Bump versions

Conflict resolved for: bindings/python/doc/tutorials.rst
2017-02-22 13:46:13 +01:00 · 2017-02-22 13:46:13 +01:00 · dc0dde69ca
--- a/Examples/Text/WordLMWithSampledSoftmax/README.md
+++ b/Examples/Text/WordLMWithSampledSoftmax/README.md
@ -4,7 +4,7 @@ This example demonstrates how to use sampled softmax for training a token based
 The model predicts the next word in a text given the previous ones where the probability of the next word is computed using a softmax.
 As the number of different words might be very high this final softmax step can turn out to be costly.

-Sampled-softmax is a technique to reduce this cost at training time. For details see also the [sampled softmax tutorial](https://github.com/Microsoft/CNTK/blob/v2.0.beta11.0/Tutorials/CNTK_207_Training_with_Sampled_Softmax.ipynb)
+Sampled-softmax is a technique to reduce this cost at training time. For details see also the [sampled softmax tutorial](https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/CNTK_207_Training_with_Sampled_Softmax.ipynb)

 Note the provided data set has only 10.000 distinct words. This number is still not very high and sampled softmax doesn't show any significant perf improvements here.
 The real perf gains will show up with larger vocabularies.
@ -19,4 +19,4 @@ Run word-rnn.py to train a model.
 The main section of word-rnn defines some parameters to control the training.

 * `use_sampled_softmax` allows to switch between sampled-softmax and full softmax.
-* `softmax_sample_size` sets the number of random samples used in sampled-softmax. 
+* `softmax_sample_size` sets the number of random samples used in sampled-softmax. 
--- a/Scripts/install/linux/install-cntk.sh
+++ b/Scripts/install/linux/install-cntk.sh
@ -67,7 +67,7 @@ CNTK_EXAMPLES_PATH="$PWD/Examples"
 CNTK_TUTORIALS_PATH="$PWD/Tutorials"
 CNTK_BINARY="$CNTK_BIN_PATH/cntk"
 CNTK_PY_ENV_FILE="$SCRIPT_DIR/conda-linux-cntk-py$PY_VERSION-environment.yml"
-CNTK_WHEEL_PATH="cntk/python/cntk-2.0.beta11.0-$PYWHEEL_QUALIFIER-linux_x86_64.whl"
+CNTK_WHEEL_PATH="cntk/python/cntk-2.0.beta12.0-$PYWHEEL_QUALIFIER-linux_x86_64.whl"

 test -d "$CNTK_BIN_PATH" && test -d "$CNTK_LIB_PATH" && test -d "$CNTK_DEP_LIB_PATH" &&
 test -d "$CNTK_TUTORIALS_PATH" &&
--- a/Source/CNTK/CNTK.cpp
+++ b/Source/CNTK/CNTK.cpp
@ -614,7 +614,7 @@ int wmainWithBS(int argc, wchar_t* argv[]) // called from wmain which is a wrapp

