diff --git a/docs/api/python/ndarray/sparse.md b/docs/api/python/ndarray/sparse.md
index 5c8db0c9c195..9fca3c1a380e 100644
--- a/docs/api/python/ndarray/sparse.md
+++ b/docs/api/python/ndarray/sparse.md
@@ -38,7 +38,7 @@ array([0, 1, 1, 3])
 ```
 
 A detailed tutorial is available at
-[CSRNDArray - NDArray in Compressed Sparse Row Storage Format](https:https://mxnet.incubator.apache.org/versions/master/tutorials/sparse/csr.html).
+[CSRNDArray - NDArray in Compressed Sparse Row Storage Format](https://mxnet.incubator.apache.org/versions/master/tutorials/sparse/csr.html).
 <br>
 
 An `RowSparseNDArray` inherits from `NDArray`, and represents a multi-dimensional, fixed-size array in row sparse format.
diff --git a/docs/community/index.md b/docs/community/index.md
index 345aaad582a9..6d3f345dc973 100644
--- a/docs/community/index.md
+++ b/docs/community/index.md
@@ -8,9 +8,9 @@ We track bugs and new feature requests in the MXNet Github repo in the issues fo
 ## Contributors
 MXNet has been developed and is used by a group of active community members. Contribute to improving it! For more information, see [contributions](http://mxnet.io/community/contribute.html).
 
-Please join the contributor mailing list. [subscribe](mailto:dev-subscribe@mxnet.incubator.apache.org) [archive](https://lists.apache.org/list.html?dev@mxnet.apache.org)
+Please join the contributor mailing list. [subscribe]('mailto:dev-subscribe@mxnet.incubator.apache.org') [archive](https://lists.apache.org/list.html?dev@mxnet.apache.org)
 
-To join the MXNet slack channel send request to the contributor mailing list. [subscribe](mailto:dev@mxnet.incubator.apache.org) [archive](https://the-asf.slackarchive.io/mxnet)
+To join the MXNet slack channel send request to the contributor mailing list. [subscribe]('mailto:dev@mxnet.incubator.apache.org') [archive](https://the-asf.slackarchive.io/mxnet)
 
 ## Roadmap
 
diff --git a/docs/how_to/add_op_in_backend.md b/docs/faq/add_op_in_backend.md
similarity index 100%
rename from docs/how_to/add_op_in_backend.md
rename to docs/faq/add_op_in_backend.md
diff --git a/docs/faq/bucketing.md b/docs/faq/bucketing.md
index bc59c97ee54e..6bcf80fea272 100644
--- a/docs/faq/bucketing.md
+++ b/docs/faq/bucketing.md
@@ -84,7 +84,7 @@ and then randomly chooses sequences from that bucket to form a mini-batch.
 It also applies padding for sequences of different length within the mini-batch as necessary.
 
 For a full, working implementation of a `DataIter`
-that reads text sequences by as described above, see [example/rnn/lstm_ptb_bucketing.py](https://github.com/dmlc/mxnet/blob/master/example/rnn/lstm_bucketing.py).
+that reads text sequences by as described above, see [example/rnn/lstm_ptb_bucketing.py](https://github.com/dmlc/mxnet/blob/master/example/rnn/bucketing/lstm_bucketing.py).
 In this example, you can use bucketing with a static configuration (e.g., `buckets = [10, 20, 30, 40, 50, 60]`), or let MXNet generate buckets automatically according to the characteristics of the dataset (`buckets = []`). The latter approach is implemented by adding a bucket as long as the number of sequences assigned to that bucket is exceeds some minimum count. For more information, see [default_gen_buckets()](https://github.com/dmlc/mxnet/blob/master/example/rnn/old/bucket_io.py#L43).
 
 ## Beyond Sequence Training
diff --git a/docs/faq/index.md b/docs/faq/index.md
index e5807f42fc15..60b75ec8b05c 100644
--- a/docs/faq/index.md
+++ b/docs/faq/index.md
@@ -7,29 +7,29 @@ and full working examples, visit the [tutorials section](../tutorials/index.md).
