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Course Outline
Overview of Generative AI Fundamentals
- Concise review of core Generative AI concepts.
- Advanced applications and illustrative case studies.
In-Depth Analysis of Generative Adversarial Networks (GANs)
- Detailed examination of GAN architectures.
- Strategies to enhance GAN training stability and performance.
- Conditional GANs and their practical uses.
- Practical exercise: Designing a complex GAN.
Advanced Variational Autoencoders (VAEs)
- Investigating the capabilities and limitations of VAEs.
- Disentangled representations within VAEs.
- Beta-VAEs and their importance.
- Practical exercise: Building an advanced VAE.
Transformers and Generative Models
- Comprehensive understanding of the Transformer architecture.
- Utilizing Generative Pretrained Transformers (GPT) and BERT for generative tasks.
- Strategies for fine-tuning generative models.
- Practical exercise: Fine-tuning a GPT model for a specific domain.
Diffusion Models
- Introduction to the principles of diffusion models.
- Methods for training diffusion models.
- Applications in image and audio generation.
- Practical exercise: Implementing a diffusion model.
Reinforcement Learning in Generative AI
- Fundamentals of reinforcement learning.
- Integrating reinforcement learning with generative models.
- Applications in game design and procedural content generation.
- Practical exercise: Creating content with reinforcement learning.
Ethical Considerations and Bias in Advanced Contexts
- Deepfakes and the realm of synthetic media.
- Techniques for detecting and mitigating bias in generative models.
- Legal and ethical frameworks.
Industry-Specific Applications
- The role of Generative AI in healthcare.
- Impacts on creative industries and entertainment.
- Applications of Generative AI in scientific research.
Research Trends in Generative AI
- Recent advancements and significant breakthroughs.
- Open problems and potential research opportunities.
- Preparing for a research career in Generative AI.
Capstone Project
- Identifying a problem suitable for Generative AI.
- Advanced dataset preparation and augmentation.
- Model selection, training, and fine-tuning.
- Evaluation, iteration, and presentation of the project.
Summary and Next Steps
Requirements
- A solid grasp of fundamental machine learning concepts and algorithms.
- Proficiency in Python programming and foundational knowledge of TensorFlow or PyTorch.
- Understanding of neural network principles and deep learning mechanics.
Target Audience
- Data scientists.
- Machine learning engineers.
- AI practitioners.
21 Hours
Testimonials (2)
The interactive style, the exercises
Tamas Tutuntzisz
Course - Introduction to Prompt Engineering
A great repository of resources for future use, instructor's style (full of good sense of humor, great level of detail)
