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Course Outline
Introduction to Natural Language Generation (NLG)
- What is NLG?
- Differences between NLU and NLG.
- Real-world applications of NLG.
Basic NLG Techniques
- Template-based generation.
- Statistical models for text generation.
- Introduction to machine learning in NLG.
Working with NLG Models
- Overview of NLG models (GPT, T5).
- Setting up basic models in Python.
- Generating text using pre-trained models.
Challenges in NLG
- Managing coherence and relevance.
- Common issues in text generation.
- Ethical considerations in AI-generated content.
Hands-On with NLG Tools
- Introduction to NLG libraries (GPT-2/3, NLTK).
- Generating text for specific use cases.
- Evaluating the quality of generated text.
Evaluating NLG Models
- Measuring fluency and coherence in generated text.
- Automated versus human evaluation techniques.
- Enhancing the quality of NLG outputs.
Future Trends in NLG
- Emerging techniques in NLG research.
- Challenges and opportunities for future text generation.
- The impact of NLG on content creation and AI development.
Summary and Next Steps
Requirements
- Fundamental understanding of programming concepts.
- Familiarity with Python programming.
Audience
- Beginners in AI.
- Data science enthusiasts.
- Content creators interested in AI-generated text.
14 Hours