 
 
 ## Modeling
-* [How do I fine-tune pre-trained models to a new dataset?](http://mxnet.io/how_to/finetune.html)
+* [How do I fine-tune pre-trained models to a new dataset?](http://mxnet.io/faq/finetune.html)
 
-* [How do I work with variable-length input in MXNet (bucketing)?](http://mxnet.io/how_to/bucketing.html)
+* [How do I work with variable-length input in MXNet (bucketing)?](http://mxnet.io/faq/bucketing.html)
 
-* [How do I visualize neural networks as computation graphs?](http://mxnet.io/how_to/visualize_graph.html)
+* [How do I visualize neural networks as computation graphs?](http://mxnet.io/faq/visualize_graph.html)
 
 
 ## Scale
-* [How can I train with multiple CPU/GPUs with data parallelism?](http://mxnet.io/how_to/multi_devices.html)
+* [How can I train with multiple CPU/GPUs with data parallelism?](http://mxnet.io/faq/multi_devices.html)
 
-* [How can I train with multiple GPUs with model parallelism?](http://mxnet.io/how_to/model_parallel_lstm.html)
+* [How can I train with multiple GPUs with model parallelism?](http://mxnet.io/faq/model_parallel_lstm.html)
 
 
 ## Speed
-* [How do I use gradient compression with distributed training?](http://mxnet.io/how_to/gradient_compression.html)
+* [How do I use gradient compression with distributed training?](http://mxnet.io/faq/gradient_compression.html)
 
-* [Can I use nnpack to improve the CPU performance of MXNet?](http://mxnet.io/how_to/nnpack.html)
+* [Can I use nnpack to improve the CPU performance of MXNet?](http://mxnet.io/faq/nnpack.html)
 
-* [What are the best setup and data-handling tips and tricks for improving speed?](http://mxnet.io/how_to/perf.html)
+* [What are the best setup and data-handling tips and tricks for improving speed?](http://mxnet.io/faq/perf.html)
 
 
 ## Deployment Environments
-* [Can I run MXNet on smart or mobile devices?](http://mxnet.io/how_to/smart_device.html)
+* [Can I run MXNet on smart or mobile devices?](http://mxnet.io/faq/smart_device.html)
 
 * [How to use data from S3 for training?](s3_integration.md)
 
@@ -44,7 +44,7 @@ and full working examples, visit the [tutorials section](../tutorials/index.md).
 * [How to convert MXNet models to Apple CoreML format?](https://github.com/apache/incubator-mxnet/tree/master/tools/coreml)
 
 ## Security
-* [How to run MXNet securely?](http://mxnet.io/how_to/security.html)
+* [How to run MXNet securely?](http://mxnet.io/faq/security.html)
 
 ## Extend and Contribute to MXNet
 
@@ -52,11 +52,11 @@ and full working examples, visit the [tutorials section](../tutorials/index.md).
 
 * [How do I contribute a patch to MXNet?](http://mxnet.io/community/contribute.html)
 
-* [How do I implement operators in MXNet backend?](http://mxnet.io/how_to/add_op_in_backend.html)
+* [How do I implement operators in MXNet backend?](http://mxnet.io/faq/add_op_in_backend.html)
 
-* [How do I create new operators in MXNet?](http://mxnet.io/how_to/new_op.html)
+* [How do I create new operators in MXNet?](http://mxnet.io/faq/new_op.html)
 
-* [How do I set MXNet's environmental variables?](http://mxnet.io/how_to/env_var.html)
+* [How do I set MXNet's environmental variables?](http://mxnet.io/faq/env_var.html)
 
 ## Questions about Using MXNet
 If you need help with using MXNet, have questions about applying it to a particular kind of problem, or have a discussion topic, please use our [forum](https://discuss.mxnet.io).
diff --git a/docs/how_to/security.md b/docs/faq/security.md
similarity index 99%
rename from docs/how_to/security.md
rename to docs/faq/security.md
index 6f64a9e608f7..09fa22b7e379 100644
--- a/docs/how_to/security.md
+++ b/docs/faq/security.md
@@ -1,4 +1,4 @@
-# MXNet Security best practices
+# MXNet Security Best Practices
 
 MXNet framework has no built-in security protections. It assumes that the MXNet entities involved in model training and inferencing (hosting) are fully trusted. It also assumes that their communications cannot be eavesdropped or tampered with. MXNet consumers shall ensure that the above assumptions are met.
 
diff --git a/docs/tutorials/unsupervised_learning/gan.md b/docs/tutorials/unsupervised_learning/gan.md
index 71774bc98919..c7b879c174b2 100644
--- a/docs/tutorials/unsupervised_learning/gan.md
+++ b/docs/tutorials/unsupervised_learning/gan.md
@@ -385,5 +385,5 @@ As a result, we have created two neural nets: a Generator, which is able to crea
 Along the way, we have learned how to do the image manipulation and visualization that is associated with the training of deep neural nets. We have also learned how to use MXNet's Module APIs to perform advanced model training functionality to fit the model.
 
 ## Acknowledgements
-This tutorial is based on [MXNet DCGAN codebase](https://github.com/apache/incubator-mxnet/blob/master/example/gan/dcgan.py),
+This tutorial is based on [MXNet DCGAN codebase](https://github.com/apache/incubator-mxnet/blob/master/example/gluon/dcgan.py),
 [The original paper on GANs](https://arxiv.org/abs/1406.2661), as well as [this paper on deep convolutional GANs](https://arxiv.org/abs/1511.06434).