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Course Outline
Introduction to Pre-trained Models for Government
- What are pre-trained models?
- Benefits of using pre-trained models for government applications
- Overview of popular pre-trained models (e.g., BERT, ResNet)
Understanding Pre-trained Model Architectures for Government
- Model architecture basics for government use cases
- Transfer learning and fine-tuning concepts for government applications
- How pre-trained models are built and trained to meet public sector needs
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) suitable for government projects
- 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 agencies
- Fine-tuning pre-trained models for custom datasets relevant to public sector needs
Deploying Pre-trained Models for Government Operations
- Exporting and saving fine-tuned models for secure government use
- Integrating models into government applications
- Basics of deploying models in production environments for government agencies
Challenges and Best Practices for Government Use
- Understanding model limitations in a public sector context
- Avoiding overfitting during fine-tuning for government datasets
- Ensuring ethical use of AI models in government operations
Future Trends in Pre-trained Models for Government Applications
- Emerging architectures and their applications in the public sector
- Advances in transfer learning for government-specific tasks
- Exploring large language models and multimodal models for enhanced government services
Summary and Next Steps for Government Agencies
Requirements
- Foundational understanding of machine learning principles
- Proficiency in Python programming
- Familiarity with data management using libraries such as Pandas
Audience
- Data scientists for government and private sectors
- AI professionals interested in advancing their skills for government applications
14 Hours
