second round of fixing broken links in multiple files

docs/python_docs/python/tutorials/packages/gluon/image/mnist.md

@@ -113,7 +113,7 @@ to train the MLP network we defined above.
 For our training, we will make use of the stochastic gradient descent (SGD) optimizer. In particular, we'll be using mini-batch SGD. Standard SGD processes train data one example at a time. In practice, this is very slow and one can speed up the process by processing examples in small batches. In this case, our batch size will be 100, which is a reasonable choice. Another parameter we select here is the learning rate, which controls the step size the optimizer takes in search of a solution. We'll pick a learning rate of 0.02, again a reasonable choice. Settings such as batch size and learning rate are what are usually referred to as hyper-parameters. What values we give them can have a great impact on training performance.
 
 We will use [Trainer](/api/python/docs/api/gluon/trainer.html) class to apply the
-[SGD optimizer](https://mxnet.io/api/python/docs/api/gluon-related/_autogen/mxnet.optimizer.SGD.html) on the
+[SGD optimizer](/api/python/docs/api/optimizer/index.html#mxnet.optimizer.SGD) on the
 initialized parameters.
 
 ```python

docs/python_docs/python/tutorials/packages/ndarray/sparse/csr.md

@@ -556,7 +556,7 @@ except mx.MXNetError as err:
 
 ## Next 
 
-[Train a Linear Regression Model with Sparse Symbols](http://mxnet.apache.org/tutorials/sparse/train.html)
+[Train a Linear Regression Model with Sparse Symbols](/api/python/docs/tutorials/packages/ndarray/sparse/train.html)
 
 
 <!-- INSERT SOURCE DOWNLOAD BUTTONS -->

docs/python_docs/python/tutorials/packages/ndarray/sparse/row_sparse.md

@@ -578,7 +578,7 @@ except mx.MXNetError as err:
 
 ## Next
 
-[Train a Linear Regression Model with Sparse Symbols](http://mxnet.apache.org/tutorials/sparse/train.html)
+[Train a Linear Regression Model with Sparse Symbols](/api/python/docs/tutorials/packages/ndarray/sparse/train.html)
 
 
 <!-- INSERT SOURCE DOWNLOAD BUTTONS -->

docs/python_docs/python/tutorials/packages/ndarray/sparse/train.md

@@ -27,18 +27,18 @@ then train a linear regression model using sparse symbols with the Module API.
 
 To complete this tutorial, we need:
 
-- MXNet. See the instructions for your operating system in [Setup and Installation](https://mxnet.io/get_started).  
+- MXNet. See the instructions for your operating system in [Setup and Installation](/get_started).  
 
-- [Jupyter Notebook](https://jupyter.org/index.html) and [Python Requests](http://docs.python-requests.org/en/master/) packages.
+- [Jupyter Notebook](https://jupyter.org/index.html) and [Python Requests](https://3.python-requests.org/) packages.
 ```
 pip install jupyter requests
 ```
 
 - Basic knowledge of Symbol in MXNet. See the detailed tutorial for Symbol in [Symbol - Neural Network Graphs and Auto-differentiation](https://mxnet.apache.org/tutorials/basic/symbol.html).
 
-- Basic knowledge of CSRNDArray in MXNet. See the detailed tutorial for CSRNDArray in [CSRNDArray - NDArray in Compressed Sparse Row Storage Format](https://mxnet.apache.org/versions/master/tutorials/sparse/csr.html).
+- Basic knowledge of CSRNDArray in MXNet. See the detailed tutorial for CSRNDArray in [CSRNDArray - NDArray in Compressed Sparse Row Storage Format](/api/python/docs/tutorials/packages/ndarray/sparse/csr.html).
 
-- Basic knowledge of RowSparseNDArray in MXNet. See the detailed tutorial for RowSparseNDArray in [RowSparseNDArray - NDArray for Sparse Gradient Updates](https://mxnet.apache.org/versions/master/tutorials/sparse/row_sparse.html).
+- Basic knowledge of RowSparseNDArray in MXNet. See the detailed tutorial for RowSparseNDArray in [RowSparseNDArray - NDArray for Sparse Gradient Updates](/api/python/docs/tutorials/packages/ndarray/sparse/row_sparse.html).
 
 ## Variables
 
@@ -155,7 +155,7 @@ f = mx.sym.sparse.elemwise_add(c, c)
 ### Storage Type Inference
 
 What will be the output storage types of sparse symbols? In MXNet, for any sparse symbol, the result storage types are inferred based on storage types of inputs.
-You can read the [Sparse Symbol API](https://mxnet.apache.org/versions/master/api/python/symbol/sparse.html) documentation to find what output storage types are. In the example below we will try out the storage types introduced in the Row Sparse and Compressed Sparse Row tutorials: `default` (dense), `csr`, and `row_sparse`.
+You can read the [Sparse Symbol API](/api/python/docs/api/symbol/sparse/index.html) documentation to find what output storage types are. In the example below we will try out the storage types introduced in the Row Sparse and Compressed Sparse Row tutorials: `default` (dense), `csr`, and `row_sparse`.
 
 
 ```python

