Course Outline
Introduction to Generative Artificial Intelligence
- Definition and scope of Generative AI
- Historical development and technological evolution of Generative AI
- Essential terminology and core concepts
Fundamentals of Machine Learning
- Overview of machine learning principles
- Categorization of learning paradigms: Supervised, Unsupervised, and Reinforcement
- Foundational algorithms and modeling techniques
- Data preparation strategies and feature engineering
Deep Learning Foundations
- Neural network architectures and deep learning principles
- Mechanisms of activation functions, loss functions, and optimization algorithms
- Strategies for mitigating overfitting, underfitting, and regularization
- Familiarization with TensorFlow and PyTorch frameworks
Overview of Generative Modeling
- Distinctions between discriminative and generative modeling approaches
- Operational use cases for generative models
Variational Autoencoders (VAEs)
- Principles of autoencoder architectures
- Structural components of VAEs
- The role and importance of latent space representation
- Practical exercise: Development of a basic VAE model
Generative Adversarial Networks (GANs)
- Fundamental concepts of GANs
- Architectural design involving Generator and Discriminator components
- Practical exercise: Implementation of a basic GAN
Advanced Generative Architectures
- Introduction to Transformer-based models
- Practical exercise: Text synthesis utilizing pre-trained GPT models
Ethical Frameworks and Governance Implications
- Addressing bias and ensuring fairness in AI model outputs
- Forward-looking implications and standards for responsible AI adoption
Sectoral Applications of Generative AI
- Integration of Generative AI in scientific research and discovery
Comprehensive Capstone Project
- Conceptualization and proposal development for a Generative AI initiative
- Curation, collection, and preprocessing of project datasets
- Assessment of model performance and formal presentation of findings
Synthesis and Future Trajectory
Requirements
- Proficiency in fundamental programming constructs using Python
- Competence in core mathematical disciplines, with specific emphasis on probability theory and linear algebra
Target Audience
- Technical developers and IT specialists within government agencies
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
