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Course Outline
Overview of Advanced NLG Techniques
- Revisiting fundamental NLG concepts
- Introduction to advanced NLG methodologies
- The role of transformers in contemporary 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
Enhancing NLG Outputs
- Managing coherence and relevance in text generation
- Controlling text length and content via NLG methods
- Techniques for reducing repetition and enhancing fluency
Ethical and Responsible NLG
- Understanding the ethical challenges of AI-generated content
- Addressing biases in NLG models
- Ensuring the responsible use of NLG technology
Practical Application 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
- Foundational understanding of NLG concepts
- Proficiency in Python programming
- Familiarity with machine learning models
Target Audience
- Data scientists
- AI developers
- Machine learning engineers
14 Hours
