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Duration 21 hours
Course Outline
Fundamentals of Deep Learning in NLU
- Comparative analysis of NLU and NLP
- The role of deep learning in natural language processing
- Specific challenges encountered in NLU modeling
Deep Architectures for NLU
- Transformers and the mechanics of attention
- Applying recursive neural networks (RNNs) to semantic parsing
- The impact of pre-trained models on NLU
Semantic Comprehension through Deep Learning
- Designing models for advanced semantic analysis
- Utilizing contextual embeddings in NLU
- Tasks involving semantic similarity and textual entailment
Sophisticated Methods in NLU
- Sequence-to-sequence models for contextual understanding
- Deep learning approaches for intent recognition
- Implementing transfer learning within NLU
Assessing Deep NLU Models
- Key metrics for measuring NLU performance
- Mitigating bias and addressing errors in deep NLU systems
- Enhancing interpretability in NLU architectures
Scalability and Performance Optimization
- Optimizing models for high-volume NLU workloads
- Maximizing computational efficiency
- Techniques for model compression and quantization
Emerging Trends in Deep Learning for NLU
- Latest innovations in transformers and large language models
- The rise of multi-modal NLU capabilities
- The shift beyond NLP: context-aware and semantic-driven AI
Conclusion and Recommended Next Steps
Requirements
- Advanced proficiency in natural language processing (NLP)
- Practical experience with deep learning frameworks
- 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
