Designing Data Pipelines with TensorFlow

This course covers one of the major improvements introduced in the TensorFlow 2.0 release: the tf.data module. This module brings a number of performance improvements that make it much easier to build efficient models in TensorFlow. Topics covered include migrating from TensorFlow 1.0 to TensorFlow 2.0, loading data into tf.data.Dataset objects, prepping data and feature engineering, and — most importantly — optimizing data pipeline performance. By the end of this material, you should know how to use the tf.data module in TensorFlow and how to take advantage of many of its performance improvements.

Prerequisites: familiarity with Python and a basic understanding of TensorFlow. Knowledge of the Keras API is helpful but not required.

This module covers the major improvements introduced in TensorFlow 2.0 and specifically what changed with respect to tf.data, the object at the center of this course on designing efficient data pipelines. It also covers how to migrate code from TensorFlow 1.0 to TensorFlow 2.0, as well as a general overview of how a dataset fits together with Keras. By the end of this module, the stage is set for building efficient data pipelines. If you are brand new t...