Natural Language Understanding (NLU) Training in Riga

This instructor-led, live training in Riga (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.

This instructor-led, live training in Riga (online or onsite) is aimed at advanced-level professionals who wish to explore the cutting-edge techniques in NLU and semantic understanding.

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

• Understand the principles of semantic understanding and contextual AI.
• Work with state-of-the-art models like transformers and pre-trained language models.
• Implement NLU techniques for improved AI language comprehension.
• Develop applications that leverage semantic analysis and contextual AI.

This instructor-led, live training in Riga (online or onsite) is aimed at intermediate-level professionals who wish to deepen their expertise in modern NLU techniques.

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

• Implement advanced NLU techniques for AI systems.
• Leverage deep learning models for semantic understanding.
• Perform intent recognition and classification in complex applications.
• Utilize state-of-the-art tools like Hugging Face Transformers for NLU tasks.

This instructor-led, live training in Riga (online or onsite) is aimed at beginner-level participants who wish to learn the fundamentals of NLU and how it fits into the larger NLP landscape.

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

• Understand the foundational concepts of Natural Language Understanding (NLU).
• Differentiate between NLU and NLP.
• Explore basic techniques for language interpretation and context understanding.
• Apply NLU techniques to simple text processing tasks.