diff --git a/samples/core/tutorials/keras/save_and_restore_models.ipynb b/samples/core/tutorials/keras/save_and_restore_models.ipynb
index 8d6e6209a4e..310612950d8 100644
--- a/samples/core/tutorials/keras/save_and_restore_models.ipynb
+++ b/samples/core/tutorials/keras/save_and_restore_models.ipynb
@@ -582,8 +582,9 @@
       "cell_type": "markdown",
       "source": [
         "The above code stores the weights to a collection of [checkpoint](https://www.tensorflow.org/guide/saved_model#save_and_restore_variables)-formatted files that contain only the trained weights in a binary format. Checkpoints contain:\n",
+        "\n",
         "* One or more shards that contain your model's weights. \n",
-        "* An index file that indicates which weights are stored in a which shard.  \n",
+        "* An index file that indicates which weights are stored in which shard.  \n",
         "\n",
         "If you are only training a model on a single machine, you'll have one shard with the suffix: `.data-00000-of-00001`"
       ]
@@ -739,7 +740,7 @@
         "* The model's configuration(architecture)\n",
         "* The optimizer configuration\n",
         "\n",
-        "Keras saves models by inspecting the architecture. Currently, it is not able to save TensorFlow optimizers (from `tf.train`). When using those you will need to re-compile the model after loading, and you will loose the state of the optimizer.\n"
+        "Keras saves models by inspecting the architecture. Currently, it is not able to save TensorFlow optimizers (from `tf.train`). When using those you will need to re-compile the model after loading, and you will lose the state of the optimizer.\n"
       ]
     },
     {
@@ -761,4 +762,4 @@
       ]
     }
   ]
-}
\ No newline at end of file
+}