Summary

In this chapter, we covered TensorFlow's most essential and useful features. We started by discussing the migration from TensorFlow v1.x to v2. In particular, we used TensorFlow's dynamic computation graph approach, the so-called eager execution mode, which makes implementing computations more convenient compared to using static graphs. We also covered the semantics of defining TensorFlow Variable objects as model parameters, annotating Python functions using the tf.function decorator to improve computational efficiency via graph compilation.

After we considered the concept of computing partial derivatives and gradients of arbitrary functions, we covered the Keras API in more detail. It provides us with a user-friendly interface for building more complex deep NN models. Finally, we utilized TensorFlow's tf.estimator API to provide a consistent interface that is typically preferred in production environments. We concluded this chapter by converting a Keras...