Generative Adversarial Network ppt for beginners .

This is a ppt of GAN

1. Generative Adversarial Networks (GANs) Session 2 Presented by Jayachandran.S . Assistant Professor in Electronics.

2. What are GANs? • Invented by Ian Goodfellow in 2014 • GANs are a type of machine learning model • They generate new data that looks similar to real data

3. Components of GANs • Two main parts: - Generator: Creates fake data - Discriminator: Evaluates data authenticity • They compete with each other

4. How GANs Work • Both improve over time through training • The generator makes fake data • The discriminator checks if the data is real or fake

5. Iterative Process • Generator aims to fool the discriminator • Discriminator aims to correctly identify real vs. fake data • Both networks learn and improve

6. Applications of GANs • Data augmentation for training other models • Image generation and editing • Creating realistic photos and art • Style transfer and super-resolution

7. Advantages of GANs • Can generate high-quality, realistic data • Useful in various fields (e.g., art, medicine) • Enhances data availability for training other models

8. Challenges of GANs • Requires a lot of computational resources • Difficult to train • Risk of generating harmful or misleading data

9. Future of GANs • Ethical considerations and regulations • Continuous research and improvements • More applications in different industries

10. Summary • Used in many innovative applications • GANs are powerful tools for generating realistic data • Consist of generator and discriminator

11. Questions? [Leave this slide open for audience questions]

12. Thank You for Your Time 😊 End of the presentation.