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Course Outline
Introduction to Natural Language Generation (NLG)
- Defining NLG
- Distinguishing between NLU and NLG
- Real-world applications of NLG
Core NLG Methodologies
- Template-based text generation
- Statistical models for text production
- Intro to machine learning within NLG
Utilizing NLG Models
- Overview of key NLG models (GPT, T5)
- Configuring basic models in Python
- Text generation using pre-trained models
Challenges in NLG
- Ensuring coherence and relevance
- Addressing common text generation issues
- Ethical aspects of AI-generated content
Practical Application of NLG Tools
- Introduction to NLG libraries (GPT-2/3, NLTK)
- Generating text for specific use cases
- Assessing the quality of generated text
Model Evaluation
- Measuring fluency and coherence in output
- Comparing automated vs. human evaluation methods
- Enhancing the quality of NLG results
Future Directions in NLG
- Novel techniques in NLG research
- Future challenges and opportunities in text generation
- The influence of NLG on content creation and AI advancement
Conclusion and Recommended Next Steps
Requirements
- Fundamental knowledge of programming concepts
- Basic proficiency in Python programming
Target Audience
- Beginners in AI
- Data science enthusiasts
- Content creators exploring AI-generated text
14 Hours