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1
Intro
2
Photo to Emoji
3
Face Editing
4
Face Aging
5
Text to Image Translation
6
Photo Editing
7
Face Reconstruction
8
Fake Faces
9
Super Resolution
10
Derailing
11
Rosebud AI
12
GAN Networks
13
Generative Models
14
Discriminative Models
15
Generative Model
16
Discriminator
17
Initial Epoch
18
Output
19
Main Motive
20
Conclusion
21
Loss Functions
22
Fake Data
23
Classification Error
24
Loss Function
25
Minimax Loss Function
Description:
Explore the world of Generative Adversarial Networks (GANs) in this comprehensive 29-minute video tutorial. Learn about the fundamental concepts of generative models, the inner workings of GANs, and their training process. Dive into topics such as the distinction between generators and discriminators, the adversarial training framework, and the equilibrium state of GAN models. Discover various applications of GANs, including photo manipulation, face editing, text-to-image translation, and super-resolution techniques. Gain insights into the loss functions used in GAN training, including the minimax loss function, and understand how GANs generate fake data and handle classification errors. By the end of this tutorial, acquire a solid foundation in GANs and their potential in artificial intelligence and deep learning applications.

What Are GANs - Generative Adversarial Networks Explained

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