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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
