Fix broken URLs (apache#12508)

docs/architecture/rnn_interface.md

@@ -1,6 +1,6 @@
 # Survey of Existing Interfaces and Implementations
 
-Commonly used deep learning libraries with good RNN/LSTM support include [Theano](http://deeplearning.net/software/theano/library/scan.html) and its wrappers [Lasagne](http://lasagne.readthedocs.org/en/latest/modules/layers/recurrent.html) and [Keras](http://keras.io/layers/recurrent/); [CNTK](https://cntk.codeplex.com/); [TensorFlow](https://www.tensorflow.org/versions/master/tutorials/recurrent/index.html); and various implementations in Torch, such as [this well-known character-level language model tutorial](https://github.com/karpathy/char-rnn), [this](https://github.com/Element-Research/rnn).
+Commonly used deep learning libraries with good RNN/LSTM support include [Theano](http://deeplearning.net/software/theano/library/scan.html) and its wrappers [Lasagne](http://lasagne.readthedocs.org/en/latest/modules/layers/recurrent.html) and [Keras](http://keras.io/layers/recurrent/); [CNTK](https://cntk.codeplex.com/); [TensorFlow](https://www.tensorflow.org/tutorials/sequences/recurrent); and various implementations in Torch, such as [this well-known character-level language model tutorial](https://github.com/karpathy/char-rnn), [this](https://github.com/Element-Research/rnn).
 
 In this document, we present a comparative analysis of the approaches taken by these libraries.
 
@@ -93,7 +93,7 @@ The low-level API for recurrent connection seem to be a *delay node*. But I'm no
 
 ## TensorFlow
 
-The [current example of RNNLM](https://www.tensorflow.org/versions/master/tutorials/recurrent/index.html#recurrent-neural-networks) in TensorFlow uses explicit unrolling for a predefined number of time steps. The white-paper mentions that an advanced control flow API (Theano's scan-like) is planned.
+The [current example of RNNLM](https://www.tensorflow.org/tutorials/sequences/recurrent#recurrent-neural-networks) in TensorFlow uses explicit unrolling for a predefined number of time steps. The white-paper mentions that an advanced control flow API (Theano's scan-like) is planned.
 
 ## Next Steps
 

docs/install/index.md

@@ -272,7 +272,7 @@ Follow the four steps in this [docker documentation](https://docs.docker.com/eng
 
 If you skip this step, you need to use *sudo* each time you invoke Docker.
 
-**Step 3** Install *nvidia-docker-plugin* following the [installation instructions](https://github.com/NVIDIA/nvidia-docker/wiki/Installation). *nvidia-docker-plugin* is required to enable the usage of GPUs from the docker containers.
+**Step 3** Install *nvidia-docker-plugin* following the [installation instructions](https://github.com/NVIDIA/nvidia-docker/wiki). *nvidia-docker-plugin* is required to enable the usage of GPUs from the docker containers.
 
 **Step 4** Pull the MXNet docker image.
 

docs/install/windows_setup.md

@@ -55,7 +55,7 @@ These commands produce a library called ```mxnet.dll``` in the ```./build/Releas
 Next, we install ```graphviz``` library that we use for visualizing network graphs you build on MXNet. We will also install [Jupyter Notebook](http://jupyter.readthedocs.io/)  used for running MXNet tutorials and examples.
 - Install ```graphviz``` by downloading MSI installer from [Graphviz Download Page](https://graphviz.gitlab.io/_pages/Download/Download_windows.html).
 **Note** Make sure to add graphviz executable path to PATH environment variable. Refer [here for more details](http://stackoverflow.com/questions/35064304/runtimeerror-make-sure-the-graphviz-executables-are-on-your-systems-path-aft)
-- Install ```Jupyter``` by installing [Anaconda for Python 2.7](https://www.continuum.io/downloads)
+- Install ```Jupyter``` by installing [Anaconda for Python 2.7](https://www.anaconda.com/download/)
 **Note** Do not install Anaconda for Python 3.5. MXNet has few compatibility issue with Python 3.5.
 
  
@@ -69,7 +69,7 @@ We have installed MXNet core library. Next, we will install MXNet interface pack
 ## Install MXNet for Python
 
 1. Install ```Python``` using windows installer available [here](https://www.python.org/downloads/release/python-2712/).
-2. Install ```Numpy``` using windows installer available [here](http://scipy.org/install.html).
+2. Install ```Numpy``` using windows installer available [here](https://scipy.org/index.html).
 3. Next, we install Python package interface for MXNet. You can find the Python interface package for [MXNet on GitHub](https://github.com/dmlc/mxnet/tree/master/python/mxnet).
 
 ```bash

docs/tutorials/onnx/export_mxnet_to_onnx.md

@@ -55,7 +55,7 @@ Help on function export_model in module mxnet.contrib.onnx.mx2onnx.export_model:
 export_model(sym, params, input_shape, input_type=<type 'numpy.float32'>, onnx_file_path=u'model.onnx', verbose=False)
     Exports the MXNet model file, passed as a parameter, into ONNX model.
     Accepts both symbol,parameter objects as well as json and params filepaths as input.
-    Operator support and coverage - https://cwiki.apache.org/confluence/display/MXNET/ONNX
+    Operator support and coverage - https://cwiki.apache.org/confluence/display/MXNET/MXNet-ONNX+Integration
 
     Parameters
     ----------

python/mxnet/contrib/onnx/mx2onnx/export_model.py

@@ -36,7 +36,8 @@ def export_model(sym, params, input_shape, input_type=np.float32,
                  onnx_file_path='model.onnx', verbose=False):
     """Exports the MXNet model file, passed as a parameter, into ONNX model.
     Accepts both symbol,parameter objects as well as json and params filepaths as input.
-    Operator support and coverage - https://cwiki.apache.org/confluence/display/MXNET/ONNX
+    Operator support and coverage -
+    https://cwiki.apache.org/confluence/display/MXNET/MXNet-ONNX+Integration
 
     Parameters
     ----------

python/mxnet/contrib/onnx/onnx2mx/import_model.py

@@ -23,7 +23,8 @@
 
 def import_model(model_file):
     """Imports the ONNX model file, passed as a parameter, into MXNet symbol and parameters.
-    Operator support and coverage - https://cwiki.apache.org/confluence/display/MXNET/ONNX
+    Operator support and coverage -
+    https://cwiki.apache.org/confluence/display/MXNET/MXNet-ONNX+Integration
 
     Parameters
     ----------

python/mxnet/contrib/text/embedding.py

@@ -490,7 +490,7 @@ class GloVe(_TokenEmbedding):
 
     License for pre-trained embeddings:
 
-        https://opendatacommons.org/licenses/pddl/
+        https://fedoraproject.org/wiki/Licensing/PDDL
 
 
     Parameters