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Course Outline
Overview of Generative AI Fundamentals
- Brief review of core Generative AI concepts
- Advanced applications and case studies
Comprehensive Study of Generative Adversarial Networks (GANs)
- Detailed examination of GAN architectures
- Techniques for enhancing GAN training
- Conditional GANs and their use cases
- Practical exercise: Designing a complex GAN
Advanced Variational Autoencoders (VAEs)
- Exploring the boundaries of VAE capabilities
- Disentangled representations within VAEs
- Beta-VAEs and their importance
- Practical exercise: Constructing an advanced VAE
Transformers and Generative Models
- Understanding the Transformer architecture
- Utilising Generative Pretrained Transformers (GPT) and BERT for generative tasks
- Fine-tuning strategies for generative models
- Practical exercise: Fine-tuning a GPT model for a specific domain
Diffusion Models
- Introduction to diffusion models
- Training methods for 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 using 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
- Applications in 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 career in Generative AI research
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
- Proficiency in Python programming and basic familiarity with TensorFlow or PyTorch
- Knowledge of neural network principles and deep learning fundamentals
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)
