Add an overview to the Keras dev guide.

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Author: pcoet

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+# Keras: The high-level API for TensorFlow
+
+Keras is the high-level API of the TensorFlow platform. It provides an
+approachable, highly-productive interface for solving machine learning (ML)
+problems, with a focus on modern deep learning. Keras covers every step of the
+machine learning workflow, from data processing to hyperparameter tuning to
+deployment. It was developed with a focus on enabling fast experimentation.
+
+With Keras, you have full access to the scalability and cross-platform
+capabilities of TensorFlow. You can run Keras on a TPU Pod or large clusters of
+GPUs, and you can export Keras models to run in the browser or on mobile
+devices. You can also serve Keras models via a web API.
+
+Keras is designed to reduce cognitive load by achieving the following goals:
+
+* Offer simple, consistent interfaces.
+* Minimize the number of actions required for common use cases.
+* Provide clear, actionable error messages.
+* Follow the principle of progressive disclosure of complexity: It's easy to get
+  started, and you can complete advanced workflows by learning as you go.
+* Help you write concise, readable code.
+
+## Who should use Keras
+
+The short answer is that every TensorFlow user should use the Keras APIs by
+default. Whether you're an engineer, a researcher, or an ML practitioner, you
+should start with Keras.
+
+There are a few use cases (for example, building tools on top of TensorFlow or
+developing your own high-performance platform) that require the low-level
+[TensorFlow Core APIs](https://www.tensorflow.org/guide/core). But if your use
+case doesn't fall into one
+of the
+[Core API applications](https://www.tensorflow.org/guide/core#core_api_applications),
+you should prefer Keras.
+
+## Keras API components
+
+The core data structures of Keras are [layers](https://keras.io/api/layers/) and
+[models](https://keras.io/api/models/). A layer is a simple input/output
+transformation, and a model is a directed acyclic graph (DAG) of layers.
+
+### Layers
+
+The `tf.keras.layers.Layer` class is the fundamental abstraction in Keras. A
+`Layer` encapsulates a state (weights) and some computation (defined in the
+`tf.keras.layers.Layer.call` method).
+
+Weights created by layers can be trainable or non-trainable. Layers are
+recursively composable: If you assign a layer instance as an attribute of
+another layer, the outer layer will start tracking the weights created by the
+inner layer.
+
+You can also use layers to handle data preprocessing tasks like normalization
+and text vectorization. Preprocessing layers can be included directly into a
+model, either during or after training, which makes the model portable.
+
+### Models
+
+A model is an object that groups layers together and that can be trained on
+data.
+
+The simplest type of model is the
+[`Sequential` model](https://www.tensorflow.org/guide/keras/sequential_model),
+which is a linear stack of layers. For more complex architectures, you can
+either use the
+[Keras functional API](https://www.tensorflow.org/guide/keras/functional_api),
+which lets you build arbitrary graphs of layers, or
+[use subclassing to write models from scratch](https://www.tensorflow.org/guide/keras/making_new_layers_and_models_via_subclassing).
+
+The `tf.keras.Model` class features built-in training and evaluation methods:
+
+* `tf.keras.Model.fit`: Trains the model for a fixed number of epochs.
+* `tf.keras.Model.predict`: Generates output predictions for the input samples.
+* `tf.keras.Model.evaluate`: Returns the loss and metrics values for the model;
+  configured via the `tf.keras.Model.compile` method.
+
+These methods give you access to the following built-in training features:
+
+* [Callbacks](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks).
+  You can leverage built-in callbacks for early stopping, model checkpointing,
+  and [TensorBoard](https://www.tensorflow.org/tensorboard) monitoring. You can
+  also
+  [implement custom callbacks](https://www.tensorflow.org/guide/keras/writing_your_own_callbacks).
+* [Distributed training](https://www.tensorflow.org/guide/keras/distributed_training).
+  You can easily scale up your training to multiple GPUs, TPUs, or devices.
+* Step fusing. With the `steps_per_execution` argument in
+  `tf.keras.Model.compile`, you can process multiple batches in a single
+  `tf.function` call, which greatly improves device utilization on TPUs.
+
+For a detailed overview of how to use `fit`, see the
+[training and evaluation guide](https://www.tensorflow.org/guide/keras/training_with_built_in_methods).
+To learn how to customize the built-in training and evaluation loops, see
+[Customizing what happens in `fit()`](https://www.tensorflow.org/guide/keras/customizing_what_happens_in_fit).
+
+### Other APIs and tools
+
+Keras provides many other APIs and tools for deep learning, including:
+
+* [Optimizers](https://keras.io/api/optimizers/)
+* [Metrics](https://keras.io/api/metrics/)
+* [Losses](https://keras.io/api/losses/)
+* [Data loading utilities](https://keras.io/api/data_loading/)
+
+For a full list of available APIs, see the
+[Keras API reference](https://keras.io/api/). To learn more about other Keras
+projects and initiatives, see
+[The Keras ecosystem](https://keras.io/getting_started/ecosystem/).
+
+## Next steps
+
+To get started using Keras with TensorFlow, check out the following topics:
+
+* [The Sequential model](https://www.tensorflow.org/guide/keras/sequential_model)
+* [The Functional API](https://www.tensorflow.org/guide/keras/functional)
+* [Training & evaluation with the built-in methods](https://www.tensorflow.org/guide/keras/training_with_built_in_methods)
+* [Making new layers and models via subclassing](https://www.tensorflow.org/guide/keras/custom_layers_and_models)
+* [Serialization and saving](https://www.tensorflow.org/guide/keras/save_and_serialize)
+* [Working with preprocessing layers](https://www.tensorflow.org/guide/keras/preprocessing_layers)
+* [Customizing what happens in fit()](https://www.tensorflow.org/guide/keras/customizing_what_happens_in_fit)
+* [Writing a training loop from scratch](https://www.tensorflow.org/guide/keras/writing_a_training_loop_from_scratch)
+* [Working with RNNs](https://www.tensorflow.org/guide/keras/rnn)
+* [Understanding masking & padding](https://www.tensorflow.org/guide/keras/masking_and_padding)
+* [Writing your own callbacks](https://www.tensorflow.org/guide/keras/custom_callback)
+* [Transfer learning & fine-tuning](https://www.tensorflow.org/guide/keras/transfer_learning)
+* [Multi-GPU and distributed training](https://www.tensorflow.org/guide/keras/distributed_training)
+
+To learn more about Keras, see the following topics at
+[keras.io](http://keras.io):
+
+* [About Keras](https://keras.io/about/)
+* [Introduction to Keras for Engineers](https://keras.io/getting_started/intro_to_keras_for_engineers/)
+* [Introduction to Keras for Researchers](https://keras.io/getting_started/intro_to_keras_for_researchers/)
+* [Keras API reference](https://keras.io/api/)
+* [The Keras ecosystem](https://keras.io/getting_started/ecosystem/)