 static void PrintBanner(int argc, wchar_t* argv[], const string& timestamp)
 {
-    fprintf(stderr, "CNTK 2.0.beta11.0 (");
+    fprintf(stderr, "CNTK 2.0.beta12.0+ (");
 #ifdef _GIT_EXIST
    fprintf(stderr, "%s %.6s, ", _BUILDBRANCH_, _BUILDSHA1_);
 #endif
--- a/Tutorials/CNTK_101_LogisticRegression.ipynb
+++ b/Tutorials/CNTK_101_LogisticRegression.ipynb
@ -21,7 +21,7 @@
    "\n",
    "This tutorial is targeted to individuals who are new to CNTK and to machine learning. In this tutorial, you will train a simple yet powerful machine learning model that is widely used in industry for a variety of applications. The model trained below scales to massive data sets in the most expeditious manner by harnessing computational scalability leveraging the computational resources you may have (one or more CPU cores, one or more GPUs, a cluster of CPUs or a cluster of GPUs), transparently via the CNTK library.\n",
    "\n",
-    "The following notebook users Python APIs. If you are looking for this example in BrainScript, please look [here](https://github.com/Microsoft/CNTK/tree/v2.0.beta11.0/Tutorials/HelloWorld-LogisticRegression). \n",
+    "The following notebook users Python APIs. If you are looking for this example in BrainScript, please look [here](https://github.com/Microsoft/CNTK/tree/v2.0.beta12.0/Tutorials/HelloWorld-LogisticRegression). \n",
    "\n",
    "## Introduction\n",
    "\n",
--- a/Tutorials/CNTK_102_FeedForward.ipynb
+++ b/Tutorials/CNTK_102_FeedForward.ipynb
@ -909,7 +909,7 @@
    "\n",
    "If you want to try running the tutorial from python command prompt. Please run the [FeedForwardNet.py][] example.\n",
    "\n",
-    "[FeedForwardNet.py]: https://github.com/Microsoft/CNTK/blob/v2.0.beta11.0/Tutorials/NumpyInterop/FeedForwardNet.py"
+    "[FeedForwardNet.py]: https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/NumpyInterop/FeedForwardNet.py"
   ]
  }
 ],
--- a/Tutorials/CNTK_103A_MNIST_DataLoader.ipynb
+++ b/Tutorials/CNTK_103A_MNIST_DataLoader.ipynb
@ -12,7 +12,7 @@
    "\n",
    "CNTK 103 tutorial is divided into two 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.beta11.0/Tutorials/CNTK_103B_MNIST_FeedForwardNetwork.ipynb): We will use the feedforward classifier used in CNTK 102 to classify digits in MNIST data set.\n",
+    "- [Part B](https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/CNTK_103B_MNIST_FeedForwardNetwork.ipynb): We will use the feedforward classifier used in CNTK 102 to classify digits in MNIST data set.\n",
    "\n",
    "[MNIST]: http://yann.lecun.com/exdb/mnist/\n",
    "\n"
--- a/Tutorials/CNTK_103B_MNIST_FeedForwardNetwork.ipynb
+++ b/Tutorials/CNTK_103B_MNIST_FeedForwardNetwork.ipynb
@ -23,7 +23,7 @@
    "\n",
    "We assume that you have successfully completed CNTK 103 Part A.\n",
    "\n",
-    "In this tutorial we will train a fully connected network on MNIST data. This notebook provides the recipe using Python APIs. If you are looking for this example in BrainScript, please look [here](https://github.com/Microsoft/CNTK/tree/v2.0.beta11.0/Examples/Image/GettingStarted)\n",
+    "In this tutorial we will train a fully connected network on MNIST data. This notebook provides the recipe using Python APIs. If you are looking for this example in BrainScript, please look [here](https://github.com/Microsoft/CNTK/tree/v2.0.beta12.0/Examples/Image/GettingStarted)\n",
    "\n",
    "## Introduction\n",
    "\n",
@ -824,7 +824,7 @@
   "source": [
    "#### Code link\n",
    "\n",
-    "If you want to try running the tutorial from Python command prompt please run the [SimpleMNIST.py](https://github.com/Microsoft/CNTK/tree/v2.0.beta11.0/Examples/Image/Classification/MLP/Python) example."
+    "If you want to try running the tutorial from Python command prompt please run the [SimpleMNIST.py](https://github.com/Microsoft/CNTK/tree/v2.0.beta12.0/Examples/Image/Classification/MLP/Python) example."
   ]
  },
  {
--- a/Tutorials/CNTK_202_Language_Understanding.ipynb
+++ b/Tutorials/CNTK_202_Language_Understanding.ipynb
@ -41,12 +41,12 @@
    "In this tutorial we are going to use a (lightly preprocessed) version of the ATIS dataset. You can download the data automatically by running the cells below or by executing the manual instructions.\n",
    "\n",
    "#### Fallback manual instructions\n",
-    "Download the ATIS [training](https://github.com/Microsoft/CNTK/blob/v2.0.beta11.0/Tutorials/SLUHandsOn/atis.train.ctf) \n",
-    "and [test](https://github.com/Microsoft/CNTK/blob/v2.0.beta11.0/Tutorials/SLUHandsOn/atis.test.ctf) \n",
+    "Download the ATIS [training](https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/SLUHandsOn/atis.train.ctf) \n",
+    "and [test](https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/SLUHandsOn/atis.test.ctf) \n",
    "files and put them at the same folder as this notebook. If you want to see how the model is \n",
    "predicting on new sentences you will also need the vocabulary files for \n",
-    "[queries](https://github.com/Microsoft/CNTK/blob/v2.0.beta11.0/Examples/LanguageUnderstanding/ATIS/BrainScript/query.wl) and\n",
-    "[slots](https://github.com/Microsoft/CNTK/blob/v2.0.beta11.0/Examples/LanguageUnderstanding/ATIS/BrainScript/slots.wl)"
+    "[queries](https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Examples/LanguageUnderstanding/ATIS/BrainScript/query.wl) and\n",
+    "[slots](https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Examples/LanguageUnderstanding/ATIS/BrainScript/slots.wl)"
   ]
  },
  {
@ -99,7 +99,7 @@
    "        print(\"Reusing locally cached:\", item['file'])\n",
    "    else:\n",
    "        print(\"Starting download:\", item['file'])\n",
-    "        url = \"https://github.com/Microsoft/CNTK/blob/v2.0.beta11.0/%s/%s?raw=true\"%(location, item['file'])\n",
+    "        url = \"https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/%s/%s?raw=true\"%(location, item['file'])\n",
    "        download(url, item['file'])\n",
    "        print(\"Download completed\")\n"
   ]
@ -771,7 +771,7 @@
    "> Note: training with Batch Normalization is currently only supported on GPU.\n",
    "\n",
    "So your task will be to insert batch-normalization layers before and after the recurrent LSTM layer.\n",
-    "If you have completed the [hands-on labs on image processing](https://github.com/Microsoft/CNTK/blob/v2.0.beta11.0/Tutorials/CNTK_201B_CIFAR-10_ImageHandsOn.ipynb),\n",
+    "If you have completed the [hands-on labs on image processing](https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/CNTK_201B_CIFAR-10_ImageHandsOn.ipynb),\n",
    "you may remember that the [batch-normalization layer](https://www.cntk.ai/pythondocs/layerref.html#batchnormalization-layernormalization-stabilizer) has this form:\n",
    "```\n",
    "    BatchNormalization()\n",
--- a/Tutorials/CNTK_204_Sequence_To_Sequence.ipynb
+++ b/Tutorials/CNTK_204_Sequence_To_Sequence.ipynb
@ -246,7 +246,7 @@
    "    if os.path.exists(file):\n",
    "        print(\"Reusing locally cached: \", file)\n",
    "    else:\n",
-    "        url = \"https://github.com/Microsoft/CNTK/blob/v2.0.beta11.0/Examples/SequenceToSequence/CMUDict/Data/%s?raw=true\"%file\n",
+    "        url = \"https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Examples/SequenceToSequence/CMUDict/Data/%s?raw=true\"%file\n",
    "        print(\"Starting download:\", file)\n",
    "        download(url, file)\n",
    "        print(\"Download completed\")\n"
--- a/bindings/python/cntk/init.py
+++ b/bindings/python/cntk/init.py
@ -3,7 +3,7 @@
 # for full license information.
 # ==============================================================================

