Course Outline
Review of Fundamental Generative AI Concepts
- A concise refresher on core Generative AI principles
- Advanced use cases and industry case studies
In-Depth Exploration of Generative Adversarial Networks (GANs)
- Detailed analysis of GAN architectures
- Strategies for enhancing GAN training stability and performance
- Conditional GANs and their practical uses
- Practical exercise: Architecting a complex GAN
Advanced Variational Autoencoders (VAEs)
- Pushing the boundaries of VAE capabilities
- Achieving disentangled representations within VAEs
- The role and significance of Beta-VAEs
- Practical exercise: Constructing a sophisticated VAE
Integrating Transformers with Generative Models
- Deep understanding of the Transformer architecture
- Utilizing GPT and BERT for generative purposes
- Effective fine-tuning strategies for generative models
- Practical exercise: Adapting a GPT model for a specific domain
Mastering Diffusion Models
- Introduction to the mechanics of diffusion models
- Best practices for training these models
- Applications in synthesizing images and audio
- Practical exercise: Building a functional diffusion model
Reinforcement Learning within Generative AI
- Core principles of reinforcement learning
- Combining reinforcement learning with generative frameworks
- Uses in game design and procedural content creation
- Practical exercise: Generating content via reinforcement learning
Ethics, Bias, and Advanced Considerations
- Addressing deepfakes and synthetic media risks
- Identifying and reducing bias in generative outputs
- Legal and ethical frameworks to consider
Sector-Specific Applications
- The role of Generative AI in healthcare
- Impact on creative industries and entertainment
- Advancements in scientific research
Current Research Trends in Generative AI
- Recent breakthroughs and technical advancements
- Open challenges and areas for future research
- Preparing for a career in Generative AI research
Capstone Project
- Defining a problem well-suited for Generative AI solutions
- Sophisticated dataset preparation and augmentation techniques
- Selecting, training, and refining the optimal model
- Evaluating, iterating on, and presenting the final project
Conclusion and Future Pathways
Requirements
- A solid grasp of core machine learning concepts and algorithms
- Proficiency in Python, along with basic experience using TensorFlow or PyTorch
- Familiarity with the foundational principles of neural networks and deep learning
Target Audience
- Data scientists
- Machine learning engineers
- AI practitioners
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 4350 € + VAT*
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Testimonials (4)
Understanding of AI and LLM. How it works and ehat the watchouts and opportunities are!
Henk Jan Beltman - genuine business
Course - Advanced AI Concepts with Perplexity
Comprehensiveness about the backround; how to do a prompt.
Olaf Kannt - Helios Klinikum Krefeld
Course - Generative AI and Prompt Engineering in Healthcare
the tips and recommended prompts that we can take away from this training
Lee Mei Lin - ST Engineering IHQ Pte Ltd
Course - InVideo AI: Creating Engaging Short-Form Videos
use of proper and effective prompt
