diff --git a/docs/error/404.md b/docs/error/404.md
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# Page Does Not Exist
If you're here that means you requested a page that doesn't exist. Sorry about that! Maybe try the search box to find what you're looking for, or navigate to the [Home Page](../index.html). Also, make sure you're looking in the correct version, as some features may only be available in [newer versions](/~https://github.com/apache/incubator-mxnet/releases) or the [master branch](../versions/master).
diff --git a/docs/error/api.md b/docs/error/api.md
index 37de50291abd..ee7f359c9f36 100644
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# API Not Available
You selected an API that is not available for this version of MXNet. Try a more recent version of MXNet, or go to the [master](../versions/master/) version.
diff --git a/docs/faq/add_op_in_backend.md b/docs/faq/add_op_in_backend.md
index ed906da27377..ba7e3db0160f 100644
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# A Beginner's Guide to Implementing Operators in MXNet Backend
## Introduction
diff --git a/docs/faq/bucketing.md b/docs/faq/bucketing.md
index dbfdedde2acf..b5fb987d23e4 100644
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# Bucketing in MXNet
When we train recurrent neural networks (RNNs), we _unroll_ the network in time.
For a single example of length T, we would unroll the network T steps.
diff --git a/docs/faq/caffe.md b/docs/faq/caffe.md
index a9bab3cdb549..e0362ca2776b 100644
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# How to | Convert from Caffe to MXNet
Key topics covered include the following:
diff --git a/docs/faq/cloud.md b/docs/faq/cloud.md
index 67b28f8b4338..ecc0f46e02c0 100644
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# MXNet on the Cloud
Deep learning can require extremely powerful hardware, often for unpredictable durations of time.
diff --git a/docs/faq/develop_and_hack.md b/docs/faq/develop_and_hack.md
index 8b7dd672eea9..e6faa0aed953 100644
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# Develop and Hack MXNet
- [Create new operators](new_op.md)
- [Use Torch from MXNet](torch.md)
diff --git a/docs/faq/distributed_training.md b/docs/faq/distributed_training.md
index 8d8666ff066c..4d4a220c5661 100644
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# Distributed Training in MXNet
MXNet supports distributed training enabling us to leverage multiple machines for faster training.
In this document, we describe how it works, how to launch a distributed training job and
diff --git a/docs/faq/env_var.md b/docs/faq/env_var.md
index 98057d0d76d6..ab7c6f3308e9 100644
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Environment Variables
=====================
MXNet has several settings that you can change with environment variables.
diff --git a/docs/faq/faq.md b/docs/faq/faq.md
index 668587ec6888..b7866be5c03a 100644
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# Frequently Asked Questions
This topic provides answers to the frequently asked questions on [mxnet/issues](/~https://github.com/dmlc/mxnet/issues). Before posting an issue, please check this page. If you would like to contribute to this page, please make the questions and answers simple. If your answer is extremely detailed, please post it elsewhere and link to it.
diff --git a/docs/faq/finetune.md b/docs/faq/finetune.md
index 04244d15b0b9..4a03eefa05c9 100644
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# Fine-tune with Pretrained Models
diff --git a/docs/faq/float16.md b/docs/faq/float16.md
index b4cd97b30e5c..323218ce7df6 100644
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# Mixed precision training using float16
In this tutorial you will walk through how one can train deep learning neural networks with mixed precision on supported hardware. You will first see how to use float16 (both with Gluon and Symbolic APIs) and then some techniques on achieving good performance and accuracy.
diff --git a/docs/faq/gradient_compression.md b/docs/faq/gradient_compression.md
index e2dbd3271d8d..9e926c52feca 100644
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# Gradient Compression
Gradient Compression reduces communication bandwidth, and in some scenarios, it can make training more scalable and efficient without significant loss in convergence rate or accuracy. Example implementations with GPUs, CPUs, and distributed training are provided in this document.
diff --git a/docs/faq/index.md b/docs/faq/index.md
index 1b4a95d3f331..e49c4d91a9d2 100644
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# MXNet FAQ
```eval_rst
diff --git a/docs/faq/model_parallel_lstm.md b/docs/faq/model_parallel_lstm.md
index b78b2c574dcc..e9a8768b6fec 100644
--- a/docs/faq/model_parallel_lstm.md
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# Training with Multiple GPUs Using Model Parallelism
Training deep learning models can be resource intensive.
Even with a powerful GPU, some models can take days or weeks to train.
diff --git a/docs/faq/multi_devices.md b/docs/faq/multi_devices.md
index a43879cb5233..60a8003f75d0 100644
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# Run MXNet on Multiple CPU/GPUs with Data Parallelism
_MXNet_ supports training with multiple CPUs and GPUs, which may be located on different physical machines.
diff --git a/docs/faq/new_op.md b/docs/faq/new_op.md
index 994a2a6f823e..4d51eaf8059d 100644
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# How to Create New Operators (Layers)
This tutorials walks you through the process of creating new MXNet operators (or layers).
diff --git a/docs/faq/nnpack.md b/docs/faq/nnpack.md
index ed38cb07df7e..c0b27b9bf4e6 100644
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### NNPACK for Multi-Core CPU Support in MXNet
[NNPACK](/~https://github.com/Maratyszcza/NNPACK) is an acceleration package
for neural network computations, which can run on x86-64, ARMv7, or ARM64 architecture CPUs.
diff --git a/docs/faq/perf.md b/docs/faq/perf.md
index f116ede11d56..00310dfbb5bd 100644
--- a/docs/faq/perf.md
+++ b/docs/faq/perf.md
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# Some Tips for Improving MXNet Performance
Even after fixing the training or deployment environment and parallelization scheme,
a number of configuration settings and data-handling choices can impact the _MXNet_ performance.
diff --git a/docs/faq/recordio.md b/docs/faq/recordio.md
index 3091052ef6f3..7367b524224d 100644
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## Create a Dataset Using RecordIO
RecordIO implements a file format for a sequence of records. We recommend storing images as records and packing them together. The benefits include:
diff --git a/docs/faq/s3_integration.md b/docs/faq/s3_integration.md
index 024356706339..49520b5814e1 100644
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# Use data from S3 for training
AWS S3 is a cloud-based object storage service that allows storage and retrieval of large amounts of data at a very low cost. This makes it an attractive option to store large training datasets. MXNet is deeply integrated with S3 for this purpose.
diff --git a/docs/faq/security.md b/docs/faq/security.md
index 0615acda3435..88d48be35ecb 100644
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# 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/faq/smart_device.md b/docs/faq/smart_device.md
index 2584b4c36caf..c56457a626cb 100644
--- a/docs/faq/smart_device.md
+++ b/docs/faq/smart_device.md
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# Deep Learning in a Single File for Smart Devices
Deep learning (DL) systems are complex and often depend on a number of libraries.
diff --git a/docs/faq/visualize_graph.md b/docs/faq/visualize_graph.md
index 06010213242c..adeb0e49136f 100644
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+++ b/docs/faq/visualize_graph.md
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# How to visualize Neural Networks as computation graph
Here, we'll demonstrate how to use ```mx.viz.plot_network```
diff --git a/docs/faq/why_mxnet.md b/docs/faq/why_mxnet.md
old mode 100755
new mode 100644
index ed8cef143070..140e853e2692
--- a/docs/faq/why_mxnet.md
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# Why MXNet?
