TensorFlow Guide

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

High Level APIs

@{$guide/keras}, TensorFlow's high-level API for building and training deep learning models.
@{$guide/eager}, an API for writing TensorFlow code imperatively, like you would use Numpy.
@{$guide/estimators}, a high-level API that provides fully-packaged models ready for large-scale training and production.
@{$guide/datasets}, easy input pipelines to bring your data into your TensorFlow program.

@{$estimators} provides an introduction.
@{$premade_estimators}, introduces Estimators for machine learning.
@{$custom_estimators}, which demonstrates how to build and train models you design yourself.
@{$feature_columns}, which shows how an Estimator can handle a variety of input data types without changes to the model.
@{$datasets_for_estimators} describes using tf.data with estimators.
@{$checkpoints}, which explains how to save training progress and resume where you left off.

@{$using_gpu} explains how TensorFlow assigns operations to devices and how you can change the arrangement manually.
@{$using_tpu} explains how to modify Estimator programs to run on a TPU.

@{$guide/low_level_intro}, which introduces the basics of how you can use TensorFlow outside of the high Level APIs.
@{$guide/tensors}, which explains how to create, manipulate, and access Tensors--the fundamental object in TensorFlow.
@{$guide/variables}, which details how to represent shared, persistent state in your program.
@{$guide/graphs}, 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.
@{$guide/saved_model}, which explains how to save and restore variables and models.

@{$guide/embedding}, 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:

@{$guide/summaries_and_tensorboard}, which introduces TensorBoard.
@{$guide/graph_viz}, which explains how to visualize the computational graph.
@{$guide/tensorboard_histograms} which demonstrates the how to use TensorBoard's histogram dashboard.

@{$guide/version_compat}, which explains backward compatibility guarantees and non-guarantees.
@{$guide/faq}, which contains frequently asked questions about TensorFlow.