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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 Techniques in NLG
- Template-based generation methods
- Statistical models for text creation
- An introduction to machine learning within NLG
Utilizing NLG Models
- Survey of NLG architectures (GPT, T5)
- Configuring basic models using Python
- Producing text via pre-trained models
Challenges in NLG
- Managing coherence and relevance
- Typical issues encountered in text generation
- Ethical aspects of AI-generated content
Practical Application with NLG Tools
- Introduction to NLG libraries (GPT-2/3, NLTK)
- Generating text tailored to specific needs
- Assessing the quality of generated text
Evaluating NLG Models
- Measuring fluency and coherence in generated outputs
- Comparing automated and human evaluation methods
- Enhancing the quality of NLG results
Future Directions in NLG
- New developments in NLG research
- Potential 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
- Basic proficiency in Python programming
Target Audience
- Beginners in the field of AI
- Data science enthusiasts
- Content creators seeking to utilize AI for text generation
14 Hours
