ch15

Chapter 15: Modeling Sequential Data Using Recurrent Neural Networks

Chapter Outline

• Introducing sequential data
  • Modeling sequential data—order matters
  • Representing sequences
  • The different categories of sequence modeling
• RNNs for modeling sequences
  • Understanding the RNN looping mechanism
  • Computing activations in an RNN
  • Hidden-recurrence versus output-recurrence
  • The challenges of learning long-range interactions
  • Long short-term memory cells
• Implementing RNNs for sequence modeling in PyTorch
  • Project one: predicting the sentiment of IMDb movie reviews
    • Preparing the movie review data
    • Embedding layers for sentence encoding
    • Building an RNN model
    • Building an RNN model for the sentiment analysis task
      • More on the bidirectional RNN
  • Project two: character-level language modeling in PyTorch
    • Preprocessing the dataset
    • Building a character-level RNN model
    • Evaluation phase: generating new text passages
• Summary

Please refer to the README.md file in ../ch01 for more information about running the code examples.