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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
