Chapter 16. Modeling Sequential Data Using Recurrent Neural Networks

In the previous chapter, we focused on Convolutional Neural Networks (CNNs) for image classification. In this chapter, we will explore Recurrent Neural Networks (RNNs) and see their application in modeling sequential data and a specific subset of sequential data—time-series data. As an overview, in this chapter, we will cover the following topics:

• Introducing sequential data

• RNNs for modeling sequences

• Long Short-Term Memory (LSTM)

• Truncated Backpropagation Through Time (T-BPTT)

• Implementing a multilayer RNN for sequence modeling in TensorFlow

• Project one – RNN sentiment analysis of the IMDb movie review dataset

• Project two – RNN character-level language modeling with LSTM cells, using text data from Shakespeare's Hamlet

• Using gradient clipping to avoid exploding gradients

Since this chapter is the last in our Python Machine Learning journey, we'll conclude with a summary of what we've learned about RNNs, and an overview of all...