-__version__ = '2.0.beta11.0+'
+__version__ = '2.0.beta12.0+'

 import os
 import numpy as np
--- a/bindings/python/doc/conf.py
+++ b/bindings/python/doc/conf.py
@ -63,9 +63,9 @@ author = 'Microsoft'
 # built documents.
 #
 # The short X.Y version.
-version = '2.0.beta11.0'
+version = '2.0.beta12.0'
 # The full version, including alpha/beta/rc tags.
-release = '2.0.beta11.0'
+release = '2.0.beta12.0'

 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.
--- a/bindings/python/doc/examples.rst
+++ b/bindings/python/doc/examples.rst
@ -4,30 +4,30 @@ Examples
 The best way to learn about the APIs is to look at the
 following examples in the [CNTK clone root]/Examples directory:

-  `MNIST <https://github.com/Microsoft/CNTK/blob/v2.0.beta11.0/Examples/Image/Classification/MLP/Python/SimpleMNIST.py>`__:
+-  `MNIST <https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Examples/Image/Classification/MLP/Python/SimpleMNIST.py>`__:
   A fully connected feed-forward model for classification of MNIST
   images. (follow the instructions in
   Examples/Image/DataSets/MNIST/README.md)

-  `TrainResNet_CIFAR10 <https://github.com/Microsoft/CNTK/blob/v2.0.beta11.0/Examples/Image/Classification/ResNet/Python/TrainResNet_CIFAR10.py>`__:
+-  `TrainResNet_CIFAR10 <https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Examples/Image/Classification/ResNet/Python/TrainResNet_CIFAR10.py>`__:
   An image classification ResNet model for training on the CIFAR image
   dataset. (follow the instructions in
   Examples/Image/DataSets/CIFAR-10/README.md to get the CIFAR dataset
   and convert it to the CNTK supported format)

-  `SequenceClassification <https://github.com/Microsoft/CNTK/blob/v2.0.beta11.0/Examples/SequenceClassification/SimpleExample/Python/SequenceClassification.py>`__:
+-  `SequenceClassification <https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Examples/SequenceClassification/SimpleExample/Python/SequenceClassification.py>`__:
   An LSTM sequence classification model for text data.

