diff --git a/notebooks/08_Introduction_to_TensorFlow.ipynb b/notebooks/08_Introduction_to_TensorFlow.ipynb
index 75107fd..efd311f 100644
--- a/notebooks/08_Introduction_to_TensorFlow.ipynb
+++ b/notebooks/08_Introduction_to_TensorFlow.ipynb
@@ -100,7 +100,7 @@
     "  * `shape` (optional) : An iterable defining the shape (size of each dimension) of the tensor e.g. `shape=(5, 2)` would indicate a 2D tensor ($\\sim$ matrix) with first dimension of size 5 and second of size 2. An entry of `None` in the shape definition corresponds to the corresponding dimension size being left unspecified, so for example `shape=(None, 28, 28)` would allow any 3D inputs with final two dimensions of size 28 to be inputted.\n",
     "  * `name` (optional): String argument defining a name for the tensor which can be useful when visualising a computation graph and for debugging purposes.\n",
     "  \n",
-    "As we will generally be working with batches of datapoints, both the `inputs` and `targets` will be 2D tensors with first dimension corresponding to the batch size (set as `None` here to allow it tand second dimension the size of each input or output vector. As in the previous semester's work we will use a 1-of-$k$ encoding for the class targets so each output corresponds to a vector of length 10 (number of digit classes)."
+    "As we will generally be working with batches of datapoints, both the `inputs` and `targets` will be 2D tensors with first dimension corresponding to the batch size (set as `None` here to allow it to specified later) and second dimension the size of each input or output vector. As in the previous semester's work we will use a 1-of-$k$ encoding for the class targets so each output corresponds to a vector of length 10 (number of digit classes)."
    ]
   },
   {