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Duration 21 hours
Course Outline
Introduction to Deep Learning for NLU
- Comparative overview of NLU and NLP
- Application of deep learning in natural language processing
- Specific challenges faced by NLU models
Deep Architectures for NLU
- Transformers and attention mechanisms
- Application of recursive neural networks (RNNs) in semantic parsing
- The function of pre-trained models in NLU
Semantic Understanding and Deep Learning
- Developing models for semantic analysis
- Use of contextual embeddings in NLU
- Tasks involving semantic similarity and entailment
Advanced Techniques in NLU
- Sequence-to-sequence models for contextual understanding
- Deep learning applications in intent recognition
- Transfer learning strategies for NLU
Evaluating Deep NLU Models
- Performance metrics for NLU
- Addressing bias and errors in deep NLU models
- Enhancing interpretability within NLU systems
Scalability and Optimization for NLU Systems
- Model optimization for large-scale NLU workloads
- Efficient management of computing resources
- Techniques for model compression and quantization
Future Trends in Deep Learning for NLU
- Recent innovations in transformers and language models
- Investigation into multi-modal NLU
- Evolution beyond NLP: Contextual and semantic-driven AI
Summary and Next Steps
Requirements
- Advanced proficiency in natural language processing (NLP)
- Practical experience with deep learning frameworks
- Working knowledge of neural network architectures
Target Audience
- Data scientists
- AI researchers
- Machine learning engineers
Testimonials (2)
Getting people that never used AI some repetition in prompting and people that do use AI to consider different methods to using it.
Matthew Gay - Tarsus Pharmaceuticals
Course - Artificial Intelligence (AI) Overview
The training was organized and well-planned out, and I come out of it with systematized knowledge and a good look at topics we looked at