Probably, if you've stumbled upon this page, you've heard of _deep learning_.
diff --git a/docs/gluon/index.md b/docs/gluon/index.md
index 4f6d3c10f38c..0f09699d122e 100644
--- a/docs/gluon/index.md
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# About Gluon
diff --git a/docs/index.md b/docs/index.md
index ab6a95dc0ddd..e7f314eb169b 100644
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# MXNet
```eval_rst
diff --git a/docs/install/amazonlinux_setup.md b/docs/install/amazonlinux_setup.md
index 42a4fcb0eb89..a432d4815662 100644
--- a/docs/install/amazonlinux_setup.md
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diff --git a/docs/install/build_from_source.md b/docs/install/build_from_source.md
index e41b1d0f1804..0ab8b5c8f8f1 100644
--- a/docs/install/build_from_source.md
+++ b/docs/install/build_from_source.md
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# Build MXNet from Source
This document explains how to build MXNet from source code.
diff --git a/docs/install/c_plus_plus.md b/docs/install/c_plus_plus.md
index 6078877c27c8..a24deff01354 100644
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## Build the C++ package
The C++ package has the same prerequisites as the MXNet library.
diff --git a/docs/install/centos_setup.md b/docs/install/centos_setup.md
index e5efe42a61dd..e01b68221bd9 100644
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# Installing MXNet on CentOS and other non-Ubuntu Linux systems
Step 1. Install build tools and git on `CentOS >= 7` and `Fedora >= 19`:
diff --git a/docs/install/download.md b/docs/install/download.md
index d7b9440e0a98..725cf5eb72db 100644
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# Source Download
These source archives are generated from tagged releases. Updates and patches will not have been applied. For any updates refer to the corresponding branches in the [GitHub repository](/~https://github.com/apache/incubator-mxnet). Choose your flavor of download from the following links:
diff --git a/docs/install/index.md b/docs/install/index.md
index 5067c5df4475..23df67dd3dad 100644
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# Installing MXNet
```eval_rst
diff --git a/docs/install/java_setup.md b/docs/install/java_setup.md
index fe55d074e754..8d5f872ecebf 100644
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# Setup the MXNet Package for Java
The following instructions are provided for macOS and Ubuntu. Windows is not yet available.
diff --git a/docs/install/osx_setup.md b/docs/install/osx_setup.md
index 4e9293efce93..d9437ab11b2d 100644
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# Installing MXNet from source on OS X (Mac)
**NOTE:** For prebuild MXNet with Python installation, please refer to the [new install guide](http://mxnet.io/install/index.html).
diff --git a/docs/install/raspbian_setup.md b/docs/install/raspbian_setup.md
index 42a4fcb0eb89..a432d4815662 100644
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diff --git a/docs/install/scala_setup.md b/docs/install/scala_setup.md
index 0dadd8bca400..d5d58cedcaa4 100644
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+++ b/docs/install/scala_setup.md
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# Setup the MXNet Package for Scala
The following instructions are provided for macOS and Ubuntu. Windows is not yet available.
diff --git a/docs/install/tx2_setup.md b/docs/install/tx2_setup.md
index 42a4fcb0eb89..a432d4815662 100644
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diff --git a/docs/install/ubuntu_setup.md b/docs/install/ubuntu_setup.md
index 9961c706af1d..df42e58ee792 100644
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# Installing MXNet on Ubuntu
The following installation instructions are for installing MXNet on computers running **Ubuntu 16.04**. Support for later versions of Ubuntu is [not yet available](#contributions).
diff --git a/docs/install/validate_mxnet.md b/docs/install/validate_mxnet.md
index dfe8d063f602..c31463b63eee 100644
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# Validate Your MXNet Installation
- [Python](#python)
diff --git a/docs/install/windows_setup.md b/docs/install/windows_setup.md
old mode 100755
new mode 100644
index b34936140aea..3c3da5349235
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# Installing MXNet on Windows
The following describes how to install with pip for computers with CPUs, Intel CPUs, and NVIDIA GPUs. Further along in the document you can learn how to build MXNet from source on Windows, or how to install packages that support different language APIs to MXNet.
diff --git a/docs/model_zoo/index.md b/docs/model_zoo/index.md
index 034d360b985a..cfe13caa0bf0 100644
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# MXNet Model Zoo
MXNet features fast implementations of many state-of-the-art models reported in the academic literature. This Model Zoo is an
diff --git a/docs/settings.ini b/docs/settings.ini
index 7de3268ab145..547ffa0c82f3 100644
--- a/docs/settings.ini
+++ b/docs/settings.ini
@@ -1,3 +1,20 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
[mxnet]
build_mxnet = 0
diff --git a/docs/tutorials/basic/data.md b/docs/tutorials/basic/data.md
index 4a682e83f9fe..00c87d0c37c5 100644
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# Iterators - Loading data
In this tutorial, we focus on how to feed data into a training or inference program.
Most training and inference modules in MXNet accept data iterators,
diff --git a/docs/tutorials/basic/index.md b/docs/tutorials/basic/index.md
index 87d72894424f..faf6526fb824 100644
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+
# Tutorials
```eval_rst
diff --git a/docs/tutorials/basic/module.md b/docs/tutorials/basic/module.md
index f7a4d6e25de7..64a1e9482ea8 100644
--- a/docs/tutorials/basic/module.md
+++ b/docs/tutorials/basic/module.md
@@ -1,3 +1,20 @@
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# Module - Neural network training and inference
diff --git a/docs/tutorials/basic/ndarray.md b/docs/tutorials/basic/ndarray.md
index 2c171f2627e8..be5dbcf29e88 100644
--- a/docs/tutorials/basic/ndarray.md
+++ b/docs/tutorials/basic/ndarray.md
@@ -1,3 +1,20 @@
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# NDArray - Imperative tensor operations on CPU/GPU
In _MXNet_, `NDArray` is the core data structure for all mathematical
diff --git a/docs/tutorials/basic/ndarray_indexing.md b/docs/tutorials/basic/ndarray_indexing.md
index 35dd8c17f675..00a231351d7d 100644
--- a/docs/tutorials/basic/ndarray_indexing.md
+++ b/docs/tutorials/basic/ndarray_indexing.md
@@ -1,3 +1,20 @@
+
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# NDArray Indexing - Array indexing features
diff --git a/docs/tutorials/basic/symbol.md b/docs/tutorials/basic/symbol.md
index 5e1e3cd8c62f..cff12aca3c95 100644
--- a/docs/tutorials/basic/symbol.md
+++ b/docs/tutorials/basic/symbol.md
@@ -1,3 +1,20 @@
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# Symbol - Neural network graphs
In a [previous tutorial](http://mxnet.io/tutorials/basic/ndarray.html), we introduced `NDArray`,
diff --git a/docs/tutorials/c++/basics.md b/docs/tutorials/c++/basics.md
index aa73a7363b1c..ddc5595cf3c0 100644
--- a/docs/tutorials/c++/basics.md
+++ b/docs/tutorials/c++/basics.md
@@ -1,3 +1,20 @@
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Basics
======
diff --git a/docs/tutorials/c++/index.md b/docs/tutorials/c++/index.md
index 87d72894424f..faf6526fb824 100644
--- a/docs/tutorials/c++/index.md
+++ b/docs/tutorials/c++/index.md
@@ -1,3 +1,20 @@
+
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# Tutorials
```eval_rst
diff --git a/docs/tutorials/c++/subgraphAPI.md b/docs/tutorials/c++/subgraphAPI.md
index 0ae4341b287c..b834df8741b5 100644
--- a/docs/tutorials/c++/subgraphAPI.md
+++ b/docs/tutorials/c++/subgraphAPI.md
@@ -1,3 +1,20 @@
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## Subgraph API
The subgraph API has been proposed and implemented as the default mechanism for integrating backend libraries to MXNet. The subgraph API is a very flexible interface. Although it was proposed as an integration mechanism, it has been used as a tool for manipulating NNVM graphs for graph-level optimizations, such as operator fusion.
diff --git a/docs/tutorials/control_flow/ControlFlowTutorial.md b/docs/tutorials/control_flow/ControlFlowTutorial.md
index 4b6a23136b5d..88aa383cb011 100644
--- a/docs/tutorials/control_flow/ControlFlowTutorial.md
+++ b/docs/tutorials/control_flow/ControlFlowTutorial.md
@@ -1,3 +1,20 @@
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# Hybridize Gluon models with control flows.
