Links docs in release branch rather than tagged commit.

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/v2.1/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/release/2.1/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.

Manual/Manual_How_to_train_using_declarative_and_imperative_API.ipynb

@@ -481,7 +481,7 @@
     "### Testing\n",
     "If the test configuration is given, after completion of training, the train method runs evaluation on the specified minibatch source. If you need to run only evaluation without training, consider using [Function.test](https://www.cntk.ai/pythondocs/cntk.ops.functions.html#cntk.ops.functions.Function.test) method instead.\n",
     "\n",
-    "For more advanced scenarios of use Function.train please see [Tutorial 200](https://github.com/Microsoft/CNTK/blob/v2.1/Tutorials/CNTK_200_GuidedTour.ipynb)"
+    "For more advanced scenarios of use Function.train please see [Tutorial 200](https://github.com/Microsoft/CNTK/blob/release/2.1/Tutorials/CNTK_200_GuidedTour.ipynb)"
    ]
   }
  ],

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 uses Python APIs. If you are looking for this example in BrainScript, please look [here](https://github.com/Microsoft/CNTK/tree/v2.1/Tutorials/HelloWorld-LogisticRegression). \n",
+    "The following notebook uses Python APIs. If you are looking for this example in BrainScript, please look [here](https://github.com/Microsoft/CNTK/tree/release/2.1/Tutorials/HelloWorld-LogisticRegression). \n",
     "\n",
     "## Introduction\n",
     "\n",

Tutorials/CNTK_102_FeedForward.ipynb

@@ -881,7 +881,7 @@
     "\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/v2.1/Tutorials/NumpyInterop/FeedForwardNet.py"
+    "[FeedForwardNet.py]: https://github.com/Microsoft/CNTK/blob/release/2.1/Tutorials/NumpyInterop/FeedForwardNet.py"
    ]
   }
  ],

Tutorials/CNTK_103B_MNIST_LogisticRegression.ipynb

@@ -23,7 +23,7 @@
     "\n",
     "We assume that you have successfully completed CNTK 103 Part A.\n",
     "\n",
-    "In this tutorial we will build and train a Multinomial Logistic Regression model using the 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/v2.1/Examples/Image/GettingStarted)\n",
+    "In this tutorial we will build and train a Multinomial Logistic Regression model using the 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/release/2.1/Examples/Image/GettingStarted)\n",
     "\n",
     "## Introduction\n",
     "\n",

Tutorials/CNTK_103C_MNIST_MultiLayerPerceptron.ipynb

@@ -23,7 +23,7 @@
     "\n",
     "We assume that you have successfully completed CNTK 103 Part A.\n",
     "\n",
-    "In this tutorial, we train a multi-layer perceptron 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/v2.1/Examples/Image/GettingStarted)\n",
+    "In this tutorial, we train a multi-layer perceptron 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/release/2.1/Examples/Image/GettingStarted)\n",
     "\n",
     "## Introduction\n",
     "\n",
@@ -770,7 +770,7 @@
    "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/v2.1/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/release/2.1/Examples/Image/Classification/MLP/Python) example."
    ]
   }
  ],

Tutorials/CNTK_103D_MNIST_ConvolutionalNeuralNetwork.ipynb

@@ -23,7 +23,7 @@
     "\n",
     "We assume that you have successfully completed CNTK 103 Part A (MNIST Data Loader).\n",
     "\n",
-    "In this tutorial we will train a Convolutional Neural Network (CNN) on MNIST data. This notebook provides the recipe using the Python API. If you are looking for this example in BrainScript, please look [here](https://github.com/Microsoft/CNTK/tree/v2.1/Examples/Image/GettingStarted)\n",
+    "In this tutorial we will train a Convolutional Neural Network (CNN) on MNIST data. This notebook provides the recipe using the Python API. If you are looking for this example in BrainScript, please look [here](https://github.com/Microsoft/CNTK/tree/release/2.1/Examples/Image/GettingStarted)\n",
     "\n",
     "## Introduction\n",
     "\n",

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/v2.1/Tutorials/SLUHandsOn/atis.train.ctf) \n",
-    "and [test](https://github.com/Microsoft/CNTK/blob/v2.1/Tutorials/SLUHandsOn/atis.test.ctf) \n",
+    "Download the ATIS [training](https://github.com/Microsoft/CNTK/blob/release/2.1/Tutorials/SLUHandsOn/atis.train.ctf) \n",
+    "and [test](https://github.com/Microsoft/CNTK/blob/release/2.1/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/v2.1/Examples/LanguageUnderstanding/ATIS/BrainScript/query.wl) and\n",
-    "[slots](https://github.com/Microsoft/CNTK/blob/v2.1/Examples/LanguageUnderstanding/ATIS/BrainScript/slots.wl)"
+    "[queries](https://github.com/Microsoft/CNTK/blob/release/2.1/Examples/LanguageUnderstanding/ATIS/BrainScript/query.wl) and\n",
+    "[slots](https://github.com/Microsoft/CNTK/blob/release/2.1/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/v2.1/%s/%s?raw=true\"%(location, item['file'])\n",
+    "        url = \"https://github.com/Microsoft/CNTK/blob/release/2.1/%s/%s?raw=true\"%(location, item['file'])\n",
     "        download(url, item['file'])\n",
     "        print(\"Download completed\")\n"
    ]
@@ -818,7 +818,7 @@
     "> 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/v2.1/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/release/2.1/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",

Tutorials/CNTK_204_Sequence_To_Sequence.ipynb

@@ -261,7 +261,7 @@
     "        print(\"Reusing locally cached:\", path)\n",
     "    else:\n",
     "        print(\"Starting download:\", dataPath[k])\n",
-    "        url = \"https://github.com/Microsoft/CNTK/blob/v2.1/Examples/SequenceToSequence/CMUDict/Data/%s?raw=true\"%dataPath[k]\n",
+    "        url = \"https://github.com/Microsoft/CNTK/blob/release/2.1/Examples/SequenceToSequence/CMUDict/Data/%s?raw=true\"%dataPath[k]\n",
     "        download(url, path)\n",
     "        print(\"Download completed\")\n",
     "    dataPath[k] = path"

Tutorials/CNTK_599A_Sequence_To_Sequence.ipynb

@@ -247,7 +247,7 @@
     "    if os.path.exists(file):\n",
     "        print(\"Reusing locally cached: \", file)\n",
     "    else:\n",
-    "        url = \"https://github.com/Microsoft/CNTK/blob/v2.1/Examples/SequenceToSequence/CMUDict/Data/%s?raw=true\"%file\n",
+    "        url = \"https://github.com/Microsoft/CNTK/blob/release/2.1/Examples/SequenceToSequence/CMUDict/Data/%s?raw=true\"%file\n",
     "        print(\"Starting download:\", file)\n",
     "        download(url, file)\n",
     "        print(\"Download completed\")\n"