diff --git a/16_nlp_with_rnns_and_attention.ipynb b/16_nlp_with_rnns_and_attention.ipynb
index 240cd52..7e8afa7 100644
--- a/16_nlp_with_rnns_and_attention.ipynb
+++ b/16_nlp_with_rnns_and_attention.ipynb
@@ -367,7 +367,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Warning**: the `predict_classes()` method is deprecated. Instead, we must use `np.argmax(model.predict(X_new), axis=-1)`."
+    "**Warning**: the `predict_classes()` method is deprecated. Instead, we must use `np.argmax(model(X_new), axis=-1)`."
    ]
   },
   {
@@ -378,7 +378,7 @@
    "source": [
     "X_new = preprocess([\"How are yo\"])\n",
     "#Y_pred = model.predict_classes(X_new)\n",
-    "Y_pred = np.argmax(model.predict(X_new), axis=-1)\n",
+    "Y_pred = np.argmax(model(X_new), axis=-1)\n",
     "tokenizer.sequences_to_texts(Y_pred + 1)[0][-1] # 1st sentence, last char"
    ]
   },
@@ -401,7 +401,7 @@
    "source": [
     "def next_char(text, temperature=1):\n",
     "    X_new = preprocess([text])\n",
-    "    y_proba = model.predict(X_new)[0, -1:, :]\n",
+    "    y_proba = model(X_new)[0, -1:, :]\n",
     "    rescaled_logits = tf.math.log(y_proba) / temperature\n",
     "    char_id = tf.random.categorical(rescaled_logits, num_samples=1) + 1\n",
     "    return tokenizer.sequences_to_texts(char_id.numpy())[0]"
@@ -512,6 +512,13 @@
     "dataset = dataset.prefetch(1)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note**: once again, I commented out `recurrent_dropout=0.2` (compared to the book) so you can get GPU acceleration (if you have one)."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 31,
@@ -520,10 +527,12 @@
    "source": [
     "model = keras.models.Sequential([\n",
     "    keras.layers.GRU(128, return_sequences=True, stateful=True,\n",
-    "                     dropout=0.2, recurrent_dropout=0.2,\n",
+    "                     #dropout=0.2, recurrent_dropout=0.2,\n",
+    "                     dropout=0.2,\n",
     "                     batch_input_shape=[batch_size, None, max_id]),\n",
     "    keras.layers.GRU(128, return_sequences=True, stateful=True,\n",
-    "                     dropout=0.2, recurrent_dropout=0.2),\n",
+    "                     #dropout=0.2, recurrent_dropout=0.2),\n",
+    "                     dropout=0.2),\n",
     "    keras.layers.TimeDistributed(keras.layers.Dense(max_id,\n",
     "                                                    activation=\"softmax\"))\n",
     "])"