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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
- Foundational understanding of machine learning concepts.
- Familiarity with Python programming.
- Knowledge of neural networks and deep learning principles.
Audience
- Data scientists.
- Software developers.
- AI enthusiasts.
14 Hours
