In this chapter, we covered a lot of new and exciting approaches for optimizing your model's performance, both on a general level, and specifically, using the TensorFlow library.
The first part covered techniques for improving your RNN performance by selecting, processing, and transforming your data, as well as tuning your hyperparameters. You also learned how to understand your model in more depth, and now know what should be done to make it work better.
The second part was specifically focused on practical ways of improving your model's performance using the built-in TensorFlow functions. The team at TensorFlow seeks to make it as easy as possible for you to quickly achieve the results you want by providing distributed environments and optimization techniques with just a few lines of code.
Combining both of the techniques covered in this...