Update index_handling.ipynb

fix typos

docs/notebooks/neuralnet/index_handling.ipynb

@@ -37,7 +37,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Then we define a simple network consists of a max pooling layer and a dense layer:"
+    "Then we define a simple network that consists of a max pooling layer and a dense layer:"
    ]
   },
   {
@@ -106,7 +106,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this example, `input_index_mapper_1` maps outputs of `input_layer` (with size [9])  to the inputs of for `maxpooling_layer` (with size [1, 3, 3]), `input_index_mapper_2` maps the outputs of `maxpooling_layer` (with size [1, 2, 2]) to the inputs of `dense_layer` (with size [4]). Given an input, we can evaluate each layer to see how `IndexMapper` works: "
+    "In this example, `input_index_mapper_1` maps outputs of `input_layer` (with size [9])  to the inputs of `maxpooling_layer` (with size [1, 3, 3]), `input_index_mapper_2` maps the outputs of `maxpooling_layer` (with size [1, 2, 2]) to the inputs of `dense_layer` (with size [4]). Given an input, we can evaluate each layer to see how `IndexMapper` works: "
    ]
   },
   {
@@ -138,7 +138,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Without `IndexMapper`, the outputs of `maxpooling_layer` is identical to the inputs of `dense_layer`. When using `IndexMapper`, using `input_indexes_with_input_layer_indexes` can provide the mapping between indexes. Therefore, there is no need to define variables for the inputs of each layer (except for `input_layer`). As shown in follows, we print both input indexes and output indexes for each layer. Also, we give the mapping between indexes of two adjacent layers, where `local_index` corresponds to the input indexes of current layer and `input_index` corresponds to the output indexes of previous layer."
+    "Without `IndexMapper`, the output of `maxpooling_layer` is identical to the input of `dense_layer`. When using `IndexMapper`, using `input_indexes_with_input_layer_indexes` can provide the mapping between indexes. Therefore, there is no need to define variables for the inputs of each layer (except for `input_layer`). As shown in the following, we print both input indexes and output indexes for each layer. Also, we give the mapping between indexes of two adjacent layers, where `local_index` corresponds to the input indexes of the current layer and `input_index` corresponds to the output indexes of previous layer."
    ]
   },
   {