Generative Adversarial Networks (GANs) In Deep Learning

1
Generative Adversarial Networks (GANs) In Deep
Learning
https//1stepgrow.com/course/advance-data-science-
and-artificial-intelligence-course/
2
Table Of Content

1. What Is a GANs Algorithm?
2. Why Is the GANs Algorithm Important?
3. How Does GANs Algorithm Work?
4. Applications Of GANs Algorithm

https//1stepgrow.com/course/advance-data-science-
and-artificial-intelligence-course/
3
What Is a GANs Algorithm?
In a generative adversarial network, two neural
networks compete with one another using deep
learning techniques to make more accurate
predictions.
https//1stepgrow.com/course/advance-data-science-
and-artificial-intelligence-course/
4
Why Is the GANs Algorithm Important?
The primary goal of generative adversarial
networks is to develop new data that has similar
properties as the training examples by learning
from a collection of training data. It is made
up of a generating and a discriminator model
for neural networks.
https//1stepgrow.com/course/advance-data-science-
and-artificial-intelligence-course/
5
How Does GANs Algorithm Work?
A generative adversarial network input in its
most basic configuration is statistical
noise. After that, the generator turns this noise
into a useful output. We may cause the
generative adversarial network to generate a
broad range of information by adding noise, and
samples from various locations throughout the
target distribution.
https//1stepgrow.com/course/advance-data-science-
and-artificial-intelligence-course/
6
Applications Of GANs Algorithm

1. Excellent Resolution.
2. Picture-to-text translation.
3. Generation of the Face Frontal View.
4. Make up fresh human poses.
5. From images to emojis.
6. Inpainting of a photograph.
7. Translation for clothing.

https//1stepgrow.com/course/advance-data-science-
and-artificial-intelligence-course/
7
THANK YOU!
For Information,Please Visit https//1stepgrow.c
om/