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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.