The documents in this unit dive into the details of how TensorFlow works. The units are as follows:
High Level APIs
Eager Execution, which is the easiest way to use tensorflow.
Estimators, which introduces a high-level TensorFlow API that greatly simplifies ML programming.
Importing Data, which explains how to set up data pipelines to read data sets into your TensorFlow program.
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.
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.
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: