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Course Outline
Introduction to Deep Learning for NLU
- Overview of NLU vs NLP
- Deep learning 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
- Pre-trained models and their significance in NLU
Semantic Understanding and Deep Learning
- Constructing models for semantic analysis
- Contextual embeddings for NLU
- Semantic similarity and entailment tasks
Advanced Techniques in NLU
- Sequence-to-sequence models for context understanding
- Deep learning for intent recognition
- Transfer learning in NLU
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
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 understanding of natural language processing (NLP)
- Hands-on experience with deep learning frameworks
- Familiarity with neural network architectures
Target Audience
- Data scientists
- AI researchers
- Machine learning engineers
21 Hours
Testimonials (2)
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
Magdalena - Samsung Electronics Polska Sp. z o.o.
Course - Deep Learning with TensorFlow 2
I really liked the end where we took the time to play around with CHAT GPT. The room was not set up the best for this- instead of one large table a couple of small ones so we could get into small groups and brainstorm would have helped
