Course Outline
Refresher on Generative AI Fundamentals
- Concise review of core Generative AI principles.
- Examination of advanced use cases and industry case studies.
In-Depth Analysis of Generative Adversarial Networks (GANs)
- Comprehensive study of GAN architectures.
- Strategies to enhance GAN training stability and performance.
- Exploration of Conditional GANs and their specific applications.
- Practical project: Designing and implementing a complex GAN.
Advanced Variational Autoencoders (VAEs)
- Pushing the boundaries of VAE capabilities.
- Achieving disentangled representations within VAE frameworks.
- The role and significance of Beta-VAEs.
- Practical project: Constructing a high-performance VAE.
Transformers in Generative Modeling
- Deep understanding of the Transformer architecture.
- Utilizing Generative Pretrained Transformers (GPT) and BERT for generative objectives.
- Effective fine-tuning strategies for generative models.
- Practical project: Fine-tuning a GPT model for a specialized domain.
Diffusion Models
- Introduction to the mechanics of diffusion models.
- Best practices for training diffusion architectures.
- Applications in high-fidelity image and audio synthesis.
- Practical project: End-to-end implementation of a diffusion model.
Integrating Reinforcement Learning into Generative AI
- Fundamentals of reinforcement learning.
- Synergies between reinforcement learning and generative models.
- Applications in game design and procedural content creation.
- Practical project: Generating content driven by reinforcement learning loops.
Ethics and Bias in Advanced AI
- The impact of deepfakes and synthetic media.
- Methods for detecting and mitigating bias in generative systems.
- Navigating legal frameworks and ethical responsibilities.
Sector-Specific Applications
- Deploying Generative AI in healthcare diagnostics and care.
- Innovation in creative industries and entertainment.
- Accelerating discoveries through Generative AI in scientific research.
Emerging Research Trends in Generative AI
- Reviewing recent breakthroughs and technological advancements.
- Identifying open problems and potential research opportunities.
- Preparing for a career in Generative AI research.
Capstone Project
- Defining a complex problem suitable for Generative AI solutions.
- Advanced dataset curation, preparation, and augmentation.
- Strategic model selection, rigorous training, and fine-tuning.
- Comprehensive evaluation, iterative refinement, and final project presentation.
Conclusion and Future Directions
Requirements
- A solid grasp of core machine learning concepts and algorithms.
- Proficiency in Python programming with practical experience using TensorFlow or PyTorch.
- A strong foundation in neural network principles and deep learning.
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
