Addressed reviewer's comments.

Change-Id: If063514deab32682ba6dce9bc1d87c06ee705c43

Author: wwwind

tensorflow_model_optimization/g3doc/guide/clustering/clustering_comprehensive_guide.ipynb

@@ -284,7 +284,7 @@
       "source": [
         "### Cluster convolutional layers per channel\n",
         "\n",
-        "The clustered model could be passed to further optimizations such as a [post training quantization](https://www.tensorflow.org/lite/performance/post_training_quantization). If the quantization is done per channel, then the model should be clustered per channel as well. This increases the accuracy of the clustered and quantized model\n",
+        "The clustered model could be passed to further optimizations such as a [post training quantization](https://www.tensorflow.org/lite/performance/post_training_quantization). If the quantization is done per channel, then the model should be clustered per channel as well. This increases the accuracy of the clustered and quantized model.\n",
         "\n",
         "**Note:** only Conv2D layers are clustered per channel\n",
         "\n",

tensorflow_model_optimization/g3doc/guide/combine/cqat_example.ipynb

@@ -598,7 +598,7 @@
       "source": [
         "## Apply post-training quantization and compare to CQAT model\n",
         "\n",
-        "Next, we use post-training quantization (no fine-tuning) on the clustered model and check its accuracy against the CQAT model. This demonstrates why you would need to use CQAT to improve the quantized model's accuracy. The difference could be not very visible, because the MNIST model is quite small and overparametrized.\n",
+        "Next, we use post-training quantization (no fine-tuning) on the clustered model and check its accuracy against the CQAT model. This demonstrates why you would need to use CQAT to improve the quantized model's accuracy. The difference may not be very visible, because the MNIST model is quite small and overparametrized.\n",
         "\n",
         "First, define a generator for the callibration dataset from the first 1000 training images."
       ]