Summary

In this chapter, you took your first steps to solving NLP tasks by understanding the primary underlying platform (TensorFlow) on which we will be implementing our algorithms. First, we discussed the underlying details of TensorFlow architecture. Next, we discussed the essential ingredients of a meaningful TensorFlow program. We got to know some new features in TensorFlow 2, such as the AutoGraph feature, in depth. We then discussed more exciting elements in TensorFlow such as data pipelines and various TensorFlow operations.

Specifically, we discussed the TensorFlow architecture by lining up the explanation with an example TensorFlow program; the sigmoid example. In this TensorFlow program, we used the AutoGraph feature to generate a TensorFlow graph; that is, using the tf.function() decorator over the function that performs the TensorFlow operations. Then, a GraphDef object was created representing the graph and sent to the distributed master. The distributed master...