diff --git a/site/en/guide/basic_training_loops.ipynb b/site/en/guide/basic_training_loops.ipynb
index a1558b1903e..b6c6fa80908 100644
--- a/site/en/guide/basic_training_loops.ipynb
+++ b/site/en/guide/basic_training_loops.ipynb
@@ -266,7 +266,7 @@
         "id": "-50nq-wPBsAW"
       },
       "source": [
-        "Before training the model, you can visualize the loss value by plotting the model's predictions in red and the training data in blue:"
+        "Before training the model, you can visualize the loss value by plotting the model's predictions and the training data:"
       ]
     },
     {
@@ -322,11 +322,11 @@
         "    current_loss = loss(y, model(x))\n",
         "\n",
         "  # Use GradientTape to calculate the gradients with respect to W and b\n",
-        "  dw, db = t.gradient(current_loss, [model.w, model.b])\n",
+        "  grad_w, grad_b = t.gradient(current_loss, [model.w, model.b])\n",
         "\n",
         "  # Subtract the gradient scaled by the learning rate\n",
-        "  model.w.assign_sub(learning_rate * dw)\n",
-        "  model.b.assign_sub(learning_rate * db)"
+        "  model.w.assign_sub(learning_rate * grad_w)\n",
+        "  model.b.assign_sub(learning_rate * grad_b)"
       ]
     },
     {
@@ -491,7 +491,7 @@
         "training_loop(keras_model, x, y)\n",
         "\n",
         "# You can also save a checkpoint using Keras's built-in support\n",
-        "keras_model.save_weights(\"my_checkpoint\")"
+        "keras_model.save_weights(\"my_checkpoint.weights.h5\")"
       ]
     },
     {
@@ -595,4 +595,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
+}
\ No newline at end of file