MXNet currently provides three control flow operators: `cond`, `foreach` and `while_loop`. Like other MXNet operators, they all have a version for NDArray and a version for Symbol. These two versions have exactly the same semantics. We can take advantage of this and use them in Gluon to hybridize models.
diff --git a/docs/tutorials/control_flow/index.md b/docs/tutorials/control_flow/index.md
index 87d72894424f..faf6526fb824 100644
--- a/docs/tutorials/control_flow/index.md
+++ b/docs/tutorials/control_flow/index.md
@@ -1,3 +1,20 @@
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# Tutorials
```eval_rst
diff --git a/docs/tutorials/embedded/index.md b/docs/tutorials/embedded/index.md
index 87d72894424f..faf6526fb824 100644
--- a/docs/tutorials/embedded/index.md
+++ b/docs/tutorials/embedded/index.md
@@ -1,3 +1,20 @@
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# Tutorials
```eval_rst
diff --git a/docs/tutorials/embedded/wine_detector.md b/docs/tutorials/embedded/wine_detector.md
index 65e6fbaa4d91..f0ae8273203e 100644
--- a/docs/tutorials/embedded/wine_detector.md
+++ b/docs/tutorials/embedded/wine_detector.md
@@ -1,3 +1,20 @@
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# Real-time Object Detection with MXNet On The Raspberry Pi
This tutorial shows developers who work with the Raspberry Pi or similar embedded ARM-based devices how to compile MXNet for those devices and run a pretrained deep network model. It also shows how to use AWS IoT to manage and monitor MXNet models running on your devices.
diff --git a/docs/tutorials/gluon/autograd.md b/docs/tutorials/gluon/autograd.md
index 4b296dd2dd5b..4d8a042c9b03 100644
--- a/docs/tutorials/gluon/autograd.md
+++ b/docs/tutorials/gluon/autograd.md
@@ -1,3 +1,20 @@
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# Automatic differentiation
MXNet supports automatic differentiation with the `autograd` package.
diff --git a/docs/tutorials/gluon/custom_layer.md b/docs/tutorials/gluon/custom_layer.md
index 97bdf05aff58..40ba0823a9d5 100644
--- a/docs/tutorials/gluon/custom_layer.md
+++ b/docs/tutorials/gluon/custom_layer.md
@@ -1,3 +1,20 @@
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# How to write a custom layer in Apache MxNet Gluon API
diff --git a/docs/tutorials/gluon/customop.md b/docs/tutorials/gluon/customop.md
index df10788d6788..eae0344c8702 100644
--- a/docs/tutorials/gluon/customop.md
+++ b/docs/tutorials/gluon/customop.md
@@ -1,3 +1,20 @@
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# Creating custom operators with numpy
diff --git a/docs/tutorials/gluon/data_augmentation.md b/docs/tutorials/gluon/data_augmentation.md
index df7462c33044..356d335a8ca2 100644
--- a/docs/tutorials/gluon/data_augmentation.md
+++ b/docs/tutorials/gluon/data_augmentation.md
@@ -1,3 +1,20 @@
+
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# Methods of applying data augmentation (Gluon API)
Data Augmentation is a regularization technique that's used to avoid overfitting when training Machine Learning models. Although the technique can be applied in a variety of domains, it's very common in Computer Vision. Adjustments are made to the original images in the training dataset before being used in training. Some example adjustments include translating, cropping, scaling, rotating, changing brightness and contrast. We do this to reduce the dependence of the model on spurious characteristics; e.g. training data may only contain faces that fill 1/4 of the image, so the model trained without data augmentation might unhelpfully learn that faces can only be of this size.
diff --git a/docs/tutorials/gluon/datasets.md b/docs/tutorials/gluon/datasets.md
index 0b0038def633..c029124af8b6 100644
--- a/docs/tutorials/gluon/datasets.md
+++ b/docs/tutorials/gluon/datasets.md
@@ -1,3 +1,20 @@
+
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# Gluon `Dataset`s and `DataLoader`
diff --git a/docs/tutorials/gluon/gluon.md b/docs/tutorials/gluon/gluon.md
index 518e99905c04..d9a6e93af689 100644
--- a/docs/tutorials/gluon/gluon.md
+++ b/docs/tutorials/gluon/gluon.md
@@ -1,3 +1,20 @@
+
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# Gluon - Neural network building blocks
Gluon package is a high-level interface for MXNet designed to be easy to use while
diff --git a/docs/tutorials/gluon/gotchas_numpy_in_mxnet.md b/docs/tutorials/gluon/gotchas_numpy_in_mxnet.md
index c82c63edbc2b..1c285a41d76b 100644
--- a/docs/tutorials/gluon/gotchas_numpy_in_mxnet.md
+++ b/docs/tutorials/gluon/gotchas_numpy_in_mxnet.md
@@ -1,3 +1,20 @@
+
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# Gotchas using NumPy in Apache MXNet
diff --git a/docs/tutorials/gluon/hybrid.md b/docs/tutorials/gluon/hybrid.md
index 6d64acdce275..db1cf64e4864 100644
--- a/docs/tutorials/gluon/hybrid.md
+++ b/docs/tutorials/gluon/hybrid.md
@@ -1,3 +1,20 @@
+
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# Hybrid - Faster training and easy deployment
*Note: a newer version is available [here](http://gluon.mxnet.io/chapter07_distributed-learning/hybridize.html).*
diff --git a/docs/tutorials/gluon/index.md b/docs/tutorials/gluon/index.md
index 87d72894424f..faf6526fb824 100644
--- a/docs/tutorials/gluon/index.md
+++ b/docs/tutorials/gluon/index.md
@@ -1,3 +1,20 @@
+
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# Tutorials
```eval_rst
diff --git a/docs/tutorials/gluon/info_gan.md b/docs/tutorials/gluon/info_gan.md
index 8b2668ab22e6..93fd6cb5e7fd 100644
--- a/docs/tutorials/gluon/info_gan.md
+++ b/docs/tutorials/gluon/info_gan.md
@@ -1,3 +1,20 @@
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# Image similarity search with InfoGAN
diff --git a/docs/tutorials/gluon/learning_rate_finder.md b/docs/tutorials/gluon/learning_rate_finder.md
index b571a53f674c..30c66e302766 100644
--- a/docs/tutorials/gluon/learning_rate_finder.md
+++ b/docs/tutorials/gluon/learning_rate_finder.md
@@ -1,3 +1,20 @@
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# Learning Rate Finder
diff --git a/docs/tutorials/gluon/learning_rate_schedules.md b/docs/tutorials/gluon/learning_rate_schedules.md
index 88b109e7f33e..46c79ebc249b 100644
--- a/docs/tutorials/gluon/learning_rate_schedules.md
+++ b/docs/tutorials/gluon/learning_rate_schedules.md
@@ -1,3 +1,20 @@
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# Learning Rate Schedules
diff --git a/docs/tutorials/gluon/learning_rate_schedules_advanced.md b/docs/tutorials/gluon/learning_rate_schedules_advanced.md
index bdaf0a9ba38d..287f70d535e8 100644
--- a/docs/tutorials/gluon/learning_rate_schedules_advanced.md
+++ b/docs/tutorials/gluon/learning_rate_schedules_advanced.md
@@ -1,3 +1,20 @@
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# Advanced Learning Rate Schedules
diff --git a/docs/tutorials/gluon/logistic_regression_explained.md b/docs/tutorials/gluon/logistic_regression_explained.md
index 577a91413b33..57ed56eb8f4f 100644
--- a/docs/tutorials/gluon/logistic_regression_explained.md
+++ b/docs/tutorials/gluon/logistic_regression_explained.md
@@ -1,3 +1,20 @@
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# Logistic regression using Gluon API explained
diff --git a/docs/tutorials/gluon/mnist.md b/docs/tutorials/gluon/mnist.md
index 5b8a98a3d668..3758f561a573 100644
--- a/docs/tutorials/gluon/mnist.md
+++ b/docs/tutorials/gluon/mnist.md
@@ -1,4 +1,21 @@
-# Handwritten Digit Recognition
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+# Hand-written Digit Recognition
In this tutorial, we'll give you a step by step walk-through of how to build a hand-written digit classifier using the [MNIST](https://en.wikipedia.org/wiki/MNIST_database) dataset.
