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Course Outline
Introduction to NLG for Text Summarisation and Content Generation
- Overview of Natural Language Generation (NLG)
- Key differences between NLG and NLP
- Use cases for NLG in content generation
Text Summarisation Techniques in NLG
- Extractive summarisation methods using NLG
- Abstractive summarisation with NLG models
- Evaluation metrics for NLG-based summarisation
Content Generation with NLG
- Overview of NLG generative models: GPT, T5, and BART
- Training NLG models for text generation
- Generating coherent and context-aware text with NLG
Fine-Tuning NLG Models for Specific Applications
- Fine-tuning NLG models like GPT for domain-specific tasks
- Transfer learning in NLG
- Handling large datasets for training NLG models
Tools and Frameworks for NLG
- Introduction to popular NLG libraries (Transformers, OpenAI GPT)
- Hands-on with Hugging Face Transformers and OpenAI API
- Building NLG pipelines for content generation
Ethical Considerations in NLG
- Bias in AI-generated content
- Mitigating harmful or inappropriate NLG outputs
- Ethical implications of NLG in content creation
Future Trends in NLG
- Recent advancements in NLG models
- Impact of transformers on NLG
- Future opportunities in NLG and automated content creation
Summary and Next Steps
Requirements
- Basic understanding of machine learning concepts
- Familiarity with Python programming
- Experience with NLP frameworks
Audience
- AI developers
- Content creators
- Data scientists
21 Hours
