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Course Outline
Review of Generative AI Basics
- Brief overview of Generative AI concepts
- Advanced applications and case studies for government
Deep Dive into Generative Adversarial Networks (GANs)
- Comprehensive study of GAN architectures
- Techniques to enhance GAN training
- Conditional GANs and their applications in public sector workflows
- Hands-on project: Designing a complex GAN for government use cases
Advanced Variational Autoencoders (VAEs)
- Exploring the capabilities and limitations of VAEs
- Disentangled representations in VAEs for improved data governance
- Beta-VAEs and their significance in enhancing model performance
- Hands-on project: Building an advanced VAE tailored for government datasets
Transformers and Generative Models
- Understanding the architecture of Transformer models
- Utilizing Generative Pretrained Transformers (GPT) and BERT for generative tasks in public sector applications
- Strategies for fine-tuning generative models to meet specific domain requirements
- Hands-on project: Fine-tuning a GPT model for government-specific domains
Diffusion Models
- Introduction to diffusion models and their relevance in public sector applications
- Training diffusion models with government datasets
- Applications in image and audio generation for government use
- Hands-on project: Implementing a diffusion model for government projects
Reinforcement Learning in Generative AI
- Fundamentals of reinforcement learning
- Integrating reinforcement learning with generative models for enhanced public sector solutions
- Applications in game design and procedural content generation for government training programs
- Hands-on project: Creating content using reinforcement learning for government applications
Advanced Topics in Ethics and Bias
- Deepfakes and synthetic media in the context of government operations
- Detecting and mitigating bias in generative models to ensure fairness and accountability
- Legal and ethical considerations for government use of generative AI
Industry-Specific Applications
- Generative AI in healthcare for improved patient outcomes
- Creative industries and entertainment for public engagement
- Generative AI in scientific research to advance government initiatives
Research Trends in Generative AI
- Latest advancements and breakthroughs in the field
- Open problems and research opportunities for government-funded projects
- Preparing for a research career focused on generative AI for government applications
Capstone Project
- Identifying a problem suitable for Generative AI in the public sector
- Advanced dataset preparation and augmentation for government data
- Model selection, training, and fine-tuning to meet government standards
- Evaluation, iteration, and presentation of the project for government stakeholders
Summary and Next Steps
Requirements
- A comprehensive understanding of foundational machine learning concepts and algorithms for government applications
- Practical experience with Python programming and the basic use of TensorFlow or PyTorch for government projects
- Knowledge of neural network principles and deep learning techniques for government initiatives
Audience
- Data scientists for government agencies
- Machine learning engineers for government projects
- AI practitioners for government operations
21 Hours
Testimonials (1)
I liked that trainer had a lot of knowledge and shared it with us
