Course Outline
Introduction to Pre-trained Models for Government
- What are pre-trained models?
- Benefits of using pre-trained models for government operations
- Overview of popular pre-trained models (e.g., BERT, ResNet)
Understanding Pre-trained Model Architectures for Government
- Model architecture basics
- Transfer learning and fine-tuning concepts
- How pre-trained models are built and trained for government applications
Setting Up the Environment for Government Use
- Installing and configuring Python and relevant libraries for government systems
- Exploring pre-trained model repositories (e.g., Hugging Face) for government use
- Loading and testing pre-trained models in a secure government environment
Hands-On with Pre-trained Models for Government Applications
- Using pre-trained models for text classification in government documents
- Applying pre-trained models to image recognition tasks for government surveillance and analysis
- Fine-tuning pre-trained models for custom datasets relevant to government operations
Deploying Pre-trained Models in Government Systems
- Exporting and saving fine-tuned models for government use
- Integrating models into government applications for enhanced decision-making
- Basics of deploying models in production environments for government agencies
Challenges and Best Practices for Government Use
- Understanding model limitations in the context of government operations
- Avoiding overfitting during fine-tuning for government datasets
- Ensuring ethical use of AI models in government applications
Future Trends in Pre-trained Models for Government
- Emerging architectures and their applications for government
- Advances in transfer learning for government-specific tasks
- Exploring large language models and multimodal models for government use
Summary and Next Steps for Government Implementation
Requirements
- Basic understanding of machine learning concepts for government applications
- Familiarity with Python programming for data analysis tasks
- Basic knowledge of data handling using libraries such as Pandas
Audience
- Data scientists working in public sector roles
- AI enthusiasts interested in government applications
Testimonials (3)
I really liked the end where we took the time to play around with CHAT GPT. The room was not set up the best for this- instead of one large table a couple of small ones so we could get into small groups and brainstorm would have helped
Nola - Laramie County Community College
Course - Artificial Intelligence (AI) Overview
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete
