diff --git a/11_training_deep_neural_networks.ipynb b/11_training_deep_neural_networks.ipynb
index e9fdc9f..4fb717f 100644
--- a/11_training_deep_neural_networks.ipynb
+++ b/11_training_deep_neural_networks.ipynb
@@ -970,6 +970,13 @@
     "model_B_on_A.add(keras.layers.Dense(1, activation=\"sigmoid\"))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that `model_B_on_A` and `model_A` actually share layers now, so when we train one, it will update both models. If we want to avoid that, we need to build `model_B_on_A` on top of a *clone* of `model_A`:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 61,
@@ -977,7 +984,9 @@
    "outputs": [],
    "source": [
     "model_A_clone = keras.models.clone_model(model_A)\n",
-    "model_A_clone.set_weights(model_A.get_weights())"
+    "model_A_clone.set_weights(model_A.get_weights())\n",
+    "model_B_on_A = keras.models.Sequential(model_A_clone.layers[:-1])\n",
+    "model_B_on_A.add(keras.layers.Dense(1, activation=\"sigmoid\"))"
    ]
   },
   {
@@ -1042,7 +1051,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Great! We got quite a bit of transfer: the error rate dropped by a factor of 4.5!"
+    "Great! We got quite a bit of transfer: the error rate dropped by a factor of 4.9!"
    ]
   },
   {
@@ -1051,7 +1060,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "(100 - 97.05) / (100 - 99.35)"
+    "(100 - 97.05) / (100 - 99.40)"
    ]
   },
   {