diff --git a/docs/tutorials/gluon/naming.md b/docs/tutorials/gluon/naming.md
index 3606a03dcbd2..e667ad3cb79e 100644
--- a/docs/tutorials/gluon/naming.md
+++ b/docs/tutorials/gluon/naming.md
@@ -1,3 +1,20 @@
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# Naming of Gluon Parameter and Blocks
diff --git a/docs/tutorials/gluon/ndarray.md b/docs/tutorials/gluon/ndarray.md
index 7cf08a88cbf3..f1503aec18db 100644
--- a/docs/tutorials/gluon/ndarray.md
+++ b/docs/tutorials/gluon/ndarray.md
@@ -1,3 +1,20 @@
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# NDArray - Scientific computing on CPU and GPU
NDArray is a tensor data structure similar to numpy's multi-dimensional array.
diff --git a/docs/tutorials/gluon/pretrained_models.md b/docs/tutorials/gluon/pretrained_models.md
index 0de5fdd0b44f..27ec1a95b8cd 100644
--- a/docs/tutorials/gluon/pretrained_models.md
+++ b/docs/tutorials/gluon/pretrained_models.md
@@ -1,3 +1,20 @@
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# Using pre-trained models in MXNet
diff --git a/docs/tutorials/gluon/save_load_params.md b/docs/tutorials/gluon/save_load_params.md
index ebc8103e7b45..ffebefdf80e1 100644
--- a/docs/tutorials/gluon/save_load_params.md
+++ b/docs/tutorials/gluon/save_load_params.md
@@ -1,3 +1,20 @@
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# Saving and Loading Gluon Models
Training large models take a lot of time and it is a good idea to save the trained models to files to avoid training them again and again. There are a number of reasons to do this. For example, you might want to do inference on a machine that is different from the one where the model was trained. Sometimes model's performance on validation set decreases towards the end of the training because of overfitting. If you saved your model parameters after every epoch, at the end you can decide to use the model that performs best on the validation set. Another reason would be to train your model using one language (like Python that has a lot of tools for training) and run inference using a different language (like Scala probably because your application is built on Scala).
diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md
index 52e2be8f6a2b..f8a9b64a6640 100644
--- a/docs/tutorials/index.md
+++ b/docs/tutorials/index.md
@@ -1,3 +1,20 @@
+
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# Tutorials
```eval_rst
diff --git a/docs/tutorials/java/index.md b/docs/tutorials/java/index.md
index 87d72894424f..faf6526fb824 100644
--- a/docs/tutorials/java/index.md
+++ b/docs/tutorials/java/index.md
@@ -1,3 +1,20 @@
+
+
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# Tutorials
```eval_rst
diff --git a/docs/tutorials/java/mxnet_java_on_intellij.md b/docs/tutorials/java/mxnet_java_on_intellij.md
index f4d4ea5ab839..cbe885e655a0 100644
--- a/docs/tutorials/java/mxnet_java_on_intellij.md
+++ b/docs/tutorials/java/mxnet_java_on_intellij.md
@@ -1,3 +1,20 @@
+
+
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# Run MXNet Java Examples Using the IntelliJ IDE (macOS)
This tutorial guides you through setting up a simple Java project in IntelliJ IDE on macOS and demonstrates usage of the MXNet Java APIs.
diff --git a/docs/tutorials/java/ssd_inference.md b/docs/tutorials/java/ssd_inference.md
index 3a20329f9a91..1117bbdcfa5b 100644
--- a/docs/tutorials/java/ssd_inference.md
+++ b/docs/tutorials/java/ssd_inference.md
@@ -1,3 +1,20 @@
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# Multi Object Detection using pre-trained SSD Model via Java Inference APIs
This tutorial shows how to use MXNet Java Inference APIs to run inference on a pre-trained Single Shot Detector (SSD) Model.
diff --git a/docs/tutorials/nlp/cnn.md b/docs/tutorials/nlp/cnn.md
index b3d7d0d38941..e671de3a1f57 100644
--- a/docs/tutorials/nlp/cnn.md
+++ b/docs/tutorials/nlp/cnn.md
@@ -1,3 +1,20 @@
+
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# Text Classification Using a Convolutional Neural Network on MXNet
This tutorial is based of Yoon Kim's [paper](https://arxiv.org/abs/1408.5882) on using convolutional neural networks for sentence sentiment classification. The tutorial has been tested on MXNet 1.0 running under Python 2.7 and Python 3.6.
diff --git a/docs/tutorials/nlp/index.md b/docs/tutorials/nlp/index.md
index 87d72894424f..faf6526fb824 100644
--- a/docs/tutorials/nlp/index.md
+++ b/docs/tutorials/nlp/index.md
@@ -1,3 +1,20 @@
+
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# Tutorials
```eval_rst
diff --git a/docs/tutorials/onnx/export_mxnet_to_onnx.md b/docs/tutorials/onnx/export_mxnet_to_onnx.md
index 3f925c7b5b84..c0f8d92901f6 100644
--- a/docs/tutorials/onnx/export_mxnet_to_onnx.md
+++ b/docs/tutorials/onnx/export_mxnet_to_onnx.md
@@ -1,3 +1,20 @@
+
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# Exporting MXNet model to ONNX format
diff --git a/docs/tutorials/onnx/fine_tuning_gluon.md b/docs/tutorials/onnx/fine_tuning_gluon.md
index 750a6757272f..dd0c0e93e862 100644
--- a/docs/tutorials/onnx/fine_tuning_gluon.md
+++ b/docs/tutorials/onnx/fine_tuning_gluon.md
@@ -1,3 +1,20 @@
+
+
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# Fine-tuning an ONNX model with MXNet/Gluon
diff --git a/docs/tutorials/onnx/index.md b/docs/tutorials/onnx/index.md
index 87d72894424f..faf6526fb824 100644
--- a/docs/tutorials/onnx/index.md
+++ b/docs/tutorials/onnx/index.md
@@ -1,3 +1,20 @@
+
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# Tutorials
```eval_rst
diff --git a/docs/tutorials/onnx/inference_on_onnx_model.md b/docs/tutorials/onnx/inference_on_onnx_model.md
index b2522ad0c1f1..f12e050fcc73 100644
--- a/docs/tutorials/onnx/inference_on_onnx_model.md
+++ b/docs/tutorials/onnx/inference_on_onnx_model.md
@@ -1,3 +1,20 @@
+
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# Running inference on MXNet/Gluon from an ONNX model
diff --git a/docs/tutorials/onnx/super_resolution.md b/docs/tutorials/onnx/super_resolution.md
index 36c06b743c8e..db5932949f14 100644
--- a/docs/tutorials/onnx/super_resolution.md
+++ b/docs/tutorials/onnx/super_resolution.md
@@ -1,3 +1,20 @@
+
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# Importing an ONNX model into MXNet
In this tutorial we will:
diff --git a/docs/tutorials/python/data_augmentation.md b/docs/tutorials/python/data_augmentation.md
index e4dbbb672997..45a4e3d8c86d 100644
--- a/docs/tutorials/python/data_augmentation.md
+++ b/docs/tutorials/python/data_augmentation.md
@@ -1,3 +1,20 @@
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# Methods of applying data augmentation (Module API)
Data Augmentation is a regularization technique that's used to avoid overfitting when training Machine Learning models. Although the technique can be applied in a variety of domains, it's very common in Computer Vision. Adjustments are made to the original images in the training dataset before being used in training. Some example adjustments include translating, cropping, scaling, rotating, changing brightness and contrast. We do this to reduce the dependence of the model on spurious characteristics; e.g. training data may only contain faces that fill 1/4 of the image, so the model trained without data augmentation might unhelpfully learn that faces can only be of this size.
