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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-based generation approaches
- Statistical models for text production
- Introduction to machine learning applications in NLG
Utilizing NLG Models
- Overview of key NLG models (GPT, T5)
- Configuring basic models in Python
- Producing text via pre-trained models
Challenges in NLG
- Ensuring coherence and relevance
- Common pitfalls in text generation
- Ethical aspects of AI-generated content
Practical Application with NLG Tools
- Introduction to NLG libraries (GPT-2/3, NLTK)
- Generating text for specific scenarios
- Assessing the quality of generated text
Assessing NLG Models
- Measuring fluency and coherence in output text
- Comparing automated and human evaluation methods
- Enhancing the quality of NLG outputs
Future Directions in NLG
- Emerging techniques in NLG research
- Challenges and opportunities for future text generation
- The influence of NLG on content creation and AI advancement
Summary and Next Steps
Requirements
- Fundamental knowledge of programming concepts
- Working familiarity with Python
Target Audience
- Beginners in AI
- Enthusiasts of data science
- Content creators seeking to utilize AI for text generation
14 Hours
