Course Outline
Review of Generative AI Basics for Government
- Brief overview of Generative AI concepts
- Advanced applications and case studies relevant to public sector operations
Deep Dive into Generative Adversarial Networks (GANs) for Government
- Comprehensive study of GAN architectures
- Techniques to enhance GAN training for government applications
- Conditional GANs and their practical uses in public sector projects
- Hands-on project: Designing a complex GAN tailored for government needs
Advanced Variational Autoencoders (VAEs) for Government
- Exploring the capabilities and limitations of VAEs in public sector contexts
- Disentangled representations in VAEs and their significance for government data
- Beta-VAEs and their applications in enhancing government datasets
- Hands-on project: Building an advanced VAE to address specific government challenges
Transformers and Generative Models for Government
- Understanding the Transformer architecture and its relevance for government tasks
- Utilizing Generative Pretrained Transformers (GPT) and BERT for generative tasks in public sector applications
- Fine-tuning strategies for generative models to meet government requirements
- Hands-on project: Fine-tuning a GPT model for a specific government domain
Diffusion Models for Government
- Introduction to diffusion models and their potential in public sector projects
- Training diffusion models for government-specific tasks
- Applications in image and audio generation for government operations
- Hands-on project: Implementing a diffusion model to support government initiatives
Reinforcement Learning in Generative AI for Government
- Basics of reinforcement learning and its applicability to public sector challenges
- Integrating reinforcement learning with generative models for government tasks
- Applications in game design, procedural content generation, and other public sector uses
- Hands-on project: Creating content with reinforcement learning for government applications
Advanced Topics in Ethics and Bias for Government
- Deepfakes and synthetic media in the context of government operations
- Detecting and mitigating bias in generative models to ensure fair and equitable outcomes
- Legal and ethical considerations for government use of generative AI
Industry-Specific Applications for Government
- Generative AI in healthcare for improving public health initiatives
- Creative industries and entertainment for enhancing public engagement
- Generative AI in scientific research to support government innovation
Research Trends in Generative AI for Government
- Latest advancements and breakthroughs relevant to government operations
- Open problems and research opportunities in the public sector
- Preparing for a research career in Generative AI with a focus on government applications
Capstone Project for Government
- Identifying a problem suitable for Generative AI solutions 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 to support government decision-making
Summary and Next Steps for Government
Requirements
- An understanding of foundational machine learning concepts and algorithms for government applications.
- Experience with Python programming and basic usage of TensorFlow or PyTorch for government projects.
- Familiarity with the principles of neural networks and deep learning for government use cases.
Audience
- Data scientists in public sector roles
- Machine learning engineers working for government agencies
- AI practitioners supporting governmental initiatives
Testimonials (3)
Trainers can answer all questions and accept any queries
Dewi Anggryni - PT Dentsu International Indonesia
Course - Copilot for Finance and Accounting Professionals
Going over the various use cases and application of AI was helpful. I enjoyed the walkthrough of the various AI Agents.
Axel Schulz - CANARIE Inc
Course - Microsoft 365 Copilot: AI Productivity Across Word, Excel, PowerPoint, Outlook, and Teams
I liked that trainer had a lot of knowledge and shared it with us
