Public Training Outline
The Generative AI
1.0 Introduction to Generative AI
1.1 Overview of Generative AI
- Definition of Generative AI
- Brief history and evolution
- Applications and industries impacted
1.2 Key Concepts and Technologies
- Understanding Machine Learning and Deep Learning
- Introduction to Neural Networks
- Generative Models: GANs, VAEs, Transformers
1.3 Use Cases and Examples
- Image and video generation
- Text generation and language models
- Music and audio synthesis
2.0 Deep Dive into Generative Models
2.1 Generative Adversarial Networks (GANs)
- Architecture and how they work
- Training GANs
- Applications and examples
2.2 Variational Autoencoders (VAEs)
- Architecture and how they work
- Training VAEs
- Applications and differences from GANs
2.3 Transformers and Language Models
- Introduction to Transformers
- How they're used in language models
- Examples and applications
3.0 Practical Applications and Future Directions
3.1 Implementing Generative AI
- Tools and frameworks
- Practical tips for implementation
- Overcoming common challenges
3.2 Ethics and Considerations
- Ethical implications of Generative AI
- Bias and fairness
- Privacy concerns
3.3 Future of Generative AI
- Emerging trends and technologies
- Potential future applications
- Closing remarks and Q&A
Additional Information
This outline provides a comprehensive overview of Generative AI, from its basics to practical applications and future directions,
suitable for a 3-hour public training session.