Tools/samples.json, bindings/python/doc/tutorials.rst: updated

Author: mahilleb-msft

Tools/samples.json

@@ -130,7 +130,7 @@
     {
         "category": ["Image"],
         "name":  "Generative Adversarial Networks (GAN)",
-        "url":  "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_205_Basic_GAN.ipynb",
+        "url":  "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_206_Basic_GAN.ipynb",
         "description":  "This tutorial is a basic implementation of GAN networks. This allows us generate realistic looking MNIST images.",
         "language":  ["Python"],
         "type":  ["Tutorial", "Recipe"]
@@ -239,6 +239,14 @@
         "language":  ["Python"],
         "type":  ["Tutorial", "Recipe"]
     },
+    {
+        "category": ["Numeric"],
+        "name":  "Training with Sampled Softmax",
+        "url":  "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_207_Training_with_Sampled_Softmax.ipynb",
+        "description":  "Training with Sampled Softmax",
+        "language":  ["Python"],
+        "type":  ["Tutorial", "Recipe"]
+    },
     {
         "category": ["Speech"],
         "name":  "AN4 Speech DNN",

bindings/python/doc/tutorials.rst

@@ -1,56 +1,61 @@
 Tutorials
 ===============
 
-#.  *Classify cancer using simulated data (Logistic Regression)*  
-     CNTK 101: `Logistic Regression`_ with NumPy  
-                    
+#.  *Classify cancer using simulated data (Logistic Regression)*
+     CNTK 101: `Logistic Regression`_ with NumPy
+
 #.  *Classify cancer using simulated data (Feed Forward)*
      CNTK 102: `Feed Forward network`_ with NumPy
-                              
+
 #.  *Recognize hand written digits (OCR) with MNIST data*
-     CNTK 103 Part A: `Data preparation <https://github.com/Microsoft/CNTK/tree/v2.0.beta10.0/Tutorials/CNTK_103A_MNIST_DataLoader.ipynb>`_ ,  Part B: `Feed Forward classifier`_
-    
+     CNTK 103 Part A: `Data preparation <https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_103A_MNIST_DataLoader.ipynb>`_ ,  Part B: `Feed Forward classifier`_
+
 #.  *Learn how to predict the stock market*
      CNTK 104: `Time Series basics`_ with finance data
-                          
-#.  *Compress (using autoencoder) hand written digits from MNIST data with no human input (unsupervised learning)* 
-     CNTK 105 Part A: `Data preparation <https://github.com/Microsoft/CNTK/tree/v2.0.beta10.0/Tutorials/CNTK_103A_MNIST_DataLoader.ipynb>`_ ,  Part B: `Feed Forward autoencoder`_
 
-#.  *Forecasting using data from an IOT device* 
+#.  *Compress (using autoencoder) hand written digits from MNIST data with no human input (unsupervised learning)*
+     CNTK 105 Part A: `Data preparation <https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_103A_MNIST_DataLoader.ipynb>`_ ,  Part B: `Feed Forward autoencoder`_
+
+#.  *Forecasting using data from an IOT device*
      CNTK 106: LSTM based forecasting - Part A: `with simulated data <https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_106A_LSTM_Timeseries_with_Simulated_Data.ipynb>`_
 
 #.  *Recognize objects in images from CIFAR-10 data*
-     CNTK 201 Part A: `Data preparation <https://github.com/Microsoft/CNTK/tree/v2.0.beta10.0/Tutorials/CNTK_201A_CIFAR-10_DataLoader.ipynb>`_,  Part B: `VGG and ResNet classifiers`_
- 
+     CNTK 201 Part A: `Data preparation <https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_201A_CIFAR-10_DataLoader.ipynb>`_,  Part B: `VGG and ResNet classifiers`_
+
 #.  *Infer meaning from text snippets using LSTMs and word embeddings*
      CNTK 202: `Language understanding`_ with ATIS3 text data
-              
+
 #.  *Train a computer to perform tasks optimally (e.g., win games) in a simulated environment*
      CNTK 203: `Reinforcement learning basics`_ with OpenAI Gym data
 
