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Course Outline
Introduction to Natural Language Generation (NLG)
- Defining NLG
- Distinctions between NLU and NLG
- Real-world applications of NLG
Fundamental NLG Techniques
- Template-driven generation
- Statistical models for text production
- Basics of machine learning in NLG
Navigating NLG Models
- Overview of prominent NLG models (GPT, T5)
- Configuring basic models in Python
- Generating text via pre-trained models
Obstacles in NLG
- Maintaining coherence and relevance
- Frequent challenges in text generation
- Ethical implications of AI-generated content
Practical Application of NLG Tools
- Introduction to NLG libraries (GPT-2/3, NLTK)
- Creating text tailored to specific use cases
- Assessing the quality of generated text
Assessing NLG Models
- Evaluating fluency and coherence in generated output
- Comparing automated and human evaluation methods
- Enhancing the quality of NLG outputs
Emerging Trends in NLG
- New techniques in NLG research
- Future challenges and opportunities in text generation
- The influence of NLG on content creation and AI advancement
Summary and Next Steps
Requirements
- Familiarity with basic programming concepts
- Working knowledge of Python programming
Target Audience
- Those new to AI
- Enthusiasts of data science
- Content creators keen on AI-driven text generation
14 Hours
