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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 TensorFlow
    • 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 TensorFlow
      • 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.