diff --git a/Notebooks/Chap17/17_1_Latent_Variable_Models.ipynb b/Notebooks/Chap17/17_1_Latent_Variable_Models.ipynb
index b79df19..a8cba0a 100644
--- a/Notebooks/Chap17/17_1_Latent_Variable_Models.ipynb
+++ b/Notebooks/Chap17/17_1_Latent_Variable_Models.ipynb
@@ -55,7 +55,7 @@
         "Pr(z) = \\text{Norm}_{z}[0,1]\n",
         "\\end{equation}\n",
         "\n",
-        "As in figure 17.2, we'll assume that the output is two dimensional, we we need to define a function that maps from the 1D latent variable to two dimensions.  Usually, we would use a neural network, but in this case, we'll just define an arbitrary relationship.\n",
+        "As in figure 17.2, we'll assume that the output is two dimensional, we need to define a function that maps from the 1D latent variable to two dimensions.  Usually, we would use a neural network, but in this case, we'll just define an arbitrary relationship.\n",
         "\n",
         "\\begin{align}\n",
         "x_{1} &=& 0.5\\cdot\\exp\\Bigl[\\sin\\bigl[2+ 3.675 z \\bigr]\\Bigr]\\\\\n",