docs/static_site/src/_includes/get_started/devices/nvidia-jetson.md

@@ -1,4 +1,4 @@
 # NVIDIA Jetson Devices
 
 To install MXNet on a Jetson TX or Nano, please refer to the [Jetson installation
-guide](get_started/jetson_setup).
+guide](/get_started/jetson_setup).

docs/static_site/src/_includes/get_started/get_started.html

@@ -256,8 +256,8 @@ <h2>Installing MXNet</h2>
             </div> <!-- END - C++-->
 
             <br>
-            For more installation options, refer to the <a href="get_started/ubuntu_setup.html">Ubuntu installation guide</a> and
-            <a href="get_started/centos_setup.html">CentOS installation guide</a>.
+            For more installation options, refer to the <a href="/get_started/ubuntu_setup.html">Ubuntu installation guide</a> and
+            <a href="/get_started/centos_setup.html">CentOS installation guide</a>.
         </div> <!-- END - Linux -->
 
 
@@ -354,7 +354,7 @@ <h2>Installing MXNet</h2>
                 </div> <!-- End of cpu gpu -->
             </div>
             <br>
-            For more installation options, refer to the <a href="get_started/osx_setup.html">MXNet macOS installation guide</a>.
+            For more installation options, refer to the <a href="/get_started/osx_setup.html">MXNet macOS installation guide</a>.
         </div> <!-- END - Mac OS -->
 
 
@@ -440,7 +440,7 @@ <h2>Installing MXNet</h2>
                 </div> <!-- End of cpu gpu -->
             </div> <!-- End of C++ -->
 
-            For more installation options, refer to the <a href="get_started/windows_setup.html">MXNet Windows installation guide</a>.
+            For more installation options, refer to the <a href="/get_started/windows_setup.html">MXNet Windows installation guide</a>.
         </div> <!-- End of Windows -->
 
 

docs/static_site/src/_includes/get_started/linux/java/cpu.md

@@ -1,6 +1,6 @@
 You can use the Maven packages defined in the following dependency to include MXNet in your Java
 project. The Java API is provided as a subset of the Scala API and is intended for inference only.
-Please refer to the <a href="get_started/java_setup.html">MXNet-Java setup guide</a> for a detailed set of
+Please refer to the <a href="/get_started/java_setup.html">MXNet-Java setup guide</a> for a detailed set of
 instructions to help you with the setup process.
 
 <a href="https://repository.apache.org/#nexus-search;gav~org.apache.mxnet~~1.5.0~~">

docs/static_site/src/_includes/get_started/linux/java/gpu.md

@@ -1,6 +1,6 @@
 You can use the Maven packages defined in the following dependency to include MXNet in your Java
 project. The Java API is provided as a subset of the Scala API and is intended for inference only.
-Please refer to the <a href="get_started/java_setup.html">MXNet-Java setup guide</a> for a detailed set of
+Please refer to the <a href="/get_started/java_setup.html">MXNet-Java setup guide</a> for a detailed set of
 instructions to help you with the setup process.
 
 <a href="https://repository.apache.org/#nexus-search;gav~org.apache.mxnet~~1.5.0~~">

docs/static_site/src/_includes/get_started/linux/julia/build-from-source.md

@@ -1,2 +1,2 @@
-Refer to the [Julia section of the MXNet Ubuntu installation guide](get_started/ubuntu_setup#install-the-mxnet-package-for-julia).
+Refer to the [Julia section of the MXNet Ubuntu installation guide](/get_started/ubuntu_setup#install-the-mxnet-package-for-julia).
 

docs/static_site/src/_includes/get_started/linux/r/cpu.md

@@ -1,5 +1,5 @@
 The default version of R that is installed with `apt-get` is insufficient. You will need
-to first [install R v3.4.4+ and build MXNet from source](get_started/ubuntu_setup.html#install-the-mxnet-package-for-r).
+to first [install R v3.4.4+ and build MXNet from source](/get_started/ubuntu_setup.html#install-the-mxnet-package-for-r).
 