 #.  *Translate text from one domain (grapheme) to other (phoneme)*
      CNTK 204: `Sequence to sequence basics`_ with CMU pronouncing dictionary
-              
+
 #.  *Teach a computer to paint like Piccasso or van Gogh*
      CNTK 205: `Artistic Style Transfer`_
-              
+
 #.  *Produce realistic data (MNIST images) with no human input (unsupervised learning)*
-     CNTK 206 Part A: `Data preparation <https://github.com/Microsoft/CNTK/tree/v2.0.beta10.0/Tutorials/CNTK_103A_MNIST_DataLoader.ipynb>`_ ,  Part B: `Basic Generative Adversarial Networks (GAN)`_
+     CNTK 206 Part A: `Data preparation <https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_103A_MNIST_DataLoader.ipynb>`_ ,  Part B: `Basic Generative Adversarial Networks (GAN)`_
+
+#.   *Training with Sampled Softmax*
+     CNTK 207: `Training with Sampled Softmax`_
 
 For our Japanese users, you can find some of the `tutorials in Japanese`_.
 
-.. _`Logistic Regression`: https://github.com/Microsoft/CNTK/tree/v2.0.beta10.0/Tutorials/CNTK_101_LogisticRegression.ipynb
-.. _`Feed Forward network`: https://github.com/Microsoft/CNTK/tree/v2.0.beta10.0/Tutorials/CNTK_102_FeedForward.ipynb
-.. _`Data preparation`: https://github.com/Microsoft/CNTK/tree/v2.0.beta10.0/Tutorials/CNTK_103A_MNIST_DataLoader.ipynb
-.. _`Feed Forward classifier`: https://github.com/Microsoft/CNTK/tree/v2.0.beta10.0/Tutorials/CNTK_103B_MNIST_FeedForwardNetwork.ipynb
-.. _`Time Series basics`: https://github.com/Microsoft/CNTK/tree/v2.0.beta10.0/Tutorials/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.ipynb
-.. _`Feed Forward autoencoder`: https://github.com/Microsoft/CNTK/tree/v2.0.beta10.0/Tutorials/CNTK_105_Basic_Autoencoder_for_Dimensionality_Reduction.ipynb
+.. _`Logistic Regression`: https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_101_LogisticRegression.ipynb
+.. _`Feed Forward network`: https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_102_FeedForward.ipynb
+.. _`Data preparation`: https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_103A_MNIST_DataLoader.ipynb
+.. _`Feed Forward classifier`: https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_103B_MNIST_FeedForwardNetwork.ipynb
+.. _`Time Series basics`: https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.ipynb
+.. _`Feed Forward autoencoder`: https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_105_Basic_Autoencoder_for_Dimensionality_Reduction.ipynb
 .. _`Basic LSTM based time series`: https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_106A_LSTM_Timeseries_with_Simulated_Data.ipynb
-.. _`data preparation`: https://github.com/Microsoft/CNTK/tree/v2.0.beta10.0/Tutorials/CNTK_201A_CIFAR-10_DataLoader.ipynb
-.. _`VGG and ResNet classifiers`: https://github.com/Microsoft/CNTK/tree/v2.0.beta10.0/Tutorials/CNTK_201B_CIFAR-10_ImageHandsOn.ipynb
+.. _`data preparation`: https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_201A_CIFAR-10_DataLoader.ipynb
+.. _`VGG and ResNet classifiers`: https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_201B_CIFAR-10_ImageHandsOn.ipynb
 .. _`Language understanding`: https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_202_Language_Understanding.ipynb
 .. _`Reinforcement learning basics`: https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_203_Reinforcement_Learning_Basics.ipynb
 .. _`Sequence to sequence basics`: https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_204_Sequence_To_Sequence.ipynb
 .. _`Artistic Style Transfer`: https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_205_Artistic_Style_Transfer.ipynb
 .. _`Basic Generative Adversarial Networks (GAN)`: https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_206_Basic_GAN.ipynb
+.. _`Training with Sampled Softmax`: https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/Tutorials/CNTK_207_Training_with_Sampled_Softmax.ipynb
+
 .. _`tutorials in Japanese`: https://notebooks.azure.com/library/cntkbeta2_ja