diff --git a/Notebooks/Chap11/11_3_Batch_Normalization.ipynb b/Notebooks/Chap11/11_3_Batch_Normalization.ipynb
index 625f8fa..a085ecf 100644
--- a/Notebooks/Chap11/11_3_Batch_Normalization.ipynb
+++ b/Notebooks/Chap11/11_3_Batch_Normalization.ipynb
@@ -4,7 +4,7 @@
   "metadata": {
     "colab": {
       "provenance": [],
-      "authorship_tag": "ABX9TyOoGS+lY+EhGthebSO4smpj",
+      "authorship_tag": "ABX9TyOZaNcBrdZ9yCHhjLOwSi69",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -205,7 +205,8 @@
         "    self.linear3 = nn.Linear(hidden_size, hidden_size)\n",
         "    self.linear4 = nn.Linear(hidden_size, hidden_size)\n",
         "    self.linear5 = nn.Linear(hidden_size, hidden_size)\n",
-        "    self.linear6 = nn.Linear(hidden_size, output_size)\n",
+        "    self.linear6 = nn.Linear(hidden_size, hidden_size)\n",
+        "    self.linear7 = nn.Linear(hidden_size, output_size)\n",
         "\n",
         "  def count_params(self):\n",
         "    return sum([p.view(-1).shape[0] for p in self.parameters()])\n",
@@ -220,11 +221,11 @@
         "    print_variance(\"After second residual connection\",res2)\n",
         "    res3 = res2 + self.linear4(res2.relu())\n",
         "    print_variance(\"After third residual connection\",res3)\n",
-        "    res4 = res3 + self.linear4(res3.relu())\n",
+        "    res4 = res3 + self.linear5(res3.relu())\n",
         "    print_variance(\"After fourth residual connection\",res4)\n",
-        "    res5 = res4 + self.linear4(res4.relu())\n",
+        "    res5 = res4 + self.linear6(res4.relu())\n",
         "    print_variance(\"After fifth residual connection\",res5)\n",
-        "    return self.linear6(res5)"
+        "    return self.linear7(res5)"
       ],
       "metadata": {
         "id": "FslroPJJffrh"
@@ -266,13 +267,14 @@
         "# Use the torch function nn.BatchNorm1d\n",
         "class ResidualNetworkWithBatchNorm(torch.nn.Module):\n",
         "  def __init__(self, input_size, output_size, hidden_size=100):\n",
-        "    super(ResidualNetworkWithBatchNorm, self).__init__()\n",
+        "    super(ResidualNetwork, self).__init__()\n",
         "    self.linear1 = nn.Linear(input_size, hidden_size)\n",
         "    self.linear2 = nn.Linear(hidden_size, hidden_size)\n",
         "    self.linear3 = nn.Linear(hidden_size, hidden_size)\n",
         "    self.linear4 = nn.Linear(hidden_size, hidden_size)\n",
         "    self.linear5 = nn.Linear(hidden_size, hidden_size)\n",
-        "    self.linear6 = nn.Linear(hidden_size, output_size)\n",
+        "    self.linear6 = nn.Linear(hidden_size, hidden_size)\n",
+        "    self.linear7 = nn.Linear(hidden_size, output_size)\n",
         "\n",
         "  def count_params(self):\n",
         "    return sum([p.view(-1).shape[0] for p in self.parameters()])\n",
@@ -287,11 +289,11 @@
         "    print_variance(\"After second residual connection\",res2)\n",
         "    res3 = res2 + self.linear4(res2.relu())\n",
         "    print_variance(\"After third residual connection\",res3)\n",
-        "    res4 = res3 + self.linear4(res3.relu())\n",
+        "    res4 = res3 + self.linear5(res3.relu())\n",
         "    print_variance(\"After fourth residual connection\",res4)\n",
-        "    res5 = res4 + self.linear4(res4.relu())\n",
+        "    res5 = res4 + self.linear6(res4.relu())\n",
         "    print_variance(\"After fifth residual connection\",res5)\n",
-        "    return self.linear6(res5)"
+        "    return self.linear7(res5)"
       ],
       "metadata": {
         "id": "5JvMmaRITKGd"