 After you have setup R v3.4.4+ and MXNet, you can build and install the MXNet R bindings with the following, assuming that `incubator-mxnet` is the source directory you used to build MXNet as follows:
 

docs/static_site/src/_includes/get_started/linux/r/gpu.md

@@ -1,7 +1,7 @@
 The default version of R that is installed with `apt-get` is insufficient. You will need
 to first
 [install R v3.4.4+ and build MXNet from
-source](get_started/ubuntu_setup.html#install-the-mxnet-package-for-r).
+source](/get_started/ubuntu_setup.html#install-the-mxnet-package-for-r).
 
 After you have setup R v3.4.4+ and MXNet, you can build and install the MXNet R bindings
 with the

docs/static_site/src/_includes/get_started/macos/java/cpu.md

@@ -1,7 +1,7 @@
 You can use the Maven packages defined in the following dependency to include MXNet in
 your Java project. The Java API is provided as a subset of the Scala API and is intended for
 inference only.
-Please refer to the [MXNet-Java setup guide](get_started/java_setup.html) for a detailed set of instructions to help you with the setup process.
+Please refer to the [MXNet-Java setup guide](/get_started/java_setup.html) for a detailed set of instructions to help you with the setup process.
 
 <a href="https://repository.apache.org/#nexus-search;gav~org.apache.mxnet~~1.5.0~~"><img
 src="https://img.shields.io/badge/org.apache.mxnet-mac cpu-green.svg"

docs/static_site/src/_includes/get_started/pip_snippet.md

@@ -1,6 +1,6 @@
 MXNet offers MKL pip packages that will be much faster when running on Intel hardware.
 Check the chart below for other options, refer to <a href="https://pypi.org/project/mxnet/">PyPI for
-other MXNet pip packages</a>, or <a href="get_started/validate_mxnet.html">validate your MXNet installation</a>.
+other MXNet pip packages</a>, or <a href="/get_started/validate_mxnet.html">validate your MXNet installation</a>.
 
 <div style="text-align: center">
     <img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/install/pip-packages-1.5.1.png"

docs/static_site/src/_includes/get_started/windows/julia/build-from-source.md

@@ -1 +1 @@
-Refer to the [Julia section of the MXNet Windows installation guide](get_started/windows_setup.html#install-the-mxnet-package-for-julia).
+Refer to the [Julia section of the MXNet Windows installation guide](/get_started/windows_setup.html#install-the-mxnet-package-for-julia).

docs/static_site/src/_includes/get_started/windows/perl/perl.md

@@ -1 +1 @@
-Refer to the [Perl section of the MXNet Windows installation guide](get_started/windows_setup.html#install-the-mxnet-package-for-perl).
+Refer to the [Perl section of the MXNet Windows installation guide](/get_started/windows_setup.html#install-the-mxnet-package-for-perl).

docs/static_site/src/_includes/get_started/windows/python/cpu/build-from-source.md

@@ -1 +1 @@
-Refer to the [MXNet Windows installation guide](get_started/windows_setup.html)
+Refer to the [MXNet Windows installation guide](/get_started/windows_setup.html)

docs/static_site/src/_includes/get_started/windows/python/gpu/build-from-source.md

@@ -1 +1 @@
-To build from source, refer to the [MXNet Windows installation guide](get_started/windows_setup.html).
+To build from source, refer to the [MXNet Windows installation guide](/get_started/windows_setup.html).

docs/static_site/src/_includes/get_started/windows/r/cpu.md

@@ -1,7 +1,7 @@
 Note: packages for 3.6.x are not yet available.
 Install 3.5.x of R from [CRAN](https://cran.r-project.org/bin/windows/base/old/).
 
-You can [build MXNet-R from source](get_started/windows_setup.html#install-mxnet-package-for-r), or
+You can [build MXNet-R from source](/get_started/windows_setup.html#install-mxnet-package-for-r), or
 you can use a
 pre-built binary:
 

docs/static_site/src/_includes/get_started/windows/r/gpu.md

@@ -1,4 +1,4 @@
-You can [build MXNet-R from source](get_started/windows_setup.html#install-mxnet-package-for-r), or
+You can [build MXNet-R from source](/get_started/windows_setup.html#install-mxnet-package-for-r), or
 you can use a
 pre-built binary:
 

docs/static_site/src/pages/api/r/docs/tutorials/callback_function.md

@@ -272,7 +272,7 @@ Yes! You can stop the training early with `return(FALSE)`. See the following exa
 When the validation metric dips below the threshold we set, the training process stops.
 
