diff --git a/courses/udacity_deep_learning/2_fullyconnected.ipynb b/courses/udacity_deep_learning/2_fullyconnected.ipynb
index 6265bb3fe7c..f87b2635763 100644
--- a/courses/udacity_deep_learning/2_fullyconnected.ipynb
+++ b/courses/udacity_deep_learning/2_fullyconnected.ipynb
@@ -131,15 +131,15 @@
         "id": "nCLVqyQ5vPPH"
       },
       "source": [
-        "We're first going to train a multinomial logistic regression using simple gradient descent.\n",
+        "We will first train a multinomial logistic regression using simple gradient descent.\n",
         "\n",
         "TensorFlow works like this:\n",
-        "* First you describe the computation that you want to see performed: what the inputs, the variables, and the operations look like. These get created as nodes over a computation graph. This description is all contained within the block below:\n",
+        "* First, you describe the computation that you want to see performed: what the inputs, the variables, and the operations look like. These get created as nodes over a computation graph. This description is all contained within the block below:\n",
         "\n",
         "      with graph.as_default():\n",
         "          ...\n",
         "\n",
-        "* Then you can run the operations on this graph as many times as you want by calling `session.run()`, providing it outputs to fetch from the graph that get returned. This runtime operation is all contained in the block below:\n",
+        "* Then you can run the operations on this graph as many times as you want by calling the `session.run()`, providing it outputs to fetch from the graph that gets returned. This runtime operation is all contained in the block below:\n",
         "\n",
         "      with tf.Session(graph=graph) as session:\n",
         "          ...\n",
diff --git a/courses/udacity_deep_learning/4_convolutions.ipynb b/courses/udacity_deep_learning/4_convolutions.ipynb
index 87676a2d2a0..557e5f1e1a0 100644
--- a/courses/udacity_deep_learning/4_convolutions.ipynb
+++ b/courses/udacity_deep_learning/4_convolutions.ipynb
@@ -14,7 +14,7 @@
         "\n",
         "Previously in `2_fullyconnected.ipynb` and `3_regularization.ipynb`, we trained fully connected networks to classify [notMNIST](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html) characters.\n",
         "\n",
-        "The goal of this assignment is make the neural network convolutional."
+        "The goal of this assignment is to make the neural network convolutional."
       ]
     },
     {
@@ -139,7 +139,7 @@
         "id": "5rhgjmROXu2O"
       },
       "source": [
-        "Let's build a small network with two convolutional layers, followed by one fully connected layer. Convolutional networks are more expensive computationally, so we'll limit its depth and number of fully connected nodes."
+        "Let's build a small network with two convolutional layers, followed by one fully connected layer. Convolutional networks are more expensive computationally, so we'll limit their depth and number of fully connected nodes."
       ]
     },
     {