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Course Outline
Introduction to NLG for Text Summarization and Content Generation
- Overview of Natural Language Generation (NLG).
- Key differences between NLG and NLP.
- Use cases for NLG in content generation.
Text Summarization Techniques in NLG
- Extractive summarization methods using NLG.
- Abstractive summarization with NLG models.
- Evaluation metrics for NLG-based summarization.
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