 ## Next Steps
-* [Neural Networks with MXNet in Five Minutes](https://mxnet.io/tutorials/r/fiveMinutesNeuralNetwork.html)
-* [Classify Real-World Images with a Pretrained Model](https://mxnet.io/tutorials/r/classifyRealImageWithPretrainedModel.html)
-* [Handwritten Digits Classification Competition](https://mxnet.io/tutorials/r/mnistCompetition.html)
-* [Character Language Model Using RNN](https://mxnet.io/tutorials/r/charRnnModel.html)
+* [Neural Networks with MXNet in Five Minutes](/api/r/docs/tutorials/five_minutes_neural_network)
+* [Classify Real-World Images with a Pretrained Model](/api/r/docs/tutorials/classify_real_image_with_pretrained_model)
+* [Handwritten Digits Classification Competition](/api/r/docs/tutorials/mnist_competition)
+* [Character Language Model Using RNN](/api/r/docs/tutorials/char_rnn_model)

docs/static_site/src/pages/api/r/docs/tutorials/custom_loss_function.md

@@ -225,7 +225,7 @@ sum(abs(test.y - pred6[1,])) / length(test.y)
 
 
 ## Next Steps
-* [Neural Networks with MXNet in Five Minutes](https://mxnet.io/tutorials/r/fiveMinutesNeuralNetwork.html)
-* [Classify Real-World Images with a PreTrained Model](https://mxnet.io/tutorials/r/classifyRealImageWithPretrainedModel.html)
-* [Handwritten Digits Classification Competition](https://mxnet.io/tutorials/r/mnistCompetition.html)
-* [Character Language Model Using RNN](https://mxnet.io/tutorials/r/charRnnModel.html)
+* [Neural Networks with MXNet in Five Minutes](/api/r/docs/tutorials/five_minutes_neural_network)
+* [Classify Real-World Images with a PreTrained Model](/api/r/docs/tutorials/classify_real_image_with_pretrained_model)
+* [Handwritten Digits Classification Competition](/api/r/docs/tutorials/mnist_competition)
+* [Character Language Model Using RNN](/api/r/docs/tutorials/char_rnn_model)

docs/static_site/src/pages/api/r/docs/tutorials/multi_dim_lstm.md

@@ -55,7 +55,7 @@ PM2.5 concentration levels.
 
 Load and pre-process the data
 ---------
-The first step is to load in the data and preprocess it. It is assumed that the data has been downloaded in a .csv file: data.csv from the [pollution dataset](https://archive.ics.uci.edu/ml/datasets/Beijing+PM2.5+Data)
+The first step is to load in the data and preprocess it. It is assumed that the data has been downloaded in a .csv file: data.csv from the [pollution dataset](https://archive.ics.uci.edu/ml/datasets/Beijing+PM2.5+Data).
 
