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  1. Skills Directory
  2. Natural Language Processing

Natural Language Processing

Advanced

NLP is a subfield of artificial intelligence and computational linguistics that focuses on the interaction between computers and human language. NLP involves the development of algorithms and techniques to enable computers to understand, interpret, and generate human language in a way that is both meaningful and useful.

This competency area includes an understanding of the concepts of text summarization, dialog systems, language understanding and generation, advanced architectures, attention mechanisms, and transformers.

 

Key Competencies:
 

  1. Text Summarization - Creating a concise summary of a long text document

  2. Dialog Systems - Creating chatbots or virtual assistants that can engage in human-like conversations with users

  3. Language Understanding & Generation - Understanding the meaning behind natural language text, such as inferring the intent of a user's query

  4. Neural Machine Translation - Translating text using advanced neural network models, which can improve translation quality over traditional statistical methods.

  5. Advanced Architectures - Understanding advanced architectures such as transformers, graph neural networks, and attention mechanisms, and how to implement them in deep learning libraries such as TensorFlow, Keras, or PyTorch.

  6. Attention Mechanisms - Understanding the API concepts for implementing attention mechanisms in deep learning models.

  7. Transformers - Ability to build and fine-tune pre-trained transformer models such as BERT, GPT-2, or RoBERTa using Python libraries such as Hugging Face Transformers or TensorFlow.