Minor edits to onnx mxnet export notebook (aws#458)

Author: eslesar-aws

sagemaker-python-sdk/mxnet_onnx_export/mxnet_onnx_export.ipynb

@@ -6,9 +6,9 @@
    "source": [
     "## Exporting ONNX Models with MXNet\n",
     "\n",
-    "The [Open Neural Network Exchange](https://onnx.ai/) (ONNX) is an open format for representing deep learning models with its extensible computation graph model and definitions of built-in operators and standard data types. Starting with MXNet 1.3, models trained using MXNet can now be saved as ONNX models.\n",
+    "The [Open Neural Network Exchange](https://onnx.ai/) (ONNX) is an open format for representing deep learning models with an extensible computation graph model, definitions of built-in operators, and standard data types. Starting with MXNet 1.3, models trained using MXNet can now be saved as ONNX models.\n",
     "\n",
-    "In this example, we will show how to train a model on Amazon SageMaker and save it as an ONNX model. This notebooks is based on the [MXNet MNIST notebook](https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-python-sdk/mxnet_mnist/mxnet_mnist.ipynb) and the [MXNet example for exporting to ONNX](https://mxnet.incubator.apache.org/tutorials/onnx/export_mxnet_to_onnx.html)."
+    "In this example, we show how to train a model on Amazon SageMaker and save it as an ONNX model. This notebook is based on the [MXNet MNIST notebook](https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-python-sdk/mxnet_mnist/mxnet_mnist.ipynb) and the [MXNet example for exporting to ONNX](https://mxnet.incubator.apache.org/tutorials/onnx/export_mxnet_to_onnx.html)."
    ]
   },
   {
@@ -17,7 +17,7 @@
    "source": [
     "### Setup\n",
     "\n",
-    "First we need to define a few variables that will be needed later in the example."
+    "First we need to define a few variables that we'll need later in the example."
    ]
   },
   {
@@ -94,7 +94,7 @@
     "    onnx_mxnet.export_model(symbol_file, params_file, data_shapes, np.float32, output_path)\n",
     "```\n",
     "\n",
-    "The last line in that method, `onnx_mxnet.export_model`, is what saves the model in the ONNX format. You can see that we pass the following arguments:\n",
+    "The last line in that method, `onnx_mxnet.export_model`, saves the model in the ONNX format. We pass the following arguments:\n",
     "\n",
     "* `symbol_file`: path to the saved input symbol file\n",
     "* `params_file`: path to the saved input params file\n",