added suggestions

Author: SurajBaloni

guide/14-deep-learning/how_AutoDL_works.ipynb

@@ -33,7 +33,7 @@
     "    - [MIOU](#miou)\n",
     "    - [Average Precision Score](#aps)\n",
     "    \n",
-    "- [What is ImageryModel](#ImageryModel)\n",
+    "- [Fine tune AutoDL models using ImageryModel](#ImageryModel)\n",
     "    - [Load the model](#imLoad)\n",
     "    - [Learning rate](#imLR)\n",
     "    - [Train the model](#imFit)\n",
@@ -53,7 +53,7 @@
    "metadata": {},
    "source": [
     "This guide explains the steps for training and evaluation of multiple network architectures supported by `arcgis.learn` API.\n",
-    "The `arcgis.learn` API currently supports more than 30 deep learning networks to solve different problems, more specifically for object detetcion task only, API provides 4 deep learning networks along with MMDetection class that in turn supports more than twenty object detection networks. Similarly for pixel classification task API provides support for 11 deep learning networks in which MMSegmentation class supports more than twenty pixel classification networks.\n",
+    "The `arcgis.learn` API currently supports more than 30 deep learning networks to solve different problems, more specifically for object detetcion task only, API provides 4 deep learning networks along with MMDetection class that in turn supports more than twenty object detection networks. Similarly for pixel classification task, API provides support for 11 deep learning networks in which MMSegmentation class supports more than twenty pixel classification networks.\n",
     "\n",
     "To train a deep learning network using the `arcgis.learn` API, one must follow the complete pipeline that involves data preprocessing, network selection, hyper parameter tuning and network selection/evaluation based on the performance of the network. This can sometime become a difficult task for a user to iteratively run all the networks to compare the performance and selecting which network works best with the given data. \n",
     "\n",
@@ -204,11 +204,11 @@
    "metadata": {},
    "source": [
     "- **Basic**\n",
-    "    - In this mode we iterate through all the supported networks exactly once with the default backbone, train it with the passed data and calculate the network performance. At the end of each iteration the function will save the model to the disk. Maximum number of epochs to train each network is 20, however if the remaining time left to process the network is less than than the expected time, the program will automatically calculate the maximum number of epochs to train the network.\n",
+    "    - In this mode we iterate through all the supported networks exactly once with the default backbone, train it with the passed data and calculate the network performance. At the end of each iteration the function will save the model to the disk. Maximum number of epochs to train each network is 20, however if the remaining time left to process the network is less than than the expected time(minimum time required to train the network), the program will automatically calculate the maximum number of epochs to train the network.\n",
     "    \n",
     "    \n",
     "- **Advanced**\n",
-    "    - To be used when the user wants to tune hyper parameters of two best performing networks from basic mode.This mode will divide the total time into two halfs. In the first half it works as basic mode where it will iterate through all the supported networks exactly once. In the second half it checks for two best performing networks. The program then trains the selected networks with different supported backbones. At the end of each iteration the function will save the model to the disk. Maximum number of epochs to train each network is 20, however if the remaining time left to process the network is less than the expected time(minimum time required to train the network) , the program will automatically calculate the number of epochs to train the network.\n",
+    "    - To be used when the user wants to tune hyper parameters of two best performing networks from basic mode.This mode will divide the total time into two halfs. In the first half it works as basic mode where it will iterate through all the supported networks exactly once. In the second half it checks for two best performing networks. The program then trains the selected networks with different supported backbones. At the end of each iteration the function will save the model to the disk. Maximum number of epochs to train each network is 20, however if the remaining time left to process the network is less than the expected time(minimum time required to train the network), the program will automatically calculate the number of epochs to train the network.\n",
     " "
    ]
   },
@@ -416,15 +416,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Fine tuneAutoDL using ImageryModel\n",
+    "# Fine tune AutoDL models using ImageryModel\n",
     "<a id='ImageryModel'></a>"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Once the best performing network is identified it can be further fine tuned using the `ImageryModel` class, this class supports methods that can be used to load, fine-tune and save the model for further use."
+    "Once the best performing network is identified it can be further fine tuned using the `ImageryModel` class. This class supports methods that can be used to load, fine-tune and save the model for further use."
    ]
   },
   {