Course Outline
Introduction to Deep Learning for NLU
- Comparison of NLU versus NLP.
- Deep learning applications in natural language processing.
- Specific challenges associated with NLU models.
Deep Architectures for NLU
- Transformers and attention mechanisms.
- Recursive neural networks (RNNs) for semantic parsing.
- The role of pre-trained models in NLU.
Semantic Understanding and Deep Learning
- Developing models for semantic analysis.
- Contextual embeddings for NLU applications.
- Semantic similarity and entailment tasks.
Advanced Techniques in NLU
- Sequence-to-sequence models for contextual understanding.
- Deep learning approaches for intent recognition.
- Transfer learning within NLU contexts.
Evaluating Deep NLU Models
- Metrics for assessing NLU performance.
- Managing bias and errors in deep NLU models.
- Enhancing interpretability in NLU systems.
Scalability and Optimization for NLU Systems
- Optimizing models for large-scale NLU tasks.
- Efficient utilization of computing resources.
- Model compression and quantization strategies.
Future Trends in Deep Learning for NLU
- Innovations in transformers and language models.
- Exploring multi-modal NLU capabilities.
- Beyond NLP: Contextual and semantic-driven AI.
Summary and Next Steps
Requirements
- Advanced understanding of natural language processing (NLP).
- Experience with deep learning frameworks.
- Familiarity with neural network architectures.
Target Audience
- Data scientists.
- AI researchers.
- Machine learning engineers.
Custom Corporate Training
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- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
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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
