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Course Outline
Review of Generative AI Fundamentals
- Concise recap of Generative AI concepts.
- Advanced applications and case studies.
In-Depth Study of Generative Adversarial Networks (GANs)
- Comprehensive exploration of GAN architectures.
- Techniques to enhance GAN training.
- Conditional GANs and their applications.
- Practical project: Designing a complex GAN.
Advanced Variational Autoencoders (VAEs)
- Exploring the limitations of VAEs.
- Disentangled representations in VAEs.
- Beta-VAEs and their significance.
- Practical project: Building an advanced VAE.
Transformers and Generative Models
- Understanding the Transformer architecture.
- Generative Pretrained Transformers (GPT) and BERT for generative tasks.
- Fine-tuning strategies for generative models.
- Practical project: Fine-tuning a GPT model for a specific domain.
Diffusion Models
- Introduction to diffusion models.
- Training diffusion models.
- Applications in image and audio generation.
- Practical 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.
- Practical project: Creating content with reinforcement learning.
Advanced Topics in Ethics and Bias
- Deepfakes and synthetic media.
- Detecting and mitigating bias in generative models.
- Legal and ethical considerations.
Industry-Specific Applications
- Generative AI in healthcare.
- Creative industries and entertainment.
- Generative AI in scientific research.
Research Trends in Generative AI
- Latest advancements and breakthroughs.
- Open problems and 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 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)
