diff --git a/site/en/tutorials/keras/save_and_load.ipynb b/site/en/tutorials/keras/save_and_load.ipynb
index 140ea1b59ac..10d203d9be4 100644
--- a/site/en/tutorials/keras/save_and_load.ipynb
+++ b/site/en/tutorials/keras/save_and_load.ipynb
@@ -109,7 +109,7 @@
         "\n",
         "Caution: TensorFlow models are code and it is important to be careful with untrusted code. See [Using TensorFlow Securely](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for details.\n",
         "\n",
-        "### Options\n",
+        "## Options\n",
         "\n",
         "There are different ways to save TensorFlow models depending on the API you're using. This guide uses [tf.keras](https://www.tensorflow.org/guide/keras)—a high-level API to build and train models in TensorFlow. The new, high-level `.keras` format used in this tutorial is recommended for saving Keras objects, as it provides robust, efficient name-based saving that is often easier to debug than low-level or legacy formats. For more advanced saving or serialization workflows, especially those involving custom objects, please refer to the [Save and load Keras models guide](https://www.tensorflow.org/guide/keras/save_and_serialize). For other approaches, refer to the [Using the SavedModel format guide](../../guide/saved_model.ipynb)."
       ]
@@ -169,7 +169,7 @@
       "source": [
         "### Get an example dataset\n",
         "\n",
-        "To demonstrate how to save and load weights, you'll use the [MNIST dataset](http://yann.lecun.com/exdb/mnist/). To speed up these runs, use the first 1000 examples:"
+        "To demonstrate how to save and load weights, you'll use the [MNIST dataset](https://www.tensorflow.org/datasets/catalog/mnist). To speed up these runs, use the first 1000 examples:"
       ]
     },
     {
@@ -218,9 +218,9 @@
         "# Define a simple sequential model\n",
         "def create_model():\n",
         "  model = tf.keras.Sequential([\n",
-        "    keras.layers.Dense(512, activation='relu', input_shape=(784,)),\n",
-        "    keras.layers.Dropout(0.2),\n",
-        "    keras.layers.Dense(10)\n",
+        "    tf.keras.layers.Dense(512, activation='relu', input_shape=(784,)),\n",
+        "    tf.keras.layers.Dropout(0.2),\n",
+        "    tf.keras.layers.Dense(10)\n",
         "  ])\n",
         "\n",
         "  model.compile(optimizer='adam',\n",
@@ -559,9 +559,9 @@
         "- Passing `save_format='h5'` to `save()`\n",
         "- Passing a filename that ends in `.h5`\n",
         "\n",
-        "Saving a fully-functional model is very useful—you can load them in TensorFlow.js ([Saved Model](https://www.tensorflow.org/js/tutorials/conversion/import_saved_model), [HDF5](https://www.tensorflow.org/js/tutorials/conversion/import_keras)) and then train and run them in web browsers, or convert them to run on mobile devices using TensorFlow Lite ([Saved Model](https://www.tensorflow.org/lite/models/convert/#convert_a_savedmodel_recommended_), [HDF5](https://www.tensorflow.org/lite/models/convert/#convert_a_keras_model_))\n",
+        "Saving a fully-functional model is very useful—you can load them in TensorFlow.js ([Saved Model](https://www.tensorflow.org/js/guide/conversion), [HDF5](https://www.tensorflow.org/js/guide/conversion)) and then train and run them in web browsers, or convert them to run on mobile devices using TensorFlow Lite ([Saved Model](https://www.tensorflow.org/lite/models/convert/), [HDF5](https://www.tensorflow.org/lite/models/convert/))\n",
         "\n",
-        "\\*Custom objects (for example, subclassed models or layers) require special attention when saving and loading. Refer to the **Saving custom objects** section below."
+        "**Note:** Custom objects (for example, subclassed models or layers) require special attention when saving and loading. Refer to the **Saving custom objects** section below."
       ]
     },
     {
@@ -813,8 +813,8 @@
         "* The model's architecture\n",
         "* The model's training configuration (what you pass to the `.compile()` method)\n",
         "* The optimizer and its state, if any (this enables you to restart training where you left off)\n",
-        "\n",
-        "Keras is not able to save the `v1.x` optimizers (from `tf.compat.v1.train`) since they aren't compatible with checkpoints. For v1.x optimizers, you need to re-compile the model after loading—losing the state of the optimizer.\n"
+        "\n"
+
       ]
     },
     {