Deep Learning for Natural Language Processing Competency (Intermediate Level)

  • 15m
  • 15 questions
The Deep Learning for Natural Language Processing Competency (Intermediate Level) benchmark measures your understanding and working knowledge of deep learning techniques and concepts, neural networks, RNNs, and memory-based networks for developing natural language processing (NLP) applications. Learners who score high on this benchmark demonstrate that they have a good understanding of deep learning frameworks and techniques used for NLP application development and can work on NLP projects with minimal supervision.

Topics covered

  • construct a RNN model with Word2Vec Embeddings
  • create word embeddings vector using Word2vec
  • define transfer learning and illustrate how it helps to get better results
  • describe the various challenges of RNN
  • illustrate different applications of basic Neural Network-based architecture
  • illustrate different types of LSTM networks
  • illustrate how LSTM networks work better and solve the vanishing gradient problem
  • illustrate sentence vector representations using GloVe vectors
  • illustrate the use of language modeling in Transfer learning
  • outline advantages and challenges of transfer learning in real world problem solving
  • outline gated recurrent unit (GRU) and how it differs from recurrent neural networks (RNNs)
  • outline key concepts related to FastText and Word2Vec
  • outline long short-term memory (LSTM) networks and how they differ from RNN
  • outline the importance of memory-based learning and the different networks it supports
  • perform data preparation for LSTM and GRU networks