minor typo fixes

site/en/r2/tutorials/generative/dcgan.ipynb

@@ -96,7 +96,7 @@
         "\n",
         "![A second diagram of a generator and discriminator](https://tensorflow.org/alpha/tutorials/generative/images/gan2.png)\n",
         "\n",
-        "This notebook demonstrate this process on the MNIST dataset. The following animation shows a series of images produced by the *generator* as it was trained for 50 epochs. The images begin as random noise, and increasingly resemble hand written digits over time.\n",
+        "This notebook demonstrates this process on the MNIST dataset. The following animation shows a series of images produced by the *generator* as it was trained for 50 epochs. The images begin as random noise, and increasingly resemble hand written digits over time.\n",
         "\n",
         "![sample output](https://tensorflow.org/images/gan/dcgan.gif)\n",
         "\n",

site/en/r2/tutorials/keras/overfit_and_underfit.ipynb

@@ -109,7 +109,7 @@
         "\n",
         "In both of the previous examples—classifying movie reviews and predicting fuel efficiency—we saw that the accuracy of our model on the validation data would peak after training for a number of epochs, and would then start decreasing.\n",
         "\n",
-        "In other words, our model would *overfit* to the training data. Learning how to deal with overfitting is important. Although it's often possible to achieve high accuracy on the *training set*, what we really want is to develop models that generalize well to a *testing data* (or data they haven't seen before).\n",
+        "In other words, our model would *overfit* to the training data. Learning how to deal with overfitting is important. Although it's often possible to achieve high accuracy on the *training set*, what we really want is to develop models that generalize well to a *testing set* (or data they haven't seen before).\n",
         "\n",
         "The opposite of overfitting is *underfitting*. Underfitting occurs when there is still room for improvement on the test data. This can happen for a number of reasons: If the model is not powerful enough, is over-regularized, or has simply not been trained long enough. This means the network has not learned the relevant patterns in the training data.\n",
         "\n",

site/en/tutorials/keras/overfit_and_underfit.ipynb

@@ -109,7 +109,7 @@
         "\n",
         "In both of the previous examples—classifying movie reviews, and predicting fuel efficiency—we saw that the accuracy of our model on the validation data would peak after training for a number of epochs, and would then start decreasing.\n",
         "\n",
-        "In other words, our model would *overfit* to the training data. Learning how to deal with overfitting is important. Although it's often possible to achieve high accuracy on the *training set*, what we really want is to develop models that generalize well to a *testing data* (or data they haven't seen before).\n",
+        "In other words, our model would *overfit* to the training data. Learning how to deal with overfitting is important. Although it's often possible to achieve high accuracy on the *training set*, what we really want is to develop models that generalize well to a *testing set* (or data they haven't seen before).\n",
         "\n",
         "The opposite of overfitting is *underfitting*. Underfitting occurs when there is still room for improvement on the test data. This can happen for a number of reasons: If the model is not powerful enough, is over-regularized, or has simply not been trained long enough. This means the network has not learned the relevant patterns in the training data.\n",
         "\n",