Understanding Generative Models

Part 1

1.Model Exploration

Study various Generative AI models like GANs, VAEs, and autoregressive models.

2.GAN Dynamics

Understand the interplay between generator and discriminator networks in GANs.

3.VAE Proficiency

Grasp the functionalities and strengths of Variational Autoencoders (VAEs).

4.Model Evaluation

Comprehend the strengths and limitations of different Generative AI models.

5. Architecture Experimentation

Experiment with different model architectures and hyperparameters for improved performance.

6. Real-world Applications

Explore how generative models are applied in image, text, and music generation.

7.Ethical Considerations

Recognize biases and ethical implications in Generative AI models.

8.Research Exploration

Stay updated with current research papers and publications in the field.

9. Model Complexity Understanding

Balance between model complexity, performance, and computational resources.

10.Collaboration Impact

Collaborate with peers to reinforce learning and enhance problem-solving skills.