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Course Outline
Introduction to Deep Learning for NLU
- Overview of NLU vs NLP
- Deep learning in natural language processing
- Challenges specific to NLU models
Deep Architectures for NLU
- Transformers and attention mechanisms
- Recursive neural networks (RNNs) for semantic parsing
- Pre-trained models and their role in NLU
Semantic Understanding and Deep Learning
- Building models for semantic analysis
- Contextual embeddings for NLU
- Semantic similarity and entailment tasks
Advanced Techniques in NLU
- Sequence-to-sequence models for understanding context
- Deep learning for intent recognition
- Transfer learning in NLU
Evaluating Deep NLU Models
- Metrics for evaluating NLU performance
- Handling bias and errors in deep NLU models
- Improving interpretability in NLU systems
Scalability and Optimization for NLU Systems
- Optimizing models for large-scale NLU tasks
- Efficient use of computing resources
- Model compression and quantization
Future Trends in Deep Learning for NLU
- Innovations in transformers and language models
- Exploring multi-modal NLU
- Beyond NLP: Contextual and semantic-driven AI
Summary and Next Steps
Requirements
- Advanced knowledge of natural language processing (NLP)
- Experience with deep learning frameworks
- Familiarity with neural network architectures
Audience
- Data scientists
- AI researchers
- Machine learning engineers
21 Hours
