diff --git a/notebooks/Block_2/Solutions to Exercises - Block 2.ipynb b/notebooks/Block_2/Solutions to Exercises - Block 2.ipynb
index 61737e98212d78113c6fe00077a801329586852e..b74e3e8e9a140560dbd000c441e5c4bf7c73f606 100644
--- a/notebooks/Block_2/Solutions to Exercises - Block 2.ipynb	
+++ b/notebooks/Block_2/Solutions to Exercises - Block 2.ipynb	
@@ -45,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 21,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -69,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 22,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -159,7 +159,7 @@
     "\n",
     "*   `input_shape=[1]` — This specifies that the input to this layer is a single value. That is, the shape is a one-dimensional array with one member. Since this is the first (and only) layer, that input shape is the input shape of the entire model. The single value is a floating point number, representing degrees Celsius.\n",
     "\n",
-    "*   `units=1` — This specifies the number of neurons in the layer. The number of neurons defines how many internal variables the layer has to try to learn how to solve the problem (more later). Since this is the final layer, it is also the size of the model's output — a single float value representing degrees Fahrenheit. (In a multi-layered network, the size and shape of the layer would need to match the `input_shape` of the next layer.)\n"
+    "*   `units=1` — This specifies the number of units in the layer. The number of units defines how many internal variables the layer has to try to learn how to solve the problem (more later). Since this is the final layer, it is also the size of the model's output — a single float value representing degrees Fahrenheit. (In a multi-layered network, the size and shape of the layer would need to match the `input_shape` of the next layer.)\n"
    ]
   },
   {