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.
Duration 21 hours
Course Outline
Introduction to AI-Driven NLG
- Fundamentals of Natural Language Generation (NLG)
- The function of NLG within conversational AI ecosystems
- Distinctions between NLU and NLG workflows
Deep Learning Techniques for NLG
- Transformers and pre-trained language model architectures
- Training strategies for optimized dialogue generation
- Managing long-term dependencies in multi-turn conversations
Chatbot Frameworks and NLG Integration
- Embedding NLG capabilities into platforms like Rasa and BotPress
- Crafting personalized and context-aware chatbot responses
- Enhancing user engagement via contextual AI mechanisms
Advanced NLG Models for Virtual Assistants
- Utilizing cutting-edge models such as GPT-3 and BERT
- Generating coherent multi-turn dialogues with AI
- Refining the fluency and naturalness of virtual assistant output
Ethical and Practical Considerations
- Identifying and mitigating bias in AI-generated content
- Establishing transparency and trust in chatbot interactions
- Addressing privacy and security protocols for virtual assistants
Evaluation and Optimization of NLG Systems
- Assessing NLG quality using metrics like BLEU, ROUGE, and human evaluation
- Tuning NLG performance for low-latency, real-time applications
- Adapting NLG solutions for specific domain use cases
Future Trends in NLG and Conversational AI
- Emerging self-supervised learning techniques for NLG
- Leveraging multimodal AI to create more interactive experiences
- Progress in context-aware conversational AI technologies
Summary and Next Steps
Requirements
- Profound knowledge of Natural Language Processing (NLP) fundamentals.
- Practical experience with machine learning and AI model development.
- Proficiency in Python programming.
Target Audience
- AI Developers.
- Chatbot Architects and Designers.
- Virtual Assistant Engineers.
