added CR feedback and import cleanup

CNTK.sln

@@ -987,7 +987,7 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Tutorials", "Tutorials", "{
 		Tutorials\CNTK_101_LogisticRegression.ipynb = Tutorials\CNTK_101_LogisticRegression.ipynb
 		Tutorials\CNTK_102_FeedForward.ipynb = Tutorials\CNTK_102_FeedForward.ipynb
 		Tutorials\CNTK_103A_MNIST_DataLoader.ipynb = Tutorials\CNTK_103A_MNIST_DataLoader.ipynb
-		Tutorials\CNTK_103B_MNIST_FeedForwardNetwork.ipynb = Tutorials\CNTK_103B_MNIST_FeedForwardNetwork.ipynb
+		Tutorials\CNTK_103C_MNIST_MultiLayerPerceptron.ipynb = Tutorials\CNTK_103C_MNIST_MultiLayerPerceptron.ipynb
 		Tutorials\CNTK_201A_CIFAR-10_DataLoader.ipynb = Tutorials\CNTK_201A_CIFAR-10_DataLoader.ipynb
 		Tutorials\CNTK_201B_CIFAR-10_ImageHandsOn.ipynb = Tutorials\CNTK_201B_CIFAR-10_ImageHandsOn.ipynb
 		Tutorials\CNTK_202_Language_Understanding.ipynb = Tutorials\CNTK_202_Language_Understanding.ipynb

Tools/samples.json

@@ -109,9 +109,9 @@
     },
     {
         "category": ["Image"],
-        "name":  "MNIST Feed Multi-Layer Perceptron OCR",
+        "name":  "MNIST Multi-Layer Perceptron (MLP) OCR",
         "url":  "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_103C_MNIST_MultiLayerPerceptron.ipynb",
-        "description":  "Recognize hand written digits (OCR) with MNIST data.",
+        "description":  "Recognize hand written digits (OCR) with MNIST data using MLP.",
         "language":  ["Python"],
         "type":  ["Tutorial", "Recipe"],
 	"dateadded": "4/24/2017"

Tutorials/CNTK_103A_MNIST_DataLoader.ipynb

@@ -8,11 +8,12 @@
    "source": [
     "# CNTK 103 Part A: MNIST Data Loader\n",
     "\n",
-    "This tutorial is targeted to individuals who are new to CNTK and to machine learning. We assume you have completed or are familiar with CNTK 101 and 102. In this tutorial, you will train a feed forward network based simple model to recognize handwritten digits. This is the first example, where we will train and evaluate a neural network based model on read real world data.  \n",
+    "This tutorial is targeted to individuals who are new to CNTK and to machine learning. We assume you have completed or are familiar with CNTK 101 and 102. In this tutorial, we will download and pre-process the MNIST digit images to be used for building different models to recognize handwritten digits. We will extend CNTK 101 and 102 to be applied to this data set. Additionally we will introduce a convolutional network to achieve superior performance. This is the first example, where we will train and evaluate a neural network based model on read real world data.  \n",
     "\n",
-    "CNTK 103 tutorial is divided into two parts:\n",
+    "CNTK 103 tutorial is divided into multiple parts:\n",
     "- Part A: Familiarize with the [MNIST][] database that will be used later in the tutorial\n",
-    "- [Part B](https://github.com/Microsoft/CNTK/blob/v2.0.rc2/Tutorials/CNTK_103B_MNIST_FeedForwardNetwork.ipynb): We will use the feedforward classifier used in CNTK 102 to classify digits in MNIST data set.\n",
+    "- Subsequent parts in this 103 series would be using the MNIST data with different types of networks.\n",
+    "\n",
     "\n",
     "[MNIST]: http://yann.lecun.com/exdb/mnist/\n",
     "\n"
@@ -289,7 +290,7 @@
    "source": [
     "**Suggested Explorations**\n",
     "\n",
-    "One can do data manipulations to improve the performance of a machine learning system. I suggest you first use the data generated so far and run the classifier in CNTK 103 Part B. Once you have a baseline with classifying the data in its original form, now use the different data manipulation techniques to further improve the model.\n",
+    "One can do data manipulations to improve the performance of a machine learning system. I suggest you first use the data generated so far and run the classifier in subsequent parts of the CNTK 103 tutorial series. Once you have a baseline with classifying the data in its original form, now use the different data manipulation techniques to further improve the model.\n",
     "\n",
     "There are several ways data alterations can be performed. CNTK readers automate a lot of these actions for you. However, to get a feel for how these transforms can impact training and test accuracies, I strongly encourage individuals to try one or more of data perturbation.\n",
     "\n",
@@ -314,7 +315,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [default]",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },