Course Outline
Introduction to Deep Learning for NLU
- Overview of NLU vs NLP
- Deep learning in natural language processing
- Challenges specific to NLU models
Deep Architectures for NLU
- Transformers and attention mechanisms
- Recursive neural networks (RNNs) for semantic parsing
- Pre-trained models and their role in NLU
Semantic Understanding and Deep Learning
- Building models for semantic analysis
- Contextual embeddings for NLU
- Semantic similarity and entailment tasks
Advanced Techniques in NLU
- Sequence-to-sequence models for understanding context
- Deep learning for intent recognition
- Transfer learning in NLU
Evaluating Deep NLU Models
- Metrics for evaluating NLU performance
- Handling bias and errors in deep NLU models
- Improving interpretability in NLU systems
Scalability and Optimization for NLU Systems
- Optimizing models for large-scale NLU tasks
- Efficient use of computing resources
- Model compression and quantization
Future Trends in Deep Learning for NLU
- Innovations in transformers and language models
- Exploring multi-modal NLU
- Beyond NLP: Contextual and semantic-driven AI
Summary and Next Steps
Requirements
- Advanced knowledge of natural language processing (NLP)
- Experience with deep learning frameworks
- Familiarity with neural network architectures
Audience
- Data scientists
- AI researchers
- Machine learning engineers
Testimonials (3)
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 adaptation of exos to our context and the consideration of our request
Amel Guetat - EURO-INFORMATION DEVELOPPEMENTS
Course - Fraud Detection with Python and TensorFlow
Machine Translated
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
