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Course Outline
Introduction to Natural Language Generation (NLG)
- Defining NLG
- Distinctions between Natural Language Understanding (NLU) and NLG
- Practical applications of NLG in real-world contexts
Fundamental NLG Methods
- Template-based text creation
- Statistical approaches for text generation
- Basics of machine learning in NLG
Implementing NLG Models
- Overview of prominent NLG models (GPT, T5)
- Configuring basic models within Python
- Generating text using pre-trained models
Addressing NLG Challenges
- Maintaining coherence and relevance
- Typical issues encountered in text generation
- Ethical implications of AI-generated content
Practical Application with NLG Tools
- Introduction to key NLG libraries (GPT-2/3, NLTK)
- Generating text tailored to specific requirements
- Assessing the quality of generated text
Assessing NLG Models
- Measuring fluency and coherence in generated outputs
- 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 Future Actions
Requirements
- Foundational knowledge of programming concepts
- Basic proficiency in Python
Target Audience
- Individuals new to Artificial Intelligence
- Data science enthusiasts
- Content creators keen on AI-driven text generation
14 Hours