message: After quantization-aware training launch: documentation updates.
diff: @@ -87,11 +87,11 @@ a smaller model size and faster computation.
  The following types of quantization are available in TensorFlow Lite:
  Technique | Data requirements | Size reduction | Accuracy | Supported hardware
  --------------------------------------------------------------------------------------------------------------- | -------------------------------- | -------------- | --------------------------- | ------------------
  +------------------------------------------------------------------------------------------------------- | -------------------------------- | -------------- | --------------------------- | ------------------
  [Post-training float16 quantization](post_training_float16_quant.ipynb) | No data | Up to 50% | Insignificant accuracy loss | CPU, GPU
  [Post-training dynamic range quantization](post_training_quant.ipynb) | No data | Up to 75% | Accuracy loss | CPU
  [Post-training integer quantization](post_training_integer_quant.ipynb) | Unlabelled representative sample | Up to 75% | Smaller accuracy loss | CPU, EdgeTPU, Hexagon DSP
  -[Quantization-aware training](https://github.com/tensorflow/tensorflow/tree/r1.13/tensorflow/contrib/quantize) | Labelled training data | Up to 75% | Smallest accuracy loss | CPU, EdgeTPU, Hexagon DSP
  +[Quantization-aware training](http://www.tensorflow.org/model_optimization/guide/quantization/training) | Labelled training data | Up to 75% | Smallest accuracy loss | CPU, EdgeTPU, Hexagon DSP
  Below are the latency and accuracy results for post-training quantization and
  quantization-aware training on a few models. All latency numbers are measured on
  @@ -144,11 +144,9 @@ broadly applicable and does not require training data.
  For cases where the accuracy and latency targets are not met, or hardware
  accelerator support is important,
  -[quantization-aware training](https://github.com/tensorflow/tensorflow/tree/r1.13/tensorflow/contrib/quantize){:.external}
  +[quantization-aware training](https://www.tensorflow.org/model_optimization/guide/quantization/training){:.external}
  is the better option. See additional optimization techniques under the
  [Tensorflow Model Optimization Toolkit](https://www.tensorflow.org/model_optimization).
  -Note: Quantization-aware training supports a subset of convolutional neural network architectures.
  -
  If you want to further reduce your model size, you can try [pruning](#pruning)
  prior to quantizing your models.
  