Generative AI Advanced

Generative Adversarial Networks (GANs) – Advanced Concepts

• Conditional GANs
  • Explore conditional GANs for controlled data generation.
• Progressive GANs
  • Learn about progressive GANs for generating high-resolution images.

Session 2: Variational Autoencoders (VAEs) – Advanced Techniques

• Variational Autoencoders with Normalizing Flows
  • Dive into advanced VAE techniques for better data generation.
• Disentangled VAEs
  • Understand disentangled VAEs for attribute manipulation in generated data.

Session 3: Reinforcement Learning for GANs

• Reinforcement Learning Basics
  • Introduction to reinforcement learning for GANs.
• Reward Models in GANs
  • Explore reward modeling techniques to improve GAN training.

Session 4: Hands-On Workshop: Advanced GANs and VAEs

• Implementing Conditional GANs
  • Create a conditional GAN for controlled image generation.
• Progressive GANs and Advanced VAEs
  • Practical exercises for progressive GANs and advanced VAEs.

Transformers and Text Generation

• Transformer Architectures
  • In-depth exploration of transformer models for text generation.
• Text Generation with GPT-3
  • Practical applications of GPT-3 for natural language generation.

Session 6: Deep Reinforcement Learning for Creativity

• Reinforcement Learning in Creative Applications
  • Understand how deep reinforcement learning is used for creative tasks.
• Training AI Artists
  • Dive into AI creativity and art generation with RL.

Session 7: Advanced Applications and Case Studies

• Healthcare and Generative AI
  • Explore how generative AI is transforming the healthcare industry.
•  Creative Industries
  • Case studies in art, music, and design using generative AI.

Session 8: Project Showcase and Future Trends

• Project Presentation
  • Participants present advanced generative AI projects they’ve worked on.
• Future Trends in Generative AI
  • Explore emerging trends and innovations in the field.