Deep Learning for Natural Language Processing Proficiency (Advanced Level)

  • 18m
  • 18 questions
The Deep Learning for Natural Language Processing Proficiency (Advanced Level) benchmark measures your working knowledge of deep learning techniques and concepts. You will be evaluated on your ability to work with neural networks, RNNs, memory-based networks, and transfer learning models for developing natural language processing (NLP) applications. Learners who score high on this benchmark demonstrate that they have experience applying deep learning frameworks and techniques used for NLP application development and can work on NLP projects independently without any supervision.

Topics covered

  • analyze the product review data using pandas, graphs, and charts
  • build a ELMo embedding layer for product reviews data classification
  • build a simple language model using ULMFiT on the product reviews data
  • compare results of important features across different networks
  • create an ELMo model for product reviews data classification
  • describe the steps involved in pre-processing the product review dataset
  • describe the steps to load the Amazon Product Reviews dataset into Google Colaboratory
  • illustrate word representations using one-hot encodings
  • illustrate word vector representations using neural network and Word2vec
  • implement and fine tune the LM model using ULMFiT
  • outline key concepts realted to ULMFiT
  • outline key concepts related to ELMo
  • perform review classification using bidirectional long short-term memory (Bi-LSTM)
  • perform review classification using ELMo and FastText
  • perform review classification using GRU
  • perform review classification using LSTM
  • perform review classification using ULMFiT and FastText
  • reshape data to adjust to ELMo embedding layer requirements