Course Outline

Advanced NLG Techniques Overview

  • Revisiting basic NLG concepts
  • Introduction to advanced NLG methods
  • Role of transformers in modern NLG

Pre-trained Models for NLG

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

Improving NLG Outputs

  • Handling coherence and relevance in text generation
  • Controlling text length and content using NLG methods
  • Techniques for reducing repetition and improving fluency

Ethical and Responsible NLG

  • Understanding the ethical challenges of AI-generated content
  • Dealing with biases in NLG models
  • Ensuring the responsible use of NLG technology

Hands-On with Advanced NLG Libraries

  • Working with 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 evaluating 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
  • Impact of NLG on industries and content creation

Summary and Next Steps

Requirements

  • Basic understanding of NLG concepts
  • Experience with Python programming
  • Familiarity with machine learning models

Audience

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

Delivery Options

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