SKILL BENCHMARK
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
