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Course Outline
Introduction to Small Language Models (SLMs)
- Overview of language models for government
- Evolution from large to small language models
- Architecture and design of SLMs
- Advantages and limitations of SLMs in public sector applications
Technical Foundations
- Understanding neural networks and parameters for government use
- Training processes for SLMs in a government context
- Data requirements and model optimization for government systems
- Evaluation metrics for language models in public sector applications
SLMs in Natural Language Processing
- Text generation with SLMs for government communications
- Language translation and localization for multilingual government services
- Sentiment analysis and text classification for government feedback systems
- Question answering and chatbots for government customer service
Real-world Applications of SLMs
- Mobile applications: On-device language processing for government apps
- Embedded systems: SLMs in IoT devices for government infrastructure
- Privacy-preserving AI: Local data processing for secure government operations
- Edge computing: SLMs in low-latency environments for government services
Case Studies
- Analyzing successful deployments of SLMs in government agencies
- Industry-specific applications (Healthcare, Finance, etc.) for government sectors
- Comparative study: SLMs vs. large models in production for government workflows
Future Directions
- Research trends in SLMs for government initiatives
- Challenges in scaling and deployment of SLMs in the public sector
- Ethical considerations and responsible AI for government use
- The road ahead: Next-generation SLMs for government applications
Hands-on Workshops
- Building a simple SLM for text generation for government communications
- Integrating SLMs into mobile apps for government services
- Fine-tuning SLMs for specific tasks in the public sector
- Performance analysis and model interpretability for government applications
Capstone Project
- Identifying a problem space for SLM application in government
- Designing and implementing an SLM solution for government use
- Testing and iterating on the model for government requirements
- Presenting the project and outcomes to government stakeholders
Summary and Next Steps
Requirements
- Basic understanding of machine learning concepts
- Familiarity with Python programming
- Knowledge of neural networks and deep learning
Audience
- Data scientists for government
- Software developers
- AI enthusiasts
14 Hours
