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Course Outline

Introduction to Small Language Models (SLMs)

  • Comprehensive overview of language modeling frameworks
  • Strategic shift from Large Language Models to SLMs for government use cases
  • Structural architecture and design principles for SLMs
  • Operational benefits and constraints associated with SLMs

Technical Foundations

  • Core concepts of neural networks and parameter efficiency
  • Training methodologies optimized for SLMs
  • Data curation requirements and model optimization techniques
  • Standardized evaluation metrics for language models

SLMs in Natural Language Processing

  • Automated text generation capabilities
  • Language translation and localization services
  • Sentiment analysis and document classification
  • Interactive question answering and conversational agents

Operational Applications of SLMs

  • Mobile implementations: On-device data processing for government personnel
  • Embedded systems: Integration within Internet of Things (IoT) infrastructure
  • Privacy-preserving AI: Secure, local data handling protocols
  • Edge computing: Low-latency operations in remote or restricted environments

Case Studies

  • Evaluation of successful SLM deployments within public sector contexts
  • Sector-specific applications (e.g., Healthcare, Finance) tailored for government standards
  • Comparative analysis: SLMs versus Large Language Models in production environments

Future Directions

  • Emerging research trends in SLM technology
  • Technical challenges related to scaling and deployment for government systems
  • Ethical guidelines and responsible AI implementation
  • Strategic outlook: Development of next-generation SLMs

Practical Workshops

  • Development of a basic SLM for text generation tasks
  • Integration of SLMs into mobile applications for government use
  • Fine-tuning SLMs to meet specific operational requirements
  • Performance evaluation and model interpretability analysis

Capstone Project

  • Identification of problem spaces suitable for SLM application
  • Design and implementation of an SLM solution for government needs
  • Rigorous testing and iterative refinement of the model
  • Presentation of project findings and operational outcomes

Summary and Next Steps

Requirements

  • Fundamental comprehension of machine learning principles
  • Competence in Python programming
  • Expertise in neural networks and deep learning architectures

Audience

  • Data scientists
  • Software developers
  • AI enthusiasts
 14 Hours

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