Update README with more detailed context / introduction as well as pointers for issues / PRs.

PiperOrigin-RevId: 380209964

LICENSE

-Copyright 2015 The TensorFlow Authors.  All rights reserved.
+Copyright 2015-present The Keras Authors.  All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
@@ -11,3 +11,181 @@ distributed under the License is distributed on an "AS IS" BASIS,
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README.md

@@ -5,7 +5,176 @@
 This repository hosts the development of the Keras library.
 Read the documentation at [keras.io](https://keras.io/).
 
-## Getting started
+## About Keras
 
-- [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 is a deep learning API written in Python,
+running on top of the machine learning platform [TensorFlow](https://github.com/tensorflow/tensorflow).
+It was developed with a focus on enabling fast experimentation.
+*Being able to go from idea to result as fast as possible is key to doing good research.*
+
+Keras is:
+
+- **Simple** -- but not simplistic. Keras reduces developer *cognitive load*
+to free you to focus on the parts of the problem that really matter.
+- **Flexible** -- Keras adopts the principle of *progressive disclosure of complexity*:
+simple workflows should be quick and easy, while arbitrarily advanced workflows
+should be *possible* via a clear path that builds upon what you've already learned.
+- **Powerful** -- Keras provides industry-strength performance and scalability:
+it is used by organizations and companies including NASA, YouTube, or Waymo.
+
+---
+
+## Keras & TensorFlow 2
+
+[TensorFlow 2](https://www.tensorflow.org/) is an end-to-end, open-source machine learning platform.
+You can think of it as an infrastructure layer for
+[differentiable programming](https://en.wikipedia.org/wiki/Differentiable_programming).
+It combines four key abilities:
+
+- Efficiently executing low-level tensor operations on CPU, GPU, or TPU.
+- Computing the gradient of arbitrary differentiable expressions.
+- Scaling computation to many devices, such as clusters of hundreds of GPUs.
+- Exporting programs ("graphs") to external runtimes such as servers, browsers, mobile and embedded devices.
+
+Keras is the high-level API of TensorFlow 2: an approachable, highly-productive interface
+for solving machine learning problems,
+with a focus on modern deep learning. It provides essential abstractions and building blocks for developing
+and shipping machine learning solutions with high iteration velocity.
+
+Keras empowers engineers and researchers to take full advantage of the scalability
+and cross-platform capabilities of TensorFlow 2: you can run Keras on TPU or on large clusters of GPUs,
+and you can export your Keras models to run in the browser or on a mobile device.
+
+---
+
+## First contact with Keras
+
+The core data structures of Keras are __layers__ and __models__.
+The simplest type of model is the [`Sequential` model](/guides/sequential_model/), a linear stack of layers.
+For more complex architectures, you should use the [Keras functional API](/guides/functional_api/),
+which allows to build arbitrary graphs of layers, or [write models entirely from scratch via subclasssing](/guides/making_new_layers_and_models_via_subclassing/).
+
+Here is the `Sequential` model:
+
+```python
+from tensorflow.keras.models import Sequential
+
+model = Sequential()
+```
+
+Stacking layers is as easy as `.add()`:
+
+```python
+from tensorflow.keras.layers import Dense
+
+model.add(Dense(units=64, activation='relu'))
+model.add(Dense(units=10, activation='softmax'))
+```
+
+Once your model looks good, configure its learning process with `.compile()`:
+
+```python
+model.compile(loss='categorical_crossentropy',
+              optimizer='sgd',
+              metrics=['accuracy'])
+```
+
+If you need to, you can further configure your optimizer. The Keras philosophy is to keep simple things simple,
+while allowing the user to be fully in control when they need to (the ultimate control being the easy extensibility of the source code via subclassing).
+
+```python
+model.compile(loss=tf.keras.losses.categorical_crossentropy,
+              optimizer=tf.keras.optimizers.SGD(
+                  learning_rate=0.01, momentum=0.9, nesterov=True))
+```
+
+You can now iterate on your training data in batches:
+
+```python
+# x_train and y_train are Numpy arrays.
+model.fit(x_train, y_train, epochs=5, batch_size=32)
+```
+
+Evaluate your test loss and metrics in one line:
+
+```python
+loss_and_metrics = model.evaluate(x_test, y_test, batch_size=128)
+```
+
+Or generate predictions on new data:
+
+```python
+classes = model.predict(x_test, batch_size=128)
+```
+
+What you just saw is the most elementary way to use Keras.
+
+However, Keras is also a highly-flexible framework suitable to iterate on state-of-the-art research ideas.
+Keras follows the principle of **progressive disclosure of complexity**: it makes it easy to get started,
+yet it makes it possible to handle arbitrarily advanced use cases,
+only requiring incremental learning at each step.
+
+In much the same way that you were able to train & evaluate a simple neural network above in a few lines,
+you can use Keras to quickly develop new training procedures or exotic model architectures.
+Here's a low-level training loop example, combining Keras functionality with the TensorFlow `GradientTape`:
+
+```python
+import tensorflow as tf
+
+# Prepare an optimizer.
+optimizer = tf.keras.optimizers.Adam()
+# Prepare a loss function.
+loss_fn = tf.keras.losses.kl_divergence
+
+# Iterate over the batches of a dataset.
+for inputs, targets in dataset:
+    # Open a GradientTape.
+    with tf.GradientTape() as tape:
+        # Forward pass.
+        predictions = model(inputs)
+        # Compute the loss value for this batch.
+        loss_value = loss_fn(targets, predictions)
+
+    # Get gradients of loss wrt the weights.
+    gradients = tape.gradient(loss_value, model.trainable_weights)
+    # Update the weights of the model.
+    optimizer.apply_gradients(zip(gradients, model.trainable_weights))
+```
+
+For more in-depth tutorials about Keras, you can check out:
+
+- [Introduction to Keras for engineers](/getting_started/intro_to_keras_for_engineers/)
+- [Introduction to Keras for researchers](/getting_started/intro_to_keras_for_researchers/)
+- [Developer guides](/guides/)
+
+---
+
+## Installation
+
+Keras comes packaged with TensorFlow 2 as `tensorflow.keras`.
+To start using Keras, simply [install TensorFlow 2](https://www.tensorflow.org/install).
+
+---
+
+## Support
+
+You can ask questions and join the development discussion:
+
+- In the [TensorFlow forum](https://discuss.tensorflow.org/).
+- On the [Keras Google group](https://groups.google.com/forum/#!forum/keras-users).
+- On the [Keras Slack channel](https://kerasteam.slack.com). Use [this link](https://keras-slack-autojoin.herokuapp.com/) to request an invitation to the channel.
+
+---
+
+## Opening an issue
+
+You can also post **bug reports and feature requests** (only)
+in [GitHub issues](https://github.com/keras-team/keras/issues).
+
+
+---
+
+## Opening a PR
+
+We welcome contributions! Before opening a PR, please read
+[our contributor guide](https://github.com/keras-team/keras/blob/master/CONTRIBUTING.md).