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Course Outline
Overview of Advanced NLG Techniques
- Recap of fundamental NLG concepts
- Introduction to advanced NLG strategies
- The pivotal role of transformers in contemporary NLG
Pre-Trained Models for NLG
- Survey of widely used pre-trained models (GPT, BERT, T5)
- Fine-tuning pre-trained models for specific objectives
- Training custom models using large-scale datasets
Enhancing NLG Output Quality
- Maintaining coherence and relevance in generated text
- Managing text length and content via NLG controls
- Strategies to minimize repetition and boost fluency
Ethical and Responsible NLG Practices
- Navigating ethical challenges associated with AI-generated content
- Addressing biases within NLG models
- Promoting responsible deployment of NLG technology
Practical Application with Advanced NLG Libraries
- Leveraging Hugging Face Transformers for NLG tasks
- Implementing GPT-3 and other cutting-edge models
- Creating domain-specific content using NLG tools
Evaluating NLG Systems
- Methods for assessing NLG model performance
- Automated evaluation metrics (BLEU, ROUGE, METEOR)
- Human-centric evaluation methods for quality assurance
Emerging Trends in NLG
- New developments in NLG research
- Challenges and opportunities in NLG advancement
- The influence of NLG on industries and content creation
Summary and Future Directions
Requirements
- Fundamental knowledge of NLG principles
- Proficiency in Python programming
- Understanding of machine learning frameworks
Target Audience
- Data Scientists
- AI Developers
- Machine Learning Engineers
14 Hours