  ```r
 ## Loading required packages
@@ -324,4 +324,4 @@ We also repeated the above experiments to generate the next 100 samples to 301st
 
 The above tutorial is just for demonstration purposes and has not been tuned extensively for accuracy.
 
-For more tutorials on MXNet-R, head on to [MXNet-R tutorials](https://mxnet.apache.org/tutorials/r/index.html)
+For more tutorials on MXNet-R, head on to [MXNet-R tutorials](/api/r/docs/tutorials)

docs/static_site/src/pages/api/r/docs/tutorials/ndarray.md

@@ -223,9 +223,9 @@ The actual computations are finished, allowing us to copy the results someplace
 the results.
 
 ## Next Steps
-* [Symbol](https://mxnet.io/tutorials/r/symbol.html)
-* [Write and use callback functions](https://mxnet.io/tutorials/r/CallbackFunction.html)
-* [Neural Networks with MXNet in Five Minutes](https://mxnet.io/tutorials/r/fiveMinutesNeuralNetwork.html)
-* [Classify Real-World Images with Pre-trained Model](https://mxnet.io/tutorials/r/classifyRealImageWithPretrainedModel.html)
-* [Handwritten Digits Classification Competition](https://mxnet.io/tutorials/r/mnistCompetition.html)
-* [Character Language Model using RNN](https://mxnet.io/tutorials/r/charRnnModel.html)
+* [Symbol](/api/r/docs/tutorials/symbol)
+* [Write and use callback functions](/api/r/docs/tutorials/callback_function)
+* [Neural Networks with MXNet in Five Minutes](/api/r/docs/tutorials/five_minutes_neural_network)
+* [Classify Real-World Images with Pre-trained Model](/api/r/docs/tutorials/classify_real_image_with_pretrained_model)
+* [Handwritten Digits Classification Competition](/api/r/docs/tutorials/mnist_competition)
+* [Character Language Model using RNN](/api/r/docs/tutorials/char_rnn_model)

docs/static_site/src/pages/api/r/docs/tutorials/symbol.md

@@ -130,7 +130,7 @@ In the example, *net* is used as a function to apply to an existing symbol
 
 The [model API](https://github.com/apache/incubator-mxnet/blob/master/R-package/R/model.R) is a thin wrapper around the symbolic executors to support neural net training.
 
-We encourage you to read [Symbolic Configuration and Execution in Pictures for python package](../../api/python/symbol_in_pictures/symbol_in_pictures.md)for a detailed explanation of concepts in pictures.
+We encourage you to read [Symbolic Configuration and Execution in Pictures for python package](/api/python/symbol_in_pictures/symbol_in_pictures.md)for a detailed explanation of concepts in pictures.
 
 ## How Efficient Is the Symbolic API?
 
@@ -147,8 +147,8 @@ be more memory efficient than CXXNet and gets to the same runtime with
 greater flexibility.
 
 ## Next Steps
-* [Write and use callback functions](https://mxnet.io/tutorials/r/CallbackFunction.html)
-* [Neural Networks with MXNet in Five Minutes](https://mxnet.io/tutorials/r/fiveMinutesNeuralNetwork.html)
-* [Classify Real-World Images with Pre-trained Model](https://mxnet.io/tutorials/r/classifyRealImageWithPretrainedModel.html)
-* [Handwritten Digits Classification Competition](https://mxnet.io/tutorials/r/mnistCompetition.html)
-* [Character Language Model using RNN](https://mxnet.io/tutorials/r/charRnnModel.html)
+* [Write and use callback functions](/api/r/docs/tutorials/callback_function)
+* [Neural Networks with MXNet in Five Minutes](/api/r/docs/tutorials/five_minutes_neural_network)
+* [Classify Real-World Images with Pre-trained Model](/api/r/docs/tutorials/classify_real_image_with_pretrained_model)
+* [Handwritten Digits Classification Competition](/api/r/docs/tutorials/mnist_competition)
+* [Character Language Model using RNN](/api/r/docs/tutorials/char_rnn_model)

docs/static_site/src/pages/get_started/index.html

@@ -28,6 +28,6 @@
 <div class="get-started-from-source">
 <div class="wrapper">
     <h2>Download from source</h2>
-    <p>The signed source code for Apache MXNet (incubating) is available for download <a href="get_started/download">here</a></p>
+    <p>The signed source code for Apache MXNet (incubating) is available for download <a href="/get_started/download">here</a></p>
 </div>
 </div>

julia/docs/src/tutorial/mnist.md

@@ -23,7 +23,7 @@ multi-layer perceptron and then a convolutional neural network (the
 LeNet architecture) on the [MNIST handwritten digit
 dataset](http://yann.lecun.com/exdb/mnist/). The code for this tutorial
 could be found in
-[examples/mnist](https://github.com/apache/incubator-mxnet/blob/master/julia/docs/src/tutorial/mnist.md).  There are also two Jupyter notebooks that expand a little more on the [MLP](https://github.com/ultradian/julia_notebooks/blob/master/mxnet/mnistMLP.ipynb) and the [LeNet](https://github.com/ultradian/julia_notebooks/blob/master/mxnet/mnistLenet.ipynb), using the more general `ArrayDataProvider`. 
+[examples/mnist](/api/julia/docs/api/tutorial/mnist/).  There are also two Jupyter notebooks that expand a little more on the [MLP](https://github.com/ultradian/julia_notebooks/blob/master/mxnet/mnistMLP.ipynb) and the [LeNet](https://github.com/ultradian/julia_notebooks/blob/master/mxnet/mnistLenet.ipynb), using the more general `ArrayDataProvider`. 
 
 Simple 3-layer MLP
 ------------------