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Course Outline
Overview of Generative AI Fundamentals
- Brief review of core Generative AI concepts.
- Advanced applications and relevant case studies.
In-depth Study of Generative Adversarial Networks (GANs)
- Comprehensive examination of GAN architectures.
- Techniques for enhancing GAN training stability and performance.
- Conditional GANs and their practical applications.
- Hands-on project: Designing a complex GAN.
Advanced Variational Autoencoders (VAEs)
- Exploring the capabilities and limitations of VAEs.
- Disentangled representations within VAEs.
- Beta-VAEs and their theoretical significance.
- Hands-on project: Building an advanced VAE.
Transformers and Generative Models
- Understanding the core Transformer architecture.
- Utilizing Generative Pretrained Transformers (GPT) and BERT for generative tasks.
- Fine-tuning strategies tailored for generative models.
- Hands-on project: Fine-tuning a GPT model for a specific domain.
Diffusion Models
- Introduction to the principles of diffusion models.
- Training methodologies for diffusion models.
- Applications in image and audio generation.
- Hands-on project: 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.
- Hands-on project: Creating content using reinforcement learning.
Advanced Ethics and Bias Considerations
- Deepfakes and the rise of synthetic media.
- Strategies for detecting and mitigating bias in generative models.
- Legal and ethical considerations in AI deployment.
Industry-Specific Applications
- Generative AI applications in healthcare.
- Utilization in creative industries and entertainment.
- The role of Generative AI in scientific research.
Current Research Trends in Generative AI
- Latest advancements and notable breakthroughs.
- Open problems and emerging research opportunities.
- Preparing for a research career in Generative AI.
Capstone Project
- Identifying a problem suitable for Generative AI solutions.
- Advanced dataset preparation and augmentation techniques.
- Model selection, training, and fine-tuning processes.
- Evaluation, iteration, and final presentation of the project.
Summary and Future Steps
Requirements
- A solid understanding of fundamental machine learning concepts and algorithms.
- Experience with Python programming and basic proficiency in TensorFlow or PyTorch.
- Familiarity with the principles of neural networks and deep learning.
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)