diff --git a/docs/tutorials/python/data_augmentation_with_masks.md b/docs/tutorials/python/data_augmentation_with_masks.md
index ac587ac2f5e2..66beece720a9 100644
--- a/docs/tutorials/python/data_augmentation_with_masks.md
+++ b/docs/tutorials/python/data_augmentation_with_masks.md
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# Data Augmentation with Masks
diff --git a/docs/tutorials/python/index.md b/docs/tutorials/python/index.md
index 87d72894424f..faf6526fb824 100644
--- a/docs/tutorials/python/index.md
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# Tutorials
```eval_rst
diff --git a/docs/tutorials/python/kvstore.md b/docs/tutorials/python/kvstore.md
index 3e6bbf12c393..42debab9b83e 100644
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# Distributed Key-Value Store
KVStore is a place for data sharing. Think of it as a single object shared
diff --git a/docs/tutorials/python/linear-regression.md b/docs/tutorials/python/linear-regression.md
index fd336ad2aed5..47d1acae9a14 100644
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# Linear Regression
In this tutorial we'll walk through how one can implement *linear regression* using MXNet APIs.
diff --git a/docs/tutorials/python/matrix_factorization.md b/docs/tutorials/python/matrix_factorization.md
index 154fa4b3e127..cfe73a4856e0 100644
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# Matrix Factorization
In a recommendation system, there is a group of users and a set of items. Given
diff --git a/docs/tutorials/python/mnist.md b/docs/tutorials/python/mnist.md
index df949d487b63..9d641b36c202 100644
--- a/docs/tutorials/python/mnist.md
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# Handwritten Digit Recognition
In this tutorial, we'll give you a step by step walk-through of how to build a hand-written digit classifier using the [MNIST](https://en.wikipedia.org/wiki/MNIST_database) dataset. For someone new to deep learning, this exercise is arguably the "Hello World" equivalent.
diff --git a/docs/tutorials/python/predict_image.md b/docs/tutorials/python/predict_image.md
index 8be98d991366..0721abd6a731 100644
--- a/docs/tutorials/python/predict_image.md
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# Predict with pre-trained models
This tutorial explains how to recognize objects in an image with a pre-trained model, and how to perform feature extraction.
diff --git a/docs/tutorials/python/profiler.md b/docs/tutorials/python/profiler.md
index 7dcda10f11b8..fe7611aa538f 100644
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# Profiling MXNet Models
It is often helpful to understand what operations take how much time while running a model. This helps optimize the model to run faster. In this tutorial, we will learn how to profile MXNet models to measure their running time and memory consumption using the MXNet profiler.
diff --git a/docs/tutorials/python/types_of_data_augmentation.md b/docs/tutorials/python/types_of_data_augmentation.md
index 4308932bf483..63ad468f85f6 100644
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# Types of Data Augmentation
diff --git a/docs/tutorials/r/CallbackFunction.md b/docs/tutorials/r/CallbackFunction.md
index 103352dd2907..1258c527e456 100644
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Callback Function
======================================
diff --git a/docs/tutorials/r/CustomIterator.md b/docs/tutorials/r/CustomIterator.md
index 1ad634bcd669..377838d93e46 100644
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Custom Iterator Tutorial
======================================
diff --git a/docs/tutorials/r/CustomLossFunction.md b/docs/tutorials/r/CustomLossFunction.md
index afb99518894c..6b60886bceba 100644
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Customized loss function
======================================
diff --git a/docs/tutorials/r/MultidimLstm.md b/docs/tutorials/r/MultidimLstm.md
index 8692086d180b..5abd38d2e02c 100644
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LSTM time series example
=============================================
diff --git a/docs/tutorials/r/charRnnModel.md b/docs/tutorials/r/charRnnModel.md
index cb21e77559b5..ed873d49214d 100644
--- a/docs/tutorials/r/charRnnModel.md
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# Character-level Language Model using RNN
diff --git a/docs/tutorials/r/classifyRealImageWithPretrainedModel.md b/docs/tutorials/r/classifyRealImageWithPretrainedModel.md
index b2f2035426ec..1344285dac59 100644
--- a/docs/tutorials/r/classifyRealImageWithPretrainedModel.md
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Classify Images with a PreTrained Model
=================================================
MXNet is a flexible and efficient deep learning framework. One of the interesting things that a deep learning
diff --git a/docs/tutorials/r/fiveMinutesNeuralNetwork.md b/docs/tutorials/r/fiveMinutesNeuralNetwork.md
index a2ce5ecd3761..0cf7e6d4a34d 100644
--- a/docs/tutorials/r/fiveMinutesNeuralNetwork.md
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Develop a Neural Network with MXNet in Five Minutes
=============================================
diff --git a/docs/tutorials/r/index.md b/docs/tutorials/r/index.md
index fbc8911f2a6d..13db9694864d 100644
--- a/docs/tutorials/r/index.md
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# R Tutorials
These tutorials introduce a few fundamental concepts in deep learning and how to implement them in R using _MXNet_.
diff --git a/docs/tutorials/r/mnistCompetition.md b/docs/tutorials/r/mnistCompetition.md
index ed3c2827011d..f59aabd77905 100644
--- a/docs/tutorials/r/mnistCompetition.md
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Handwritten Digits Classification Competition
=============================================
diff --git a/docs/tutorials/r/ndarray.md b/docs/tutorials/r/ndarray.md
index cb7639a8a44d..e935a5052cf3 100644
--- a/docs/tutorials/r/ndarray.md
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# NDArray: Vectorized Tensor Computations on CPUs and GPUs
`NDArray` is the basic vectorized operation unit in MXNet for matrix and tensor computations.
diff --git a/docs/tutorials/r/symbol.md b/docs/tutorials/r/symbol.md
index 4a87643b9f50..d60c5fbfbc59 100644
--- a/docs/tutorials/r/symbol.md
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# Symbol and Automatic Differentiation
The computational unit `NDArray` requires a way to construct neural networks. MXNet provides a symbolic interface, named Symbol, to do this. Symbol combines both flexibility and efficiency.
diff --git a/docs/tutorials/scala/char_lstm.md b/docs/tutorials/scala/char_lstm.md
index 4d6a5aee921e..972661bc81ef 100644
--- a/docs/tutorials/scala/char_lstm.md
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# Developing a Character-level Language model
This tutorial shows how to train a character-level language model with a multilayer recurrent neural network (RNN) using Scala. This model takes one text file as input and trains an RNN that learns to predict the next character in the sequence. In this tutorial, you train a multilayer LSTM (Long Short-Term Memory) network that generates relevant text using Barack Obama's speech patterns.
diff --git a/docs/tutorials/scala/index.md b/docs/tutorials/scala/index.md
index f14337f90f08..1e03345a1e45 100644
--- a/docs/tutorials/scala/index.md
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# MXNet-Scala Tutorials
## Installation & Setup
diff --git a/docs/tutorials/scala/mnist.md b/docs/tutorials/scala/mnist.md
index 79f2129ef0ef..89bc203231f7 100644
--- a/docs/tutorials/scala/mnist.md
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# Handwritten Digit Recognition
This Scala tutorial guides you through a classic computer vision application: identifying hand written digits.
