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Course Outline
Generative AI Fundamentals Review
- Concise recap of core Generative AI concepts
- Advanced applications and illustrative case studies
In-Depth Exploration of Generative Adversarial Networks (GANs)
- Detailed analysis of GAN architectures
- Strategies to enhance GAN training processes
- 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
- Understanding the core Transformer architecture
- Utilizing 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 techniques 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
- 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 grasp 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 methodologies
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)
