diff --git a/13_loading_and_preprocessing_data.ipynb b/13_loading_and_preprocessing_data.ipynb
index f5926a4..56b8aae 100644
--- a/13_loading_and_preprocessing_data.ipynb
+++ b/13_loading_and_preprocessing_data.ipynb
@@ -841,6 +841,13 @@
     "y_pred = model.predict(new_set)  # or you could just pass a NumPy array"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Warning**: the `mean_squared_error()` function no longer exists. You must now use an instance of the `MeanSquaredError` class instead."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 30,
@@ -857,7 +864,7 @@
    "source": [
     "# extra code – defines the optimizer and loss function for training\n",
     "optimizer = tf.keras.optimizers.SGD(learning_rate=0.01)\n",
-    "loss_fn = tf.keras.losses.mean_squared_error\n",
+    "loss_fn = tf.keras.losses.MeanSquaredError()\n",
     "\n",
     "n_epochs = 5\n",
     "for epoch in range(n_epochs):\n",
@@ -898,7 +905,7 @@
     "        optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n",
     "\n",
     "optimizer = tf.keras.optimizers.SGD(learning_rate=0.01)\n",
-    "loss_fn = tf.keras.losses.mean_squared_error\n",
+    "loss_fn = tf.keras.losses.MeanSquaredError()\n",
     "for epoch in range(n_epochs):\n",
     "    print(\"\\rEpoch {}/{}\".format(epoch + 1, n_epochs), end=\"\")\n",
     "    train_one_epoch(model, optimizer, loss_fn, train_set)"
@@ -2719,10 +2726,10 @@
     "tf.random.set_seed(42)\n",
     "np.random.seed(42)\n",
     "X_train_num = np.random.rand(10_000, 8)\n",
-    "X_train_cat = np.random.choice(ocean_prox, size=10_000)\n",
+    "X_train_cat = np.random.choice(ocean_prox, size=10_000).astype(object)\n",
     "y_train = np.random.rand(10_000, 1)\n",
     "X_valid_num = np.random.rand(2_000, 8)\n",
-    "X_valid_cat = np.random.choice(ocean_prox, size=2_000)\n",
+    "X_valid_cat = np.random.choice(ocean_prox, size=2_000).astype(object)\n",
     "y_valid = np.random.rand(2_000, 1)\n",
     "\n",
     "num_input = tf.keras.layers.Input(shape=[8], name=\"num\")\n",
@@ -2768,37 +2775,6 @@
     "                    validation_data=valid_set)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 100,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/5\n",
-      "313/313 [==============================] - 1s 1ms/step - loss: 0.0829 - val_loss: 0.0830\n",
-      "Epoch 2/5\n",
-      "313/313 [==============================] - 0s 1ms/step - loss: 0.0829 - val_loss: 0.0830\n",
-      "Epoch 3/5\n",
-      "313/313 [==============================] - 0s 1ms/step - loss: 0.0828 - val_loss: 0.0830\n",
-      "Epoch 4/5\n",
-      "313/313 [==============================] - 0s 1ms/step - loss: 0.0828 - val_loss: 0.0829\n",
-      "Epoch 5/5\n",
-      "313/313 [==============================] - 0s 1ms/step - loss: 0.0828 - val_loss: 0.0829\n"
-     ]
-    }
-   ],
-   "source": [
-    "# extra code – shows that the dataset can contain dictionaries\n",
-    "train_set = tf.data.Dataset.from_tensor_slices(\n",
-    "    ({\"num\": X_train_num, \"cat\": X_train_cat}, y_train)).batch(32)\n",
-    "valid_set = tf.data.Dataset.from_tensor_slices(\n",
-    "    ({\"num\": X_valid_num, \"cat\": X_valid_cat}, y_valid)).batch(32)\n",
-    "history = model.fit(train_set, epochs=5, validation_data=valid_set)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -2808,7 +2784,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 100,
    "metadata": {},
    "outputs": [
     {
@@ -2819,7 +2795,7 @@
        "       [6, 2, 1, 2]])>"
       ]
      },
-     "execution_count": 101,
+     "execution_count": 100,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2833,7 +2809,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 101,
    "metadata": {},
    "outputs": [
     {
@@ -2842,7 +2818,7 @@
        "<tf.RaggedTensor [[2, 1], [6, 2, 1, 2]]>"
       ]
      },
-     "execution_count": 102,
+     "execution_count": 101,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2855,7 +2831,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 102,
    "metadata": {},
    "outputs": [
     {
@@ -2868,7 +2844,7 @@
        "        1.0986123 ]], dtype=float32)>"
       ]
      },
-     "execution_count": 103,
+     "execution_count": 102,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2881,7 +2857,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 103,
    "metadata": {},
    "outputs": [
     {
@@ -2890,7 +2866,7 @@
        "1.3862943611198906"
       ]
      },
-     "execution_count": 104,
+     "execution_count": 103,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2901,7 +2877,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 104,
    "metadata": {},
    "outputs": [
     {
@@ -2910,7 +2886,7 @@
        "1.0986122886681098"
       ]
      },
-     "execution_count": 105,
+     "execution_count": 104,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2930,7 +2906,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 105,
    "metadata": {},
    "outputs": [
     {
@@ -2950,7 +2926,7 @@
        "        -0.1 , -0.18, -0.13, -0.04,  0.15]], dtype=float32)"
       ]
      },
-     "execution_count": 106,
+     "execution_count": 105,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2972,7 +2948,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 106,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2985,7 +2961,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 107,
    "metadata": {},
    "outputs": [
     {
@@ -3009,7 +2985,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 108,
    "metadata": {},
    "outputs": [
     {
@@ -3040,7 +3016,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 109,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3052,7 +3028,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 110,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3064,7 +3040,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 111,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3075,7 +3051,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 112,
    "metadata": {},
    "outputs": [
     {
@@ -3151,7 +3127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 113,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3162,7 +3138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 114,
    "metadata": {},
    "outputs": [
     {
@@ -3184,7 +3160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 115,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3201,7 +3177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 116,
    "metadata": {},
    "outputs": [
     {
@@ -3244,7 +3220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3265,7 +3241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3284,7 +3260,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3314,7 +3290,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3325,7 +3301,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
@@ -3352,7 +3328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3377,7 +3353,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
@@ -3441,7 +3417,7 @@
        "<keras.callbacks.History at 0x7fa3e08af370>"
       ]
      },
-     "execution_count": 124,
+     "execution_count": 123,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3460,7 +3436,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
@@ -3516,7 +3492,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
@@ -3534,7 +3510,7 @@
        "PosixPath('datasets/aclImdb')"
       ]
      },
-     "execution_count": 126,
+     "execution_count": 125,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3559,7 +3535,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3582,7 +3558,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
@@ -3636,7 +3612,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
@@ -3645,7 +3621,7 @@
        "(12500, 12500, 12500, 12500)"
       ]
      },
-     "execution_count": 129,
+     "execution_count": 128,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3672,7 +3648,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3701,7 +3677,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3719,7 +3695,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
@@ -3747,7 +3723,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -3778,7 +3754,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 134,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3794,7 +3770,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
@@ -3818,7 +3794,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
@@ -3835,7 +3811,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3864,7 +3840,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 138,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3884,7 +3860,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 139,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
@@ -3893,7 +3869,7 @@
        "['[UNK]', 'the', 'and', 'a', 'of', 'to', 'is', 'in', 'it', 'i']"
       ]
      },
-     "execution_count": 139,
+     "execution_count": 138,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3918,7 +3894,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 140,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
@@ -3943,7 +3919,7 @@
        "<keras.callbacks.History at 0x7fa401b8f9d0>"
       ]
      },
-     "execution_count": 140,
+     "execution_count": 139,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3984,7 +3960,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 141,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
@@ -3995,7 +3971,7 @@
        "       [6.       , 0.       , 0.       ]], dtype=float32)>"
       ]
      },
-     "execution_count": 141,
+     "execution_count": 140,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4021,7 +3997,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
@@ -4030,7 +4006,7 @@
        "<tf.Tensor: shape=(1, 3), dtype=float32, numpy=array([[3.535534 , 4.9497476, 2.1213202]], dtype=float32)>"
       ]
      },
-     "execution_count": 142,
+     "execution_count": 141,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4048,7 +4024,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
@@ -4057,7 +4033,7 @@
        "<tf.Tensor: shape=(1, 3), dtype=float32, numpy=array([[6., 0., 0.]], dtype=float32)>"
       ]
      },
-     "execution_count": 143,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4075,7 +4051,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 144,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4107,7 +4083,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 145,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
@@ -4132,7 +4108,7 @@
        "<keras.callbacks.History at 0x7fa3a0bf9460>"
       ]
      },
-     "execution_count": 145,
+     "execution_count": 144,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4160,7 +4136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": 145,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4172,7 +4148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": 146,
    "metadata": {},
    "outputs": [
     {
@@ -4214,7 +4190,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.9.10"
   },
   "nav_menu": {
    "height": "264px",