diff --git a/docs/tutorials/scala/mxnet_scala_on_intellij.md b/docs/tutorials/scala/mxnet_scala_on_intellij.md
index 174e3018098b..18d30106a085 100644
--- a/docs/tutorials/scala/mxnet_scala_on_intellij.md
+++ b/docs/tutorials/scala/mxnet_scala_on_intellij.md
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# Run MXNet Scala Examples Using the IntelliJ IDE (macOS)
This tutorial guides you through setting up a Scala project in the IntelliJ IDE on macOS, and shows how to use the MXNet package from your application.
diff --git a/docs/tutorials/sparse/csr.md b/docs/tutorials/sparse/csr.md
index 0aede1ab4313..25de1f716045 100644
--- a/docs/tutorials/sparse/csr.md
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# CSRNDArray - NDArray in Compressed Sparse Row Storage Format
diff --git a/docs/tutorials/sparse/index.md b/docs/tutorials/sparse/index.md
index 87d72894424f..faf6526fb824 100644
--- a/docs/tutorials/sparse/index.md
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# Tutorials
```eval_rst
diff --git a/docs/tutorials/sparse/row_sparse.md b/docs/tutorials/sparse/row_sparse.md
index 46a5edad075e..f89de3d5b75e 100644
--- a/docs/tutorials/sparse/row_sparse.md
+++ b/docs/tutorials/sparse/row_sparse.md
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# RowSparseNDArray - NDArray for Sparse Gradient Updates
diff --git a/docs/tutorials/sparse/train.md b/docs/tutorials/sparse/train.md
index fde4c0e65521..2f315cfa1010 100644
--- a/docs/tutorials/sparse/train.md
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# Train a Linear Regression Model with Sparse Symbols
In previous tutorials, we introduced `CSRNDArray` and `RowSparseNDArray`,
diff --git a/docs/tutorials/speech_recognition/ctc.md b/docs/tutorials/speech_recognition/ctc.md
index 0b01fb48999c..948e681de52f 100644
--- a/docs/tutorials/speech_recognition/ctc.md
+++ b/docs/tutorials/speech_recognition/ctc.md
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# Connectionist Temporal Classification
```python
diff --git a/docs/tutorials/speech_recognition/index.md b/docs/tutorials/speech_recognition/index.md
index 87d72894424f..faf6526fb824 100644
--- a/docs/tutorials/speech_recognition/index.md
+++ b/docs/tutorials/speech_recognition/index.md
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# Tutorials
```eval_rst
diff --git a/docs/tutorials/tensorrt/index.md b/docs/tutorials/tensorrt/index.md
index 9515a5b9fd1e..bc10874886ef 100644
--- a/docs/tutorials/tensorrt/index.md
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# Tutorials
```eval_rst
diff --git a/docs/tutorials/tensorrt/inference_with_trt.md b/docs/tutorials/tensorrt/inference_with_trt.md
index 489a92ec1014..ff3cddf3d574 100644
--- a/docs/tutorials/tensorrt/inference_with_trt.md
+++ b/docs/tutorials/tensorrt/inference_with_trt.md
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# Optimizing Deep Learning Computation Graphs with TensorRT
NVIDIA's TensorRT is a deep learning library that has been shown to provide large speedups when used for network inference. MXNet 1.3.0 is shipping with experimental integrated support for TensorRT. This means MXNet users can noew make use of this acceleration library to efficiently run their networks. In this blog post we'll see how to install, enable and run TensorRT with MXNet. We'll also give some insight into what is happening behind the scenes in MXNet to enable TensorRT graph execution.
diff --git a/docs/tutorials/unsupervised_learning/gan.md b/docs/tutorials/unsupervised_learning/gan.md
index 0efdc5565519..ca0fb15e01c5 100644
--- a/docs/tutorials/unsupervised_learning/gan.md
+++ b/docs/tutorials/unsupervised_learning/gan.md
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# Generative Adversarial Network (GAN)
diff --git a/docs/tutorials/unsupervised_learning/index.md b/docs/tutorials/unsupervised_learning/index.md
index 87d72894424f..faf6526fb824 100644
--- a/docs/tutorials/unsupervised_learning/index.md
+++ b/docs/tutorials/unsupervised_learning/index.md
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# Tutorials
```eval_rst
diff --git a/docs/tutorials/vision/cnn_visualization.md b/docs/tutorials/vision/cnn_visualization.md
index 63d2b13271ba..1814e2fe0095 100644
--- a/docs/tutorials/vision/cnn_visualization.md
+++ b/docs/tutorials/vision/cnn_visualization.md
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# Visualizing Decisions of Convolutional Neural Networks
Convolutional Neural Networks have made a lot of progress in Computer Vision. Their accuracy is as good as humans in some tasks. However it remains hard to explain the predictions of convolutional neural networks, as they lack the interpretability offered by other models, for example decision trees.
diff --git a/docs/tutorials/vision/index.md b/docs/tutorials/vision/index.md
index 87d72894424f..faf6526fb824 100644
--- a/docs/tutorials/vision/index.md
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# Tutorials
```eval_rst
diff --git a/docs/tutorials/vision/large_scale_classification.md b/docs/tutorials/vision/large_scale_classification.md
index aac03e4dd903..36a0ff6c9daa 100644
--- a/docs/tutorials/vision/large_scale_classification.md
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# Large Scale Image Classification
Training a neural network with a large number of images presents several challenges. Even with the latest GPUs, it is not possible to train large networks using a large number of images in a reasonable amount of time using a single GPU. This problem can be somewhat mitigated by using multiple GPUs in a single machine. But there is a limit to the number of GPUs that can be attached to one machine (typically 8 or 16). This tutorial explains how to train large networks with terabytes of data using multiple machines each containing multiple GPUs.
diff --git a/example/README.md b/example/README.md
index 2123104a1487..22b9f59fa98c 100644
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# MXNet Examples
This page contains a curated list of awesome MXNet examples, tutorials and blogs. It is inspired by [awesome-php](/~https://github.com/ziadoz/awesome-php) and [awesome-machine-learning](/~https://github.com/josephmisiti/awesome-machine-learning). See also [Awesome-MXNet](/~https://github.com/chinakook/Awesome-MXNet) for a similar list.
diff --git a/example/adversary/README.md b/example/adversary/README.md
index 5d5b44fb91ba..d7c2f46d9efb 100644
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# Adversarial examples
This demonstrates the concept of "adversarial examples" from [1] showing how to fool a well-trained CNN.
diff --git a/example/autoencoder/README.md b/example/autoencoder/README.md
index 7efa30a19b78..66e933d681a9 100644
--- a/example/autoencoder/README.md
+++ b/example/autoencoder/README.md
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-# Example of Autencoder
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+# Example of a Convolutional Autoencoder
Autoencoder architecture is often used for unsupervised feature learning. This [link](http://ufldl.stanford.edu/tutorial/unsupervised/Autoencoders/) contains an introduction tutorial to autoencoders. This example illustrates a simple autoencoder using stack of fully-connected layers for both encoder and decoder. The number of hidden layers and size of each hidden layer can be customized using command line arguments.
diff --git a/example/autoencoder/variational_autoencoder/README.md b/example/autoencoder/variational_autoencoder/README.md
index c6e68d54c4f9..2a7a4966f596 100644
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Variational Auto Encoder(VAE)
=============================
diff --git a/example/bayesian-methods/README.md b/example/bayesian-methods/README.md
index ec9e8be86927..20240de86998 100644
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Bayesian Methods
================
diff --git a/example/bi-lstm-sort/README.md b/example/bi-lstm-sort/README.md
index f00cc85caa30..e26e2b02de63 100644
--- a/example/bi-lstm-sort/README.md
+++ b/example/bi-lstm-sort/README.md
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# Bidirectionnal LSTM to sort an array.
