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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

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