Updated docs, test and nb spell mistakes

Tests/EndToEndTests/CNTKv2Python/Examples/CNTK_106B_LSTM_Timeseries_with_IOT_Data_test.py

@@ -10,13 +10,14 @@ import numpy as np
 
 abs_path = os.path.dirname(os.path.abspath(__file__))
 notebook = os.path.join(abs_path, "..", "..", "..", "..", "Tutorials", "CNTK_106B_LSTM_Timeseries_with_IOT_Data.ipynb")
+notebook_timeoutSeconds = 450
 
 def test_cntk_106B_lstm_timeseries_with_iot_data_noErrors(nb):
     errors = [output for cell in nb.cells if 'outputs' in cell
               for output in cell['outputs'] if output.output_type == "error"]
     assert errors == []
 
-expectedEvalErrorByDeviceId = {-1: 0.000085, 0: 0.000085}
+expectedEvalErrorByDeviceId = { -1: 0.000085, 0: 0.000085 }
 
 def test_cntk_106B_lstm_timeseries_with_iot_data_evalCorrect(nb, device_id):
     testCell = [cell for cell in nb.cells

Tools/samples.json

@@ -99,7 +99,7 @@
         "category": ["Image"],
         "name":  "MNIST Feed Forward OCR",
         "url":  "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_103B_MNIST_FeedForwardNetwork.ipynb",
-        "description":  "Use Feed Forward networks on an OCR problem.",
+        "description":  "Recognize hand written digits (OCR) with MNIST data.",
         "language":  ["Python"],
         "type":  ["Tutorial", "Recipe"]
     },
@@ -107,7 +107,7 @@
         "category": ["Image"],
         "name":  "MNIST Autoencoder Dim Reduction",
         "url":  "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_105_Basic_Autoencoder_for_Dimensionality_Reduction.ipynb",
-        "description":  "Use Autoencoder for dimensionality reduction.",
+        "description":  "Compress (using autoencoder) hand written digits from MNIST data with no human input (unsupervised learning).",
         "language":  ["Python"],
         "type":  ["Tutorial", "Recipe"]
     },
@@ -131,7 +131,7 @@
         "category": ["Image"],
         "name":  "Generative Adversarial Networks (GAN)",
         "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.",
+        "description":  "This tutorial is a basic implementation of GAN networks. This allows us generate realistic looking MNIST images with no human input (unsupervised learning).",
         "language":  ["Python"],
         "type":  ["Tutorial", "Recipe"]
     },
@@ -203,7 +203,7 @@
         "category": ["Numeric"],
         "name":  "Feed Forward Classification",
         "url":  "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_102_FeedForward.ipynb",
-        "description":  "Use Feed Forward networks on a classification problem with CNTK and NumPy.",
+        "description":  "Classify cancer using simulated data (Feed Forward) with Numpy",
         "language":  ["Python"],
         "type":  ["Tutorial"]
     },
@@ -219,7 +219,7 @@
         "category": ["Numeric"],
         "name":  "Logistic Regression",
         "url":  "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_101_LogisticRegression.ipynb",
-        "description":  "Logistic regression example with Synthetic data.",
+        "description":  "Classify cancer using simulated data (Logistic Regression).",
         "language":  ["Python", "BrainScript"],
         "type":  ["Tutorial", "Recipe"]
     },
@@ -235,7 +235,7 @@
         "category": ["Numeric"],
         "name":  "LSTM based Timeseries with real world data",
         "url":  "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_106B_LSTM_Timeseries_with_IOT_Data.ipynb",
-        "description":  "Timeseries modeling with LSTMs using real world data.",
+        "description":  "Forecasting using data from an IOT device.",
         "language":  ["Python"],
         "type":  ["Tutorial", "Recipe"]
     },
@@ -347,7 +347,7 @@
         "category": ["Speech", "Text"],
         "name":  "Sequence-to-Sequence",
         "url":  "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_204_Sequence_To_Sequence.ipynb",
-        "description":  "The input is a sequence with a dynamic length, and the output is also a sequence with some dynamic length. This is a natural fit for machine translation , automatic text summarization , word to pronunciation models and even parse tree generation.",
+        "description":  "Translate text from one domain (grapheme) to other (phoneme). The input is a sequence with a dynamic length, and the output is also a sequence with some dynamic length. This is a natural fit for machine translation , automatic text summarization , word to pronunciation models and even parse tree generation.",
         "language":  ["Python"],
         "type":  ["Tutorial"]
     },

bindings/python/doc/tutorials.rst

@@ -17,7 +17,7 @@ Tutorials
      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>`_
+     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>`_, Part B: `with real IOT data <https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_106B_LSTM_Timeseries_with_IOT_Data.ipynb>`_ 
 
 #.  *Recognize objects in images from CIFAR-10 data*
      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`_