tutorials: minor spelling tweaks (tensorflow#145)

hongyunnchen · Mar 9, 2020 · 8c22f20 · 8c22f20
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diff --git a/docs/tutorials/hello_many_worlds.ipynb b/docs/tutorials/hello_many_worlds.ipynb
@@ -498,7 +498,7 @@
         "\n",
         "- The *input circuit* or *datapoint circuit*: The first three $R$ gates.\n",
         "- The *controlled circuit*: The other three $R$ gates.\n",
-        "- The *controller*: The classical neural-network setting the parameters of the controled circuit."
+        "- The *controller*: The classical neural-network setting the parameters of the controlled circuit."
       ]
     },
     {
@@ -607,9 +607,9 @@
         "colab_type": "text"
       },
       "source": [
-        "Use `tfq` to conect the controller to the controlled circuit, as a single `keras.Model`. \n",
+        "Use `tfq` to connect the controller to the controlled circuit, as a single `keras.Model`. \n",
         "\n",
-        "See the [Keras Funtional API guide](https://www.tensorflow.org/guide/keras/functional) for more about this style of model definition.\n",
+        "See the [Keras Functional API guide](https://www.tensorflow.org/guide/keras/functional) for more about this style of model definition.\n",
         "\n",
         "First define the inputs to the model:  "
       ]
@@ -812,7 +812,7 @@
         "colab_type": "text"
       },
       "source": [
-        "Thre are two copies of the circuit, one for each datapoint."
+        "There are two copies of the circuit, one for each datapoint."
       ]
     },
     {

diff --git a/docs/tutorials/mnist.ipynb b/docs/tutorials/mnist.ipynb
@@ -454,7 +454,7 @@
       "source": [
         "The resulting counts do not closely match the reported values, but the exact procedure is not specified.\n",
         "\n",
-        "It is also worth noting here that applying filtering contradictory examples at this point does not totally prevent the model from recieving contradictory training examples: the next step binarizes the data which will cause more collisions. "
+        "It is also worth noting here that applying filtering contradictory examples at this point does not totally prevent the model from receiving contradictory training examples: the next step binarizes the data which will cause more collisions. "
       ]
     },
     {
@@ -566,7 +566,7 @@
         "colab_type": "text"
       },
       "source": [
-        "Compare this circuit to the indices where the image vlaue excedes the threshold:"
+        "Compare this circuit to the indices where the image value exceeds the threshold:"
       ]
     },
     {

diff --git a/docs/tutorials/qcnn.ipynb b/docs/tutorials/qcnn.ipynb
@@ -393,7 +393,7 @@
         "colab_type": "text"
       },
       "source": [
-        "You can see that just like with regular machine learning you create a training and testing set to use to benchmark the model. You can quikly look at some datapoints with:"
+        "You can see that just like with regular machine learning you create a training and testing set to use to benchmark the model. You can quickly look at some datapoints with:"
       ]
     },
     {