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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
+
+  * @{$guide/keras}, TensorFlow's high-level API for building and
+    training deep learning models.
+  * @{$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
+
+* @{$estimators} provides an introduction.
+* @{$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
+
+  * @{$guide/low_level_intro}, which introduces the
+    basics of how you can use TensorFlow outside of the high Level APIs.
+  * @{$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.
+  * @{$guide/saved_model}, which
+    explains how to save and restore variables and models.
+
+## ML Concepts
+
+  * @{$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
+
+  * @{$guide/debugger}, which
+    explains how to use the TensorFlow debugger (tfdbg).
+
+## TensorBoard
+
+TensorBoard is a utility to visualize different aspects of machine learning.
+The following guides explain how to use TensorBoard:
+
+  * @{$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.
+
+
+## Misc
+
+  * @{$guide/version_compat},
+    which explains backward compatibility guarantees and non-guarantees.
+  * @{$guide/faq}, which contains frequently asked
+    questions about TensorFlow.