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Course Outline
Introduction to Natural Language Generation (NLG)
- Defining NLG
- Distinguishing between NLU and NLG
- Real-world applications of NLG
Foundational NLG Techniques
- Template-based text generation
- Statistical approaches for text creation
- The role of machine learning in NLG
Utilizing NLG Models
- Overview of key NLG models (GPT, T5)
- Configuring basic models in Python
- Text generation using pre-trained architectures
Addressing NLG Challenges
- Ensuring text coherence and relevance
- Navigating common text generation pitfalls
- Ethical considerations in AI-created content
Practical NLG Tool Usage
- Introduction to NLG libraries (GPT-2/3, NLTK)
- Generating text for specific scenarios
- Assessing the quality of generated text
Assessing NLG Performance
- Evaluating fluency and coherence in outputs
- Automated vs. human evaluation methods
- Strategies to enhance NLG output quality
Future Directions in NLG
- Emerging research techniques in NLG
- Opportunities and hurdles for future text generation
- The influence of NLG on content creation and AI advancement
Recap and Subsequent Steps
Requirements
- Familiarity with fundamental programming concepts
- Proficiency in Python programming
Target Audience
- Beginners in AI
- Enthusiasts in data science
- Content creators exploring AI-generated text
14 Hours
