Implementing RNNs for sequence modeling in TensorFlow
Now that we have covered the underlying theory behind RNNs, we are ready to move on to the more practical portion of this chapter: implementing RNNs in TensorFlow. During the rest of this chapter, we will apply RNNs to two common problem tasks:
- Sentiment analysis
- Language modeling
These two projects, which we will walk through together in the following pages, are both fascinating but also quite involved. Thus, instead of providing the code all at once, we will break the implementation up into several steps and discuss the code in detail. If you like to have a big picture overview and want to see all the code at once before diving into the discussion, take a look at the code implementation first, which you can view at https://github.com/rasbt/python-machine-learning-book-3rd-edition/tree/master/ch16.
