Course Outline

Introduction to Small Language Models (SLMs)

  • Overview of language models
  • Evolution from large to Small Language Models
  • Architecture and design of SLMs
  • Advantages and limitations of SLMs

Technical Foundations

  • Understanding neural networks and parameters
  • Training processes for SLMs
  • Data requirements and model optimization
  • Evaluation metrics for language models

SLMs in Natural Language Processing

  • Text generation with SLMs
  • Language translation and localization
  • Sentiment analysis and text classification
  • Question answering and chatbots

Real-world Applications of SLMs

  • Mobile applications: On-device language processing
  • Embedded systems: SLMs in IoT devices
  • Privacy-preserving AI: Local data processing
  • Edge computing: SLMs in low-latency environments

Case Studies

  • Analyzing successful deployments of SLMs
  • Industry-specific applications (Healthcare, Finance, etc.)
  • Comparative study: SLMs vs. large models in production

Future Directions

  • Research trends in SLMs
  • Challenges in scaling and deployment
  • Ethical considerations and responsible AI
  • The road ahead: Next-generation SLMs

Hands-on Workshops

  • Building a simple SLM for text generation
  • Integrating SLMs into mobile apps
  • Fine-tuning SLMs for specific tasks
  • Performance analysis and model interpretability

Capstone Project

  • Identifying a problem space for SLM application
  • Designing and implementing an SLM solution
  • Testing and iterating on the model
  • Presenting the project and outcomes

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
  • Software developers
  • AI enthusiasts
 14 Hours

Number of participants


Price per participant