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Course Outline
Overview of Advanced NLG Techniques
- Review of foundational NLG concepts
- Introduction to sophisticated NLG methodologies
- The pivotal role 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 large-scale datasets
Enhancing NLG Outputs
- Managing coherence and relevance in text generation
- Regulating text length and content via NLG strategies
- Strategies for minimizing repetition and boosting fluency
Ethical and Responsible NLG
- Navigating the ethical complexities of AI-generated content
- Mitigating biases inherent in NLG models
- Safeguarding the responsible deployment of NLG technology
Practical Application with Advanced NLG Libraries
- Leveraging Hugging Face Transformers for NLG
- Implementing GPT-3 and other state-of-the-art models
- Producing domain-specific content through NLG
Evaluation of NLG Systems
- Methodologies for assessing NLG model performance
- Automated evaluation metrics (BLEU, ROUGE, METEOR)
- Human-centric evaluation methods for quality assurance
Future Trends in NLG
- Emerging innovations in NLG research
- Key challenges and opportunities in NLG development
- The impact of NLG on various industries and content creation
Summary and Future Directions
Requirements
- Fundamental knowledge of NLG principles
- Proficiency in Python programming
- Working familiarity with machine learning models
Target Audience
- Data Scientists
- AI Developers
- Machine Learning Engineers
14 Hours
