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Duration 21 hours
Course Outline
Introduction to Deep Learning in NLU
- Comparative overview of NLU and NLP
- The role of deep learning in natural language processing
- Unique challenges facing NLU models
Deep Architectures for NLU
- Transformers and the mechanics of attention
- Recursive neural networks (RNNs) applied to semantic parsing
- The impact of pre-trained models on NLU
Semantic Understanding and Deep Learning
- Developing models for semantic analysis
- Utilizing contextual embeddings in NLU
- Tasks involving semantic similarity and entailment
Advanced NLU Techniques
- Sequence-to-sequence models for context awareness
- Deep learning approaches for intent recognition
- Applications of transfer learning in NLU
Evaluation of Deep NLU Models
- Performance metrics for NLU assessment
- Mitigating bias and error in deep NLU models
- Enhancing interpretability within NLU systems
Scalability and Optimization of NLU Systems
- Optimizing models for large-scale NLU operations
- Efficient allocation of computing resources
- Techniques for model compression and quantization
Future Directions in Deep Learning for NLU
- Evolution of transformers and language models
- Investigations into multi-modal NLU
- Expanding beyond NLP: Contextual and semantic-driven AI
Concluding Remarks and Subsequent Steps
Requirements
- In-depth knowledge of Natural Language Processing (NLP)
- Proficiency with deep learning frameworks
- Understanding 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
