Course Outline
Recap of Generative AI Fundamentals
- Brief review of core Generative AI concepts
- Examination of advanced applications and relevant case studies
In-Depth Analysis of Generative Adversarial Networks (GANs)
- Detailed exploration of GAN architectures
- Strategies to enhance GAN training stability and performance
- Implementation and use cases for Conditional GANs
- Practical project: Architecting a complex GAN
Advanced Variational Autoencoders (VAEs)
- Pushing the boundaries of VAE capabilities
- Achieving disentangled representations within VAEs
- Understanding the significance of Beta-VAEs
- Practical project: Constructing a sophisticated VAE
Transformers in Generative Modeling
- Deep dive into the Transformer architecture
- Leveraging Generative Pretrained Transformers (GPT) and BERT for generation tasks
- Effective fine-tuning methodologies for generative models
- Practical project: Adapting a GPT model to a specific domain
Diffusion Models Explained
- Foundational overview of diffusion models
- Processes for training diffusion models
- Applications in synthesizing images and audio
- Practical project: Developing a diffusion model
Integrating Reinforcement Learning into Generative AI
- Core principles of reinforcement learning
- Combining reinforcement learning with generative models
- Use cases in game design and procedural content generation
- Practical project: Generating content using reinforcement learning techniques
Ethics, Bias, and Responsible AI
- Addressing deepfakes and synthetic media risks
- Identifying and reducing bias in generative models
- Navigating legal and ethical frameworks
Sector-Specific Implementations
- Generative AI applications in healthcare
- Innovations in creative industries and entertainment
- Utilizing Generative AI in scientific discovery
Current Research Trends in Generative AI
- Reviewing recent breakthroughs and advancements
- Exploring open problems and research opportunities
- Preparing for a career in Generative AI research
Capstone Project
- Defining a problem domain suitable for Generative AI solutions
- Advanced dataset curation and augmentation
- Selecting, training, and refining the model
- Evaluating results, iterating, and presenting the final project
Conclusion and Path Forward
Requirements
- A solid grasp of core machine learning concepts and algorithms
- Practical experience in Python programming, including foundational use of 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