This is an example of using bidirectionmal lstm to sort an array. Please refer to the notebook.
diff --git a/example/caffe/README.md b/example/caffe/README.md
index 466305cc9b88..a497176a996f 100644
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# How to use Caffe operator in MXNet
[Caffe](http://caffe.berkeleyvision.org/) has been a well-known and widely-used deep learning framework. Now MXNet has supported calling most caffe operators(layers) and loss functions directly in its symbolic graph! Using one's own customized caffe layer is also effortless.
diff --git a/example/capsnet/README.md b/example/capsnet/README.md
index 500c7df72515..9319ea3b43bc 100644
--- a/example/capsnet/README.md
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**CapsNet-MXNet**
=========================================
diff --git a/example/captcha/README.md b/example/captcha/README.md
index cc97442f6207..7743997e90bd 100644
--- a/example/captcha/README.md
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This is the R version of [captcha recognition](http://blog.xlvector.net/2016-05/mxnet-ocr-cnn/) example by xlvector and it can be used as an example of multi-label training. For a captcha below, we consider it as an image with 4 labels and train a CNN over the data set.
diff --git a/example/cnn_chinese_text_classification/README.md b/example/cnn_chinese_text_classification/README.md
index bfb271dd5c45..e28a0ec9ac47 100644
--- a/example/cnn_chinese_text_classification/README.md
+++ b/example/cnn_chinese_text_classification/README.md
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Implementing CNN + Highway Network for Chinese Text Classification in MXNet
============
Sentiment classification forked from [incubator-mxnet/cnn_text_classification/](/~https://github.com/apache/incubator-mxnet/tree/master/example/cnn_text_classification), i've implemented the [Highway Networks](https://arxiv.org/pdf/1505.00387.pdf) architecture.The final train model is CNN + Highway Network structure, and this version can achieve a best dev accuracy of 94.75% with the Chinese corpus.
diff --git a/example/cnn_text_classification/README.md b/example/cnn_text_classification/README.md
index 2f1991f319ac..f4ebc43afa96 100644
--- a/example/cnn_text_classification/README.md
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Implementing CNN for Text Classification in MXNet
============
It is a slightly simplified implementation of Kim's [Convolutional Neural Networks for Sentence Classification](http://arxiv.org/abs/1408.5882) paper in MXNet.
diff --git a/example/ctc/README.md b/example/ctc/README.md
index a2f54cffaf86..e64c6e633b2f 100644
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# Connectionist Temporal Classification
[Connectionist Temporal Classification](https://www.cs.toronto.edu/~graves/icml_2006.pdf) (CTC) is a cost function that is used to train Recurrent Neural Networks (RNNs) to label unsegmented input sequence data in supervised learning. For example in a speech recognition application, using a typical cross-entropy loss the input signal needs to be segmented into words or sub-words. However, using CTC-loss, a single unaligned label sequence per input sequence is sufficient for the network to learn both the alignment and labeling. Baidu's warp-ctc page contains a more detailed [introduction to CTC-loss](/~https://github.com/baidu-research/warp-ctc#introduction).
diff --git a/example/deep-embedded-clustering/README.md b/example/deep-embedded-clustering/README.md
index 3972f90bda4a..f3b532d0023d 100644
--- a/example/deep-embedded-clustering/README.md
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# DEC Implementation
This is based on the paper `Unsupervised deep embedding for clustering analysis` by Junyuan Xie, Ross Girshick, and Ali Farhadi
diff --git a/example/distributed_training/README.md b/example/distributed_training/README.md
index b0b0447725b5..d1bd11738cec 100644
--- a/example/distributed_training/README.md
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# Distributed Training using Gluon
Deep learning models are usually trained using GPUs because GPUs can do a lot more computations in parallel that CPUs. But even with the modern GPUs, it could take several days to train big models. Training can be done faster by using multiple GPUs like described in [this](https://gluon.mxnet.io/chapter07_distributed-learning/multiple-gpus-gluon.html) tutorial. However only a certain number of GPUs can be attached to one host (typically 8 or 16). To make the training even faster, we can use multiple GPUs attached to multiple hosts.
diff --git a/example/dsd/README.md b/example/dsd/README.md
index 0ce5cc5d1f0f..4a2a02cddbfa 100644
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DSD Training
============
This folder contains an optimizer class that implements DSD training coupled with SGD. The training
diff --git a/example/fcn-xs/README.md b/example/fcn-xs/README.md
index 145aa31cb700..d809137eb4da 100644
--- a/example/fcn-xs/README.md
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FCN-xs EXAMPLE
--------------
This folder contains an example implementation for Fully Convolutional Networks (FCN) in MXNet.
diff --git a/example/gluon/dc_gan/README.md b/example/gluon/dc_gan/README.md
index 5aacd78a3ed5..fd41d198a69d 100644
--- a/example/gluon/dc_gan/README.md
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# DCGAN in MXNet
[Deep Convolutional Generative Adversarial Networks(DCGAN)](https://arxiv.org/abs/1511.06434) implementation with Apache MXNet GLUON.
diff --git a/example/gluon/embedding_learning/README.md b/example/gluon/embedding_learning/README.md
index e7821619a381..ce1fb536a859 100644
--- a/example/gluon/embedding_learning/README.md
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# Image Embedding Learning
This example implements embedding learning based on a Margin-based Loss with distance weighted sampling [(Wu et al, 2017)](http://www.philkr.net/papers/2017-10-01-iccv/2017-10-01-iccv.pdf). The model obtains a validation Recall@1 of ~64% on the [Caltech-UCSD Birds-200-2011](http://www.vision.caltech.edu/visipedia/CUB-200-2011.html) dataset.
diff --git a/example/gluon/sn_gan/README.md b/example/gluon/sn_gan/README.md
index 5b2a750e4efb..054416fced09 100644
--- a/example/gluon/sn_gan/README.md
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# Spectral Normalization GAN
This example implements [Spectral Normalization for Generative Adversarial Networks](https://arxiv.org/abs/1802.05957) based on [CIFAR10](https://www.cs.toronto.edu/~kriz/cifar.html) dataset.
diff --git a/example/gluon/style_transfer/README.md b/example/gluon/style_transfer/README.md
index ef273a5975ab..c182f3f8fce8 100644
--- a/example/gluon/style_transfer/README.md
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# MXNet-Gluon-Style-Transfer
This repo provides MXNet Implementation of **[Neural Style Transfer](#neural-style)** and **[MSG-Net](#real-time-style-transfer)**.
diff --git a/example/gluon/tree_lstm/README.md b/example/gluon/tree_lstm/README.md
index e14ab4c70afc..8e3b385b77b0 100644
--- a/example/gluon/tree_lstm/README.md
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# Tree-Structured Long Short-Term Memory Networks
This is a [MXNet Gluon](https://mxnet.io/) implementation of Tree-LSTM as described in the paper [Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks](http://arxiv.org/abs/1503.00075) by Kai Sheng Tai, Richard Socher, and Christopher Manning.
diff --git a/example/gluon/word_language_model/README.md b/example/gluon/word_language_model/README.md
index 43d173b868ab..11d3c04a433d 100644
--- a/example/gluon/word_language_model/README.md
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# Word-level language modeling RNN
This example trains a multi-layer RNN (Elman, GRU, or LSTM) on WikiText-2 language modeling benchmark.
diff --git a/example/kaggle-ndsb1/README.md b/example/kaggle-ndsb1/README.md
index 82a99f7b6947..804ed9474dc9 100644
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Tutorial for Kaggle NDSB-1
-----
diff --git a/example/kaggle-ndsb2/README.md b/example/kaggle-ndsb2/README.md
index 302e54033c8a..62a751b69302 100644
--- a/example/kaggle-ndsb2/README.md
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# End-to-End Deep Learning Tutorial for Kaggle NDSB-II
In this example, we will demo how to use MXNet to build an end-to-end deep learning system to help Diagnose Heart Disease. The demo network is able to achieve 0.039222 CRPS on validation set, which is good enough to get Top-10 (on Dec 22nd, 2015).
