Chapter 15: Modeling Sequential Data Using Recurrent Neural Networks

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.

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