Generative AI training encompasses teaching models or algorithms to generate unique content like images, text, or music by learning from existing data patterns and structures. This process trains AI systems to produce novel content that mirrors human-created data but is autonomously generated by the machine learning model.
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Module 1: Introduction to Generative AI
Overview of Generative Artificial Intelligence
Understanding Generative Models
Applications and Use Cases
Module 2: Fundamentals of Neural Networks
Introduction to Neural Networks
Deep Learning Basics
Architectures for Generative Models
Module 3: Generative Adversarial Networks (GANs)
Understanding GANs Architecture
Generating Realistic Data with GANs
Module 4: Variational Autoencoders (VAEs)
Concepts of Variational Autoencoders
Latent Space Representation
Applications and Limitations of VAEs
Module 5: Reinforcement Learning for Generative Models
Introduction to Reinforcement Learning
Deep Reinforcement Learning
RL for Generative Modeling
Module 6: Text Generation with AI
Language Models and Text Generation
Module 7: Image Generation Techniques
Generative Image Models
Conditional Image Generation
Progressive Growing GANs (PGGANs)
Module 8: Video and Audio Generation
Generative Video Models
Audio Synthesis with AI
Creating Realistic Video and Audio Content
Module 9: Ethical Considerations in Generative AI
Bias and Fairness in AI
AI Ethics and Responsible AI
Addressing Ethical Challenges in Generative Models
Module 10: Advanced Applications of Generative AI
Medical Image Synthesis
Style Transfer and Domain Adaptation
Generative AI in Robotics
Module 11: Deployment and Optimization
Scalability and Deployment Challenges
Optimizing Generative Models
Real-world Implementation Strategies
Module 12: Future Trends and Research Directions
Emerging Trends in Generative AI
Cutting-edge Research Areas
Prospects and Future Scope
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