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Course Outline
Introduction to Natural Language Generation (NLG)
- Defining NLG.
- Distinguishing between NLU and NLG.
- Real-world applications of NLG.
Fundamental NLG Techniques
- Template-based generation methods.
- Statistical models for text creation.
- Introduction to machine learning within NLG.
Working with NLG Models
- Overview of prominent NLG models (GPT, T5).
- Setting up fundamental models using Python.
- Generating text using pre-trained models.
Challenges in NLG
- Ensuring coherence and relevance in output.
- Addressing common issues in text generation.
- Ethical considerations in AI-generated content.
Hands-On with NLG Tools
- Introduction to key NLG libraries (GPT-2/3, NLTK).
- Generating text for specific use cases.
- Evaluating generated text for quality assurance.
Evaluating NLG Models
- Measuring fluency and coherence in generated text.
- Comparing automated versus human evaluation techniques.
- Strategies for improving 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.
- Working knowledge of Python programming.
Who Should Attend
- Those new to Artificial Intelligence.
- Data science enthusiasts.
- Content creators looking to leverage AI for text generation.
14 Hours
