Course Outline
Refresher on Generative AI Fundamentals
- Brief overview of core Generative AI concepts
- Examination of advanced applications and case studies
In-Depth Analysis of Generative Adversarial Networks (GANs)
- Detailed study of GAN architectures
- Strategies to enhance GAN training stability
- Conditional GANs and their use cases
- Practical project: Designing a sophisticated GAN
Sophisticated Variational Autoencoders (VAEs)
- Investigating the boundaries of VAE capabilities
- Disentangled representations within VAE frameworks
- The role and importance of Beta-VAEs
- Practical project: Constructing an advanced VAE
Transformers in Generative Modeling
- Deep understanding of the Transformer architecture
- Utilizing GPT and BERT for generative purposes
- Fine-tuning methodologies for generative models
- Practical project: Fine-tuning a GPT model for a specialized domain
Diffusion Models
- Overview of diffusion model mechanics
- Training procedures for diffusion models
- Applications in audio and image synthesis
- Practical project: Implementing a diffusion model
Reinforcement Learning within Generative AI
- Fundamentals of reinforcement learning
- Integrating RL techniques with generative architectures
- Use cases in procedural content generation and game design
- Practical project: Generating content via reinforcement learning
Ethics and Bias: Advanced Perspectives
- Impacts of deepfakes and synthetic media
- Identifying and reducing bias in generative systems
- Navigating legal and ethical frameworks
Sector-Specific Applications
- Generative AI in the healthcare sector
- Innovations in entertainment and creative industries
- Applications in scientific research
Emerging Research Trends
- Recent breakthroughs and advancements
- Unresolved problems and research opportunities
- Preparation for a career in Generative AI research
Capstone Project
- Identifying problems suited for Generative AI solutions
- Advanced dataset preparation and augmentation techniques
- Model selection, training, and fine-tuning processes
- Project evaluation, iteration, and final presentation
Conclusions and Future Directions
Requirements
- A solid grasp of core machine learning concepts and algorithms.
- Proficiency in Python programming, along with foundational experience using TensorFlow or PyTorch.
- Knowledge of neural network principles and deep learning fundamentals.
Target Audience
- Data Scientists
- Machine Learning Engineers
- 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
