doc: fix typos in PyTorch CIFAR-10 notebook

sagemaker-python-sdk/pytorch_cnn_cifar10/pytorch_local_mode_cifar10.ipynb

@@ -4,15 +4,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# PyTorch Cifar10 local training  \n",
+    "# PyTorch CIFAR-10 local training  \n",
     "\n",
-    "## Pre-requisites\n",
+    "## Prerequisites\n",
     "\n",
     "This notebook shows how to use the SageMaker Python SDK to run your code in a local container before deploying to SageMaker's managed training or hosting environments.  This can speed up iterative testing and debugging while using the same familiar Python SDK interface.  Just change your estimator's `train_instance_type` to `local` (or `local_gpu` if you're using an ml.p2 or ml.p3 notebook instance).\n",
     "\n",
-    "In order to use this feature you'll need to install docker-compose (and nvidia-docker if training with a GPU).\n",
+    "In order to use this feature, you'll need to install docker-compose (and nvidia-docker if training with a GPU).\n",
     "\n",
-    "**Note, you can only run a single local notebook at one time.**"
+    "**Note: you can only run a single local notebook at one time.**"
    ]
   },
   {
@@ -30,7 +30,7 @@
    "source": [
     "## Overview\n",
     "\n",
-    "The **SageMaker Python SDK** helps you deploy your models for training and hosting in optimized, productions ready containers in SageMaker. The SageMaker Python SDK is easy to use, modular, extensible and compatible with TensorFlow, MXNet, PyTorch and Chainer. This tutorial focuses on how to create a convolutional neural network model to train the [Cifar10 dataset](https://www.cs.toronto.edu/~kriz/cifar.html) using **PyTorch in local mode**.\n",
+    "The **SageMaker Python SDK** helps you deploy your models for training and hosting in optimized, productions ready containers in SageMaker. The SageMaker Python SDK is easy to use, modular, extensible and compatible with TensorFlow, MXNet, PyTorch. This tutorial focuses on how to create a convolutional neural network model to train the [CIFAR-10 dataset](https://www.cs.toronto.edu/~kriz/cifar.html) using **PyTorch in local mode**.\n",
     "\n",
     "### Set up the environment\n",
     "\n",
@@ -80,7 +80,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Download the Cifar10 dataset"
+    "### Download the CIFAR-10 dataset"
    ]
   },
   {
@@ -290,8 +290,17 @@
    "pygments_lexer": "ipython2",
    "version": "2.7.14"
   },
-  "notice": "Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.  Licensed under the Apache License, Version 2.0 (the \"License\"). You may not use this file except in compliance with the License. A copy of the License is located at http://aws.amazon.com/apache2.0/ or in the \"license\" file accompanying this file. This file is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
+  "notice": "Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.  Licensed under the Apache License, Version 2.0 (the \"License\"). You may not use this file except in compliance with the License. A copy of the License is located at http://aws.amazon.com/apache2.0/ or in the \"license\" file accompanying this file. This file is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.",
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "source": [],
+    "metadata": {
+     "collapsed": false
+    }
+   }
+  }
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}