-  `Sequence2Sequence <https://github.com/Microsoft/CNTK/blob/v2.0.beta11.0/Examples/SequenceToSequence/CMUDict/Python/Sequence2Sequence.py>`__:
+-  `Sequence2Sequence <https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Examples/SequenceToSequence/CMUDict/Python/Sequence2Sequence.py>`__:
   A sequence to sequence grapheme to phoneme translation model that
   trains on the CMUDict corpus.

-  `NumpyInterop <https://github.com/Microsoft/CNTK/blob/v2.0.beta11.0/Tutorials/NumpyInterop/FeedForwardNet.py>`__
+-  `NumpyInterop <https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/NumpyInterop/FeedForwardNet.py>`__
   - NumPy interoperability example showing how to train a simple feed-forward
   network with training data fed using NumPy arrays.

-  `LanguageUnderstanding <https://github.com/Microsoft/CNTK/blob/v2.0.beta11.0/Examples/LanguageUnderstanding/ATIS/Python/LanguageUnderstanding.py>`__
+-  `LanguageUnderstanding <https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Examples/LanguageUnderstanding/ATIS/Python/LanguageUnderstanding.py>`__
   - Language Understanding.

-  `Video <https://github.com/Microsoft/CNTK/blob/v2.0.beta11.0/Examples/Video/GettingStarted/Python/Conv3D_UCF11.py>`__
+-  `Video <https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Examples/Video/GettingStarted/Python/Conv3D_UCF11.py>`__
   - Basic 3D convolution networks for deep learning on video tasks.
--- a/bindings/python/doc/index.rst
+++ b/bindings/python/doc/index.rst
@ -2,7 +2,7 @@
 .. some aliases
 .. _CNTK: http://cntk.ai/

-Python API for CNTK (2.0.beta11.0)
+Python API for CNTK (2.0.beta12.0)
 =================================

 CNTK_, the Microsoft Cognitive Toolkit, is a system for describing, training,
@ -10,7 +10,7 @@ and executing computational networks. It is also a framework for describing
 arbitrary learning machines such as deep neural networks (DNNs). CNTK is an
 implementation of computational networks that supports both CPU and GPU.
 
-This page describes the Python API for CNTK_ version 2.0.beta11.0. This is an ongoing effort
+This page describes the Python API for CNTK_ version 2.0.beta12.0. This is an ongoing effort
 to expose such an API to the CNTK system, thus enabling the use of higher-level
 tools such as IDEs to facilitate the definition of computational networks, to execute
 them on sample data in real time. Please give feedback through these `channels`_.
--- a/bindings/python/doc/tutorials.rst
+++ b/bindings/python/doc/tutorials.rst
@ -8,19 +8,19 @@
     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/blob/v2.0.beta11.0/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.beta12.0/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, FFN)*
-     CNTK 105 Part A: `Data preparation <https://github.com/Microsoft/CNTK/blob/v2.0.beta11.0/Tutorials/CNTK_103A_MNIST_DataLoader.ipynb>`_ ,  Part B: `Feed Forward autoencoder`_
+     CNTK 105 Part A: `Data preparation <https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/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.beta11.0/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.beta12.0/Tutorials/CNTK_106A_LSTM_Timeseries_with_Simulated_Data.ipynb>`_

 #.  *Recognize objects in images from CIFAR-10 data (Convolutional Network, CNN)*
-     CNTK 201 Part A: `Data preparation <https://github.com/Microsoft/CNTK/blob/v2.0.beta11.0/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.beta12.0/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
@ -35,7 +35,7 @@
     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/blob/v2.0.beta11.0/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.beta12.0/Tutorials/CNTK_103A_MNIST_DataLoader.ipynb>`_ ,  Part B: `Basic Generative Adversarial Networks (GAN)`_

 #.  *Training with Sampled Softmax*
     CNTK 207: `Training with Sampled Softmax`_
@ -44,21 +44,21 @@ Try these notebooks pre-installed on `CNTK Azure Notebooks`_ for free.

 For our Japanese users, you can find some of the `tutorials in Japanese`_.

