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Course Outline

Advanced NLG Techniques Overview

  • Revisiting core NLG concepts
  • Introduction to advanced NLG methods
  • The role of transformers in contemporary NLG

Pre-trained Models for NLG

  • Overview of prominent pre-trained models (GPT, BERT, T5)
  • Fine-tuning pre-trained models for specific tasks
  • Training custom models with large datasets

Enhancing NLG Outputs

  • Managing coherence and relevance in text generation
  • Controlling text length and content through NLG methods
  • Techniques to minimize repetition and enhance fluency

Ethical and Responsible NLG

  • Understanding the ethical challenges of AI-generated content
  • Addressing biases within NLG models
  • Ensuring the responsible application of NLG technology

Practical Application with Advanced NLG Libraries

  • Utilizing Hugging Face Transformers for NLG
  • Implementing GPT-3 and other state-of-the-art models
  • Generating domain-specific content using NLG

Evaluating NLG Systems

  • Techniques for assessing NLG models
  • Automated evaluation metrics (BLEU, ROUGE, METEOR)
  • Human evaluation methods for quality assurance

Future Trends in NLG

  • Emerging techniques in NLG research
  • Challenges and opportunities in NLG development
  • The impact of NLG on industries and content creation

Summary and Next Steps

Requirements

  • Foundational understanding of NLG concepts
  • Practical experience with Python programming
  • Familiarity with machine learning models

Target Audience

  • Data scientists
  • AI developers
  • Machine learning engineers
 14 Hours

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  • Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
  • Flexible Schedule: Dates and times adapted to your team's agenda.
  • Format: Online (live), In-company (at your offices), or Hybrid.
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