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Duration 14 hours
Course Outline
Overview of Advanced NLG Techniques
- Review of foundational NLG concepts
- Introduction to sophisticated NLG methods
- The significance of transformers in contemporary NLG
Pre-trained Models for NLG
- Survey of leading pre-trained models (GPT, BERT, T5)
- Adapting pre-trained models for specialized tasks
- Training bespoke models utilizing extensive datasets
Enhancing NLG Outputs
- Managing coherence and relevance in text synthesis
- Regulating text length and content via NLG strategies
- Methods to minimize repetition and boost fluency
Ethical and Responsible NLG
- Examining ethical complexities in AI-generated content
- Mitigating biases within NLG models
- Safeguarding the responsible application of NLG technology
Practical Application with Advanced NLG Libraries
- Leveraging Hugging Face Transformers for NLG
- Integrating GPT-3 and other state-of-the-art models
- Creating domain-specific content through NLG
Assessing NLG Systems
- Methodologies for evaluating NLG models
- Automated assessment metrics (BLEU, ROUGE, METEOR)
- Human-centric evaluation approaches for quality control
Emerging Trends in NLG
- Cutting-edge techniques in NLG research
- Challenges and prospects in NLG evolution
- The influence of NLG on industry and content creation
Conclusion and Future Directions
Requirements
- Fundamental knowledge of NLG principles
- Proficiency in Python programming
- Acquaintance with machine learning frameworks
Target Audience
- Data Scientists
- AI Developers
- Machine Learning Engineers
