TensorFlow Guide

The documents in this unit dive into the details of how TensorFlow works. The units are as follows:

High Level APIs

  • Keras, TensorFlow's high-level API for building and training deep learning models.
  • Eager Execution, an API for writing TensorFlow code imperatively, like you would use Numpy.
  • Estimators, a high-level API that provides fully-packaged models ready for large-scale training and production.
  • Importing Data, easy input pipelines to bring your data into your TensorFlow program.



  • Using GPUs explains how TensorFlow assigns operations to devices and how you can change the arrangement manually.
  • Using TPUs explains how to modify Estimator programs to run on a TPU.

Low Level APIs

  • Introduction, which introduces the basics of how you can use TensorFlow outside of the high Level APIs.
  • Tensors, which explains how to create, manipulate, and access Tensors--the fundamental object in TensorFlow.
  • Variables, which details how to represent shared, persistent state in your program.
  • Graphs and Sessions, which explains:
    • dataflow graphs, which are TensorFlow's representation of computations as dependencies between operations.
    • sessions, which are TensorFlow's mechanism for running dataflow graphs across one or more local or remote devices. If you are programming with the low-level TensorFlow API, this unit is essential. If you are programming with a high-level TensorFlow API such as Estimators or Keras, the high-level API creates and manages graphs and sessions for you, but understanding graphs and sessions can still be helpful.
  • Save and Restore, which explains how to save and restore variables and models.

ML Concepts

  • Embeddings, which introduces the concept of embeddings, provides a simple example of training an embedding in TensorFlow, and explains how to view embeddings with the TensorBoard Embedding Projector.



TensorBoard is a utility to visualize different aspects of machine learning. The following guides explain how to use TensorBoard: