message: Add link to TFlite's supported models table and some copyedits
diff: @@ -37,8 +37,9 @@ a custom (less-dynamic) memory allocator to ensure minimal load, initialization,
  and execution latency.
  TensorFlow Lite provides an interface to leverage hardware acceleration, if
  -available on the device. It does so via the Android Neural Networks library,
  -released as part of Android O-MR1.
  +available on the device. It does so via the
  +[Android Neural Networks API](https://developer.android.com/ndk/guides/neuralnetworks/index.html),
  +available on Android 8.1 (API level 27) and higher.
  ## Why do we need a new mobile-specific library?
  @@ -116,6 +117,10 @@ following:
  Wear](https://research.googleblog.com/2017/02/on-device-machine-intelligence.html)
  to all first-party and third-party apps.
  + Also see the complete list of
  + [TensorFlow Lite's supported models](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/lite/g3doc/models.md),
  + including the model sizes, performance numbers, and downloadable model files.
  +
  - Quantized versions of the MobileNet model, which runs faster than the
  non-quantized (float) version on CPU.
  @@ -131,10 +136,10 @@ compatibility with this release.
  ## Getting Started
  We recommend you try out TensorFlow Lite with the pre-tested models indicated
  -above. If you have an existing mode, you will need to test whether your model is
  -compatible with both the converter and the supported operator set. To test your
  -model, see the [documentation on
  -GitHub](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite).
  +above. If you have an existing model, you will need to test whether your model
  +is compatible with both the converter and the supported operator set. To test
  +your model, see the
  +[documentation on GitHub](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite).
  ### Retrain Inception-V3 or MobileNet for a custom data set
  