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Duration 21 hours
Course Outline
Introduction to NLG for Text Summarization and Content Creation
- Overview of Natural Language Generation (NLG)
- Distinguishing characteristics between NLG and NLP
- Practical applications of NLG in content generation
Text Summarization Methods within NLG
- Extractive summarization approaches utilizing NLG
- Abstractive summarization achieved through NLG models
- Performance metrics for evaluating NLG-based summaries
Creating Content with NLG
- Survey of NLG generative architectures: GPT, T5, and BART
- Training NLG models for text production
- Producing coherent, context-sensitive text using NLG
Adapting NLG Models for Specialized Uses
- Fine-tuning NLG models like GPT for domain-specific objectives
- Application of transfer learning in NLG
- Managing large-scale datasets for NLG model training
Essential Tools and Frameworks for NLG
- Introduction to leading NLG libraries (Transformers, OpenAI GPT)
- Practical work with Hugging Face Transformers and the OpenAI API
- Constructing NLG pipelines for content production
Ethical Dimensions of NLG
- Addressing bias in AI-generated material
- Mitigating harmful or unsuitable NLG outputs
- Ethical ramifications of NLG in content creation
Emerging Trends in NLG
- Latest developments in NLG models
- The influence of transformers on NLG evolution
- Prospective opportunities in NLG and automated content creation
Recap and Future Directions
Requirements
- Foundational understanding of machine learning concepts
- Proficiency in Python programming
- Prior experience utilizing NLP frameworks
Target Audience
- AI developers
- Content creators
- Data scientists
