
(SRGAN)

16. Applications of GANs
Generating Cartoon Characters1
Using DCGANs, you can create faces
of anime and Pokemon characters

17. Applications of GANs
Generating Human Faces2
GANs can be trained on the images
of humans to generate realistic faces

18. Applications of GANs
Text to image translation3
GANs can build realistic images from
textual descriptions of simple objects
like birds
Bird with a black
head, yellow body
and a short beak

19. Applications of GANs
3-D Object Generation4
GANs can generate 3-D models
using 2-D pictures of objects from
multiple perspectives

20. Reasons For Decline:
• The language is seen to be bulky and clumsy
• Work nowadays usually involves maintaining legacy applications or migrating to
C#
• Capabilities are limited to the Windows platform
• Harsh declaration syntax and requirements, a rigid development environment
and a lack of libraries
VB. NET1