Understanding Generative Models
Part 1
1.Model Exploration
Study various Generative AI models like GANs, VAEs, and autoregressive models.
Study various Generative AI models like GANs, VAEs, and autoregressive models.
2.GAN Dynamics
Understand the interplay between generator and discriminator networks in GANs.
Understand the interplay between generator and discriminator networks in GANs.
3.VAE Proficiency
Grasp the functionalities and strengths of Variational Autoencoders (VAEs).
Grasp the functionalities and strengths of Variational Autoencoders (VAEs).
4.Model Evaluation
Comprehend the strengths and limitations of different Generative AI models.
Comprehend the strengths and limitations of different Generative AI models.
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5. Architecture Experimentation
Experiment with different model architectures and hyperparameters for improved performance.
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.
Explore how generative models are applied in image, text, and music generation.
7.Ethical Considerations
Recognize biases and ethical implications in Generative AI models.
Recognize biases and ethical implications in Generative AI models.
8.Research Exploration
Stay updated with current research papers and publications in the field.
Stay updated with current research papers and publications in the field.
9. Model Complexity Understanding
Balance between model complexity, performance, and computational resources.
Balance between model complexity, performance, and computational resources.
10.Collaboration Impact
Collaborate with peers to reinforce learning and enhance problem-solving skills.
Collaborate with peers to reinforce learning and enhance problem-solving skills.
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