Populates the guide landing page (getting_stared.md).
# TensorFlow Federated
-This landing page for the Developer's Guide content just sends the user to the
-Federated Core and Federated Learning sections, as well as to the associated
-tutorials.
+Welcome!
-TODO(b/112659243): Populate the content of this and related files.
+TFF consists of two layers described in dedicated guide pages:
+
+* [Federated Learning (FL)](federated_learning.md), high-level interfaces that
+ allow users to plug existing Keras or non-Keras machine learning models into
+ the framework, and perform basic tasks, such as federated training or
+ evaluation, without having to study the details of federated learning
+ algorithms.
+
+* [Federated Core (FC)](federated_core.md), lower-level interfaces that allow
+ users to concisely express their own custom federated algorithms by combining
+ TensorFlow with distributed communication operators within a strongly-typed
+ functional programming environment.
+
+We recommend that you start your journey by reviewing the tutorials listed
+below, as they walk you through the main concepts and APIs offered by TFF
+using practical examples. Before you start, make sure to
+follow the [installation instructions](install.md) to configure your
+environment for use with TFF.
+
+* [Federated Learning for Image Classification]
+ (tutorials/federated_learning_for_image_classification.ipynb) introduces
+ some the key parts of the Federated Learning (FL) API, and demonstrates how
+ to use TFF to simulate federated learning on federated MNIST-like data.
+
+* [Federated Learning for Text Generation]
+ (tutorials/federated_learning_for_text_generation.ipynb) further demonstrates
+ how to use TFF's FL API to refine a serialized pre-trained model for a
+ language modeling task.
+
+* [Custom Federated Algorithms with the Federated Core API]
+ (tutorials/custom_federated_algorithms.ipynb) introduces the key concepts and
+ interfaces offered by the Federated Core API (FC API), and demonstrates how to
+ implement a simple federated averating training algorithm as well as how to
+ perform federated evaluation.
