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Duration 21 hours
Course Outline
Introduction to Deep Learning for Natural Language Understanding
- Comparing NLU with traditional NLP
- The role of deep learning in language processing
- Specific challenges faced by NLU models
Deep Architectures Designed for NLU
- Transformers and attention mechanisms
- Recursive neural networks (RNNs) for semantic parsing
- The significance of pre-trained models in NLU
Semantic Comprehension and Deep Learning
- Developing models for semantic analysis
- Utilizing contextual embeddings for NLU
- Addressing semantic similarity and entailment tasks
Advanced Techniques in NLU
- Sequence-to-sequence models for contextual understanding
- Applying deep learning to intent recognition
- Implementing transfer learning within NLU contexts
Evaluating Deep NLU Models
- Key metrics for assessing NLU performance
- Managing bias and errors in deep NLU systems
- Enhancing the interpretability of NLU applications
Scalability and Optimization for NLU Systems
- Optimizing models for large-scale NLU operations
- Maximizing efficiency of computing resources
- Techniques for model compression and quantization
Future Trends in Deep Learning for Natural Language Understanding
- Innovations in transformers and language modeling
- Exploring multi-modal NLU capabilities
- Beyond traditional NLP: Contextual and semantic-driven AI
Summary and Next Steps
Requirements
- Proficient understanding of natural language processing (NLP) concepts
- Practical experience with deep learning frameworks
- Familiarity with neural network structures
Target Audience
- Data scientists
- Artificial intelligence 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
