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 knowledge of machine learning concepts
  • Familiarity with Python programming
  • Experience with NLP frameworks

Audience

  • AI developers
  • Content creators
  • Data scientists
 21 Hours

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