Course Code: dlnlu
Duration: 21 hours
Prerequisites:
  • 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
Overview:

This course focuses on advanced deep learning architectures tailored specifically for Natural Language Understanding (NLU), exploring how NLU differs from traditional NLP models. Participants will gain hands-on experience in building deep learning models for semantic understanding and explore future trends in language comprehension.

This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to specialize in cutting-edge deep learning techniques for NLU.

By the end of this training, participants will be able to:

  • Understand the key differences between NLU and NLP models.
  • Apply advanced deep learning techniques to NLU tasks.
  • Explore deep architectures such as transformers and attention mechanisms.
  • Leverage future trends in NLU for building sophisticated AI systems.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.
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

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