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Duration 21 hours
Course Outline
Introduction to Generative AI
- Defining the scope of generative AI
- Survey of generative architectures (including GANs, VAEs, and more)
- Real-world applications and illustrative case studies
The Case for Synthetic Data
- Constraints inherent in real-world datasets
- Addressing privacy and security risks
- Improving the robustness of AI models
Techniques for Synthetic Data Generation
- Maintaining data quality and diversity
- Hands-on workshop: Building your initial synthetic dataset
Assessing Synthetic Data
- Key metrics for measuring synthetic data fidelity
- Benchmarking synthetic versus real data performance
- In-depth case study examination
Ethical and Legal Dimensions
- Navigating complex ethical landscapes
- Adhering to legal frameworks and compliance standards
- Striking a balance between innovation and accountability
Advanced Concepts in Data Synthesis
- Utilizing synthetic data for unsupervised learning tasks
- Cross-domain data synthesis techniques
- Emerging trends in generative AI
Capstone Project
- Applying learned concepts to practical scenarios
- Formulating a comprehensive synthetic data strategy
- Project evaluation and constructive feedback
Recap and Future Directions
Requirements
- Solid grasp of fundamental machine learning principles
- Proficiency in Python programming
- Familiarity with standard data science processes
Target Audience
- Data scientists
- AI practitioners
Testimonials (3)
Comprehensiveness about the backround; how to do a prompt.
Olaf Kannt - Helios Klinikum Krefeld
Course - Generative AI and Prompt Engineering in Healthcare
the tips and recommended prompts that we can take away from this training
Lee Mei Lin - ST Engineering IHQ Pte Ltd
Course - InVideo AI: Creating Engaging Short-Form Videos
use of proper and effective prompt
