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Course Outline
Introduction to Generative AI
- Definition and scope of generative AI
- Survey of generative architectures (GANs, VAEs, and others)
- Real-world applications and illustrative case studies
The Imperative for Synthetic Data
- Inherent constraints of authentic datasets
- Managing privacy and security risks
- Strengthening the robustness of AI models
Methods for Synthetic Data Generation
- Techniques employed in the synthesis of data
- Ensuring high quality and diversity in generated data
- Practical workshop: Building your initial synthetic dataset
Assessing Synthetic Data
- Key metrics for evaluating the quality of synthetic data
- Performance comparison between synthetic and real datasets
- In-depth analysis of relevant case studies
Ethical and Regulatory Frameworks
- Navigating the complex ethical landscape
- Adherence to legal standards and compliance requirements
- Balancing technological innovation with responsible practice
Advanced Concepts in Data Synthesis
- Utilizing synthetic data for unsupervised learning tasks
- Techniques for cross-domain data synthesis
- Emerging trends and future directions in generative AI
Capstone Project
- Application of learned concepts to real-world scenarios
- Formulation of a comprehensive synthetic data strategy
- Final assessment and constructive feedback
Summary and Subsequent Steps
Requirements
- Foundational knowledge of core machine learning principles
- Proficiency in Python programming
- Working understanding of standard data science workflows
Target Audience
- Data scientists
- AI practitioners
21 Hours
Testimonials (2)
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
