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# TensorFlow Guide
The documents in this unit dive into the details of how TensorFlow
works. The units are as follows:
## High Level APIs
* [Keras](../guide/keras.md), TensorFlow's high-level API for building and
training deep learning models.
* [Eager Execution](../guide/eager.md), an API for writing TensorFlow code
imperatively, like you would use Numpy.
* [Importing Data](../guide/datasets.md), easy input pipelines to bring your data into
your TensorFlow program.
* [Estimators](../guide/estimators.md), a high-level API that provides
fully-packaged models ready for large-scale training and production.
## Estimators
* [Premade Estimators](../guide/premade_estimators.md), the basics of premade Estimators.
* [Checkpoints](../guide/checkpoints.md), save training progress and resume where you left off.
* [Feature Columns](../guide/feature_columns.md), handle a variety of input data types without changes to the model.
* [Datasets for Estimators](../guide/datasets_for_estimators.md), use `tf.data` to input data.
* [Creating Custom Estimators](../guide/custom_estimators.md), write your own Estimator.
## Accelerators
* [Using GPUs](../guide/using_gpu.md) explains how TensorFlow assigns operations to
devices and how you can change the arrangement manually.
* [Using TPUs](../guide/using_tpu.md) explains how to modify `Estimator` programs to run on a TPU.
## Low Level APIs
* [Introduction](../guide/low_level_intro.md), which introduces the
basics of how you can use TensorFlow outside of the high Level APIs.
* [Tensors](../guide/tensors.md), which explains how to create,
manipulate, and access Tensors--the fundamental object in TensorFlow.
* [Variables](../guide/variables.md), which details how
to represent shared, persistent state in your program.
* [Graphs and Sessions](../guide/graphs.md), 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](../guide/saved_model.md), which
explains how to save and restore variables and models.
## ML Concepts
* [Embeddings](../guide/embedding.md), 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
* [TensorFlow Debugger](../guide/debugger.md), 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:
* [TensorBoard: Visualizing Learning](../guide/summaries_and_tensorboard.md),
which introduces TensorBoard.
* [TensorBoard: Graph Visualization](../guide/graph_viz.md), which
explains how to visualize the computational graph.
* [TensorBoard Histogram Dashboard](../guide/tensorboard_histograms.md) which demonstrates the how to
use TensorBoard's histogram dashboard.
## Misc
* [TensorFlow Version Compatibility](../guide/version_compat.md),
which explains backward compatibility guarantees and non-guarantees.
* [Frequently Asked Questions](../guide/faq.md), which contains frequently asked
questions about TensorFlow.
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