-.. _`Logistic Regression`: https://github.com/Microsoft/CNTK/blob/v2.0.beta11.0/Tutorials/CNTK_101_LogisticRegression.ipynb
-.. _`Feed Forward network`: https://github.com/Microsoft/CNTK/blob/v2.0.beta11.0/Tutorials/CNTK_102_FeedForward.ipynb
-.. _`Data preparation`: https://github.com/Microsoft/CNTK/blob/v2.0.beta11.0/Tutorials/CNTK_103A_MNIST_DataLoader.ipynb
-.. _`Feed Forward classifier`: https://github.com/Microsoft/CNTK/blob/v2.0.beta11.0/Tutorials/CNTK_103B_MNIST_FeedForwardNetwork.ipynb
-.. _`Time Series basics`: https://github.com/Microsoft/CNTK/blob/v2.0.beta11.0/Tutorials/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.ipynb
-.. _`Feed Forward autoencoder`: https://github.com/Microsoft/CNTK/blob/v2.0.beta11.0/Tutorials/CNTK_105_Basic_Autoencoder_for_Dimensionality_Reduction.ipynb
-.. _`Basic LSTM based time series`: https://github.com/Microsoft/CNTK/blob/v2.0.beta11.0/Tutorials/CNTK_106A_LSTM_Timeseries_with_Simulated_Data.ipynb
-.. _`data preparation`: https://github.com/Microsoft/CNTK/blob/v2.0.beta11.0/Tutorials/CNTK_201A_CIFAR-10_DataLoader.ipynb
-.. _`VGG and ResNet classifiers`: https://github.com/Microsoft/CNTK/blob/v2.0.beta11.0/Tutorials/CNTK_201B_CIFAR-10_ImageHandsOn.ipynb
-.. _`Language understanding`: https://github.com/Microsoft/CNTK/blob/v2.0.beta11.0/Tutorials/CNTK_202_Language_Understanding.ipynb
-.. _`Reinforcement learning basics`: https://github.com/Microsoft/CNTK/blob/v2.0.beta11.0/Tutorials/CNTK_203_Reinforcement_Learning_Basics.ipynb
-.. _`Sequence to sequence basics`: https://github.com/Microsoft/CNTK/blob/v2.0.beta11.0/Tutorials/CNTK_204_Sequence_To_Sequence.ipynb
-.. _`Artistic Style Transfer`: https://github.com/Microsoft/CNTK/blob/v2.0.beta11.0/Tutorials/CNTK_205_Artistic_Style_Transfer.ipynb
-.. _`Basic Generative Adversarial Networks (GAN)`: https://github.com/Microsoft/CNTK/blob/v2.0.beta11.0/Tutorials/CNTK_206_Basic_GAN.ipynb
-.. _`Training with Sampled Softmax`: https://github.com/Microsoft/CNTK/blob/v2.0.beta11.0/Tutorials/CNTK_207_Training_with_Sampled_Softmax.ipynb
+.. _`Logistic Regression`: https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/CNTK_101_LogisticRegression.ipynb
+.. _`Feed Forward network`: https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/CNTK_102_FeedForward.ipynb
+.. _`Data preparation`: https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/CNTK_103A_MNIST_DataLoader.ipynb
+.. _`Feed Forward classifier`: https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/CNTK_103B_MNIST_FeedForwardNetwork.ipynb
+.. _`Time Series basics`: https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.ipynb
+.. _`Feed Forward autoencoder`: https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/CNTK_105_Basic_Autoencoder_for_Dimensionality_Reduction.ipynb
+.. _`Basic LSTM based time series`: https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/CNTK_106A_LSTM_Timeseries_with_Simulated_Data.ipynb
+.. _`data preparation`: https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/CNTK_201A_CIFAR-10_DataLoader.ipynb
+.. _`VGG and ResNet classifiers`: https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/CNTK_201B_CIFAR-10_ImageHandsOn.ipynb
+.. _`Language understanding`: https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/CNTK_202_Language_Understanding.ipynb
+.. _`Reinforcement learning basics`: https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/CNTK_203_Reinforcement_Learning_Basics.ipynb
+.. _`Sequence to sequence basics`: https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/CNTK_204_Sequence_To_Sequence.ipynb
+.. _`Artistic Style Transfer`: https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/CNTK_205_Artistic_Style_Transfer.ipynb
+.. _`Basic Generative Adversarial Networks (GAN)`: https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/CNTK_206_Basic_GAN.ipynb
+.. _`Training with Sampled Softmax`: https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/CNTK_207_Training_with_Sampled_Softmax.ipynb

 .. _`CNTK Azure Notebooks`: https://notebooks.azure.com/cntk/libraries/tutorials
 .. _`tutorials in Japanese`: https://notebooks.azure.com/library/cntkbeta2_ja
--- a/bindings/python/setup.py
+++ b/bindings/python/setup.py
@ -174,7 +174,7 @@ if IS_PY2:
    cntk_install_requires.append('enum34>=1.1.6')

 setup(name="cntk",
-      version="2.0.beta11.0",
+      version="2.0.beta12.0",
      url="http://cntk.ai",
      ext_modules=[cntk_module],
      packages=packages,