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# Programmer's Guide
The documents in this unit dive into the details of how TensorFlow
works. The units are as follows:
## High Level APIs
* @{$programmers_guide/keras}, TensorFlow's high-level API for building and
training deep learning models.
* @{$programmers_guide/eager}, an API for writing TensorFlow code
imperatively, like you would use Numpy.
* @{$programmers_guide/estimators}, a high-level API that provides
fully-packaged models ready for large-scale training and production.
* @{$programmers_guide/datasets}, easy input pipelines to bring your data into
your TensorFlow program.
## Estimators
* @{$estimators} provides an introduction.
* @{$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.
## Accelerators
* @{$using_gpu} explains how TensorFlow assigns operations to
devices and how you can change the arrangement manually.
* @{$using_tpu} explains how to modify `Estimator` programs to run on a TPU.
## Low Level APIs
* @{$programmers_guide/low_level_intro}, which introduces the
basics of how you can use TensorFlow outside of the high Level APIs.
* @{$programmers_guide/tensors}, which explains how to create,
manipulate, and access Tensors--the fundamental object in TensorFlow.
* @{$programmers_guide/variables}, which details how
to represent shared, persistent state in your program.
* @{$programmers_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.
* @{$programmers_guide/saved_model}, which
explains how to save and restore variables and models.
## ML Concepts
* @{$programmers_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.
## Debugging
* @{$programmers_guide/debugger}, which
explains how to use the TensorFlow debugger (tfdbg).
## TensorBoard
TensorBoard is a utility to visualize different aspects of machine learning.
The following guides explain how to use TensorBoard:
* @{$programmers_guide/summaries_and_tensorboard},
which introduces TensorBoard.
* @{$programmers_guide/graph_viz}, which
explains how to visualize the computational graph.
* @{$programmers_guide/tensorboard_histograms} which demonstrates the how to
use TensorBoard's histogram dashboard.
## Misc
* @{$programmers_guide/version_compat},
which explains backward compatibility guarantees and non-guarantees.
* @{$programmers_guide/faq}, which contains frequently asked
questions about TensorFlow.
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