Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Overview of Advanced NLG Techniques
- Reviewing fundamental NLG concepts
- Introduction to sophisticated NLG methodologies
- The role of transformers in contemporary NLG
Pre-trained Models for NLG
- Survey of prominent pre-trained models (GPT, BERT, T5)
- Fine-tuning pre-trained models for specific tasks
- Training bespoke models utilizing large datasets
Enhancing NLG Outputs
- Managing coherence and relevance in text generation
- Regulating text length and content via NLG methods
- Strategies for minimizing repetition and boosting fluency
Ethical and Responsible NLG
- Exploring the ethical implications of AI-generated content
- Addressing biases within NLG models
- Ensuring the responsible deployment of NLG technology
Practical Work with Advanced NLG Libraries
- Utilizing Hugging Face Transformers for NLG
- Implementing GPT-3 and other state-of-the-art models
- Creating domain-specific content using NLG
Evaluating NLG Systems
- Methodologies for assessing NLG models
- Automated evaluation metrics (BLEU, ROUGE, METEOR)
- Human-centric evaluation methods for quality assurance
Future Directions in NLG
- Emerging innovations in NLG research
- Challenges and opportunities in NLG development
- The influence of NLG on various industries and content creation
Summary and Recommended Next Steps
Requirements
- A foundational understanding of NLG principles
- Proficiency in Python programming
- Knowledge of machine learning models
Target Audience
- Data scientists
- AI developers
- Machine learning engineers
14 Hours
