Course Outline

Introduction to Small Language Models (SLMs)

  • Overview of language models for government use
  • Evolution from large to Small Language Models for government applications
  • Architecture and design of SLMs for government operations
  • Advantages and limitations of SLMs in the public sector

Technical Foundations

  • Understanding neural networks and parameters for government use
  • Training processes for SLMs in government contexts
  • Data requirements and model optimization for government applications
  • Evaluation metrics for language models in the public sector

SLMs in Natural Language Processing

  • Text generation with SLMs for government communications
  • Language translation and localization for government services
  • Sentiment analysis and text classification for government feedback systems
  • Question answering and chatbots for government assistance

Real-world Applications of SLMs

  • Mobile applications: On-device language processing for government mobile solutions
  • Embedded systems: SLMs in IoT devices for government infrastructure
  • Privacy-preserving AI: Local data processing for government security
  • Edge computing: SLMs in low-latency environments for government operations

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 government production environments

Future Directions

  • Research trends in SLMs for government use
  • Challenges in scaling and deployment for government operations
  • Ethical considerations and responsible AI for government applications
  • The road ahead: Next-generation SLMs for government innovation

Hands-on Workshops

  • Building a simple SLM for text generation for government communications
  • Integrating SLMs into mobile apps for government use
  • Fine-tuning SLMs for specific tasks in government services
  • Performance analysis and model interpretability for government decision-making

Capstone Project

  • Identifying a problem space for SLM application in government operations
  • Designing and implementing an SLM solution for government use
  • Testing and iterating on the model for government efficiency
  • Presenting the project and outcomes to government stakeholders

Summary and Next Steps

Requirements

  • Basic understanding of machine learning concepts for government applications
  • Familiarity with Python programming
  • Knowledge of neural networks and deep learning

Audience

  • Data scientists in the public sector
  • Software developers for government projects
  • AI enthusiasts interested in governmental use cases
 14 Hours

Number of participants


Price per participant

Upcoming Courses

Related Categories