diff --git a/example/model-parallel/matrix_factorization/README.md b/example/model-parallel/matrix_factorization/README.md
index 00507d924f81..94ec91d0e8b8 100644
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Model Parallel Matrix Factorization
===================================
diff --git a/example/module/README.md b/example/module/README.md
index 99dd756ead63..24c917d5c8bc 100644
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# Module Usage Example
This folder contains usage examples for MXNet module.
diff --git a/example/multi-task/README.md b/example/multi-task/README.md
index b7756fe378a7..a110fd0bd7cf 100644
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# Mulit-task learning example
This is a simple example to show how to use mxnet for multi-task learning. It uses MNIST as an example, trying to predict jointly the digit and whether this digit is odd or even.
diff --git a/example/multivariate_time_series/README.md b/example/multivariate_time_series/README.md
index 87baca36d35f..c3db6c130759 100644
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# LSTNet
- This repo contains an MXNet implementation of [this](https://arxiv.org/pdf/1703.07015.pdf) state of the art time series forecasting model.
diff --git a/example/named_entity_recognition/README.md b/example/named_entity_recognition/README.md
index c914a6985dfe..eaa358d4f18c 100644
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## Goal
- This repo contains an MXNet implementation of this state of the art [entity recognition model](https://www.aclweb.org/anthology/Q16-1026).
diff --git a/example/nce-loss/README.md b/example/nce-loss/README.md
index 56e43525a7ca..26b028001f85 100644
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+++ b/example/nce-loss/README.md
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# Examples of NCE Loss
[Noise-contrastive estimation](http://proceedings.mlr.press/v9/gutmann10a/gutmann10a.pdf) loss (nce-loss) is used to speedup multi-class classification when class num is huge.
diff --git a/example/neural-style/README.md b/example/neural-style/README.md
index 5c4b58924827..e8e69ccc2194 100644
--- a/example/neural-style/README.md
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# Neural art
This is an implementation of the paper
diff --git a/example/neural-style/end_to_end/README.md b/example/neural-style/end_to_end/README.md
index 4a228c199bb7..209d98df7254 100644
--- a/example/neural-style/end_to_end/README.md
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# End to End Neural Art
Please refer to this [blog](http://dmlc.ml/mxnet/2016/06/20/end-to-end-neural-style.html) for details of how it is implemented.
diff --git a/example/numpy-ops/README.md b/example/numpy-ops/README.md
index aa4911f67414..61bf8365bd18 100644
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# Training with Custom Operators in Python
These examples demonstrate custom operator implementations in python.
diff --git a/example/profiler/README.md b/example/profiler/README.md
index 1b9279ccf227..255ad8f65579 100644
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+++ b/example/profiler/README.md
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# MXNet Profiler Examples
This folder contains examples of using MXNet profiler to generate profiling results in json files.
diff --git a/example/rcnn/README.md b/example/rcnn/README.md
index b5284183d160..728a6781b251 100644
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# Faster R-CNN in MXNet
Please redirect any issue or question of using this symbolic example of Faster R-CNN to /~https://github.com/ijkguo/mx-rcnn.
diff --git a/example/recommenders/README.md b/example/recommenders/README.md
index 4b1d5ca6da14..e318cc0fb3cc 100644
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# Recommender Systems
diff --git a/example/reinforcement-learning/a3c/README.md b/example/reinforcement-learning/a3c/README.md
index 5eaba66a5b86..55ade6a279f4 100644
--- a/example/reinforcement-learning/a3c/README.md
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# A3C Implementation
This is an attempt to implement the A3C algorithm in paper Asynchronous Methods for Deep Reinforcement Learning.
diff --git a/example/reinforcement-learning/ddpg/README.md b/example/reinforcement-learning/ddpg/README.md
index 2e299dd5daa3..69e00d982f98 100644
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# mx-DDPG
MXNet Implementation of DDPG
diff --git a/example/reinforcement-learning/parallel_actor_critic/README.md b/example/reinforcement-learning/parallel_actor_critic/README.md
index d3288492a611..5a442079b3a2 100644
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# 'Parallel Advantage-Actor Critic' Implementation
This repo contains a MXNet implementation of a variant of the A3C algorithm from [Asynchronous Methods for Deep Reinforcement Learning](https://arxiv.org/pdf/1602.01783v2.pdf).
diff --git a/example/restricted-boltzmann-machine/README.md b/example/restricted-boltzmann-machine/README.md
index a8769a51e05a..d1609049c137 100644
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# Restricted Boltzmann machine (RBM)
An example of the binary RBM [1] learning the MNIST data. The RBM is implemented as a custom operator, and a gluon block is also provided. `binary_rbm.py` contains the implementation of the RBM. `binary_rbm_module.py` and `binary_rbm_gluon.py` train the MNIST data using the module interface and the gluon interface respectively. The MNIST data is downloaded automatically.
diff --git a/example/rnn/README.md b/example/rnn/README.md
index 1d1df6ed7687..cbafc19e2ef0 100644
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Recurrent Neural Network Examples
===========
diff --git a/example/rnn/bucketing/README.md b/example/rnn/bucketing/README.md
index 7b7883d79ad1..707370af5a96 100644
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RNN Example
===========
This folder contains RNN examples using high level mxnet.rnn interface.
diff --git a/example/rnn/old/README.md b/example/rnn/old/README.md
index 5d73523dd964..b199c7770114 100644
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RNN Example
===========
This folder contains RNN examples using low level symbol interface.
diff --git a/example/rnn/word_lm/README.md b/example/rnn/word_lm/README.md
index ab0a8d704b9c..9f9e1b75b51e 100644
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Word Level Language Modeling
===========
This example trains a multi-layer LSTM on Sherlock Holmes language modeling benchmark.
diff --git a/example/sparse/factorization_machine/README.md b/example/sparse/factorization_machine/README.md
index 32b956ed0201..0023b33a1413 100644
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Factorization Machine
===========
This example trains a factorization machine model using the criteo dataset.
diff --git a/example/sparse/linear_classification/README.md b/example/sparse/linear_classification/README.md
index 926d9234269d..05674cd0c277 100644
--- a/example/sparse/linear_classification/README.md
+++ b/example/sparse/linear_classification/README.md
@@ -1,3 +1,20 @@
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Linear Classification Using Sparse Matrix Multiplication
===========
This examples trains a linear model using the sparse feature in MXNet. This is for demonstration purpose only.
diff --git a/example/sparse/matrix_factorization/README.md b/example/sparse/matrix_factorization/README.md
index ddbf662c858f..88ae7b8e7678 100644
--- a/example/sparse/matrix_factorization/README.md
+++ b/example/sparse/matrix_factorization/README.md
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Matrix Factorization w/ Sparse Embedding
===========
The example demonstrates the basic usage of the sparse.Embedding operator in MXNet, adapted based on @leopd's recommender examples.
diff --git a/example/sparse/wide_deep/README.md b/example/sparse/wide_deep/README.md
index 3df5e420ee36..769d72365402 100644
--- a/example/sparse/wide_deep/README.md
+++ b/example/sparse/wide_deep/README.md
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## Wide and Deep Learning
The example demonstrates how to train [wide and deep model](https://arxiv.org/abs/1606.07792). The [Census Income Data Set](https://archive.ics.uci.edu/ml/datasets/Census+Income) that this example uses for training is hosted by the [UC Irvine Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/). Tricks of feature engineering are adapted from tensorflow's [wide and deep tutorial](/~https://github.com/tensorflow/models/tree/master/official/wide_deep).
diff --git a/example/speech_recognition/README.md b/example/speech_recognition/README.md
index 6f01911e1300..d2125f6578c1 100644
--- a/example/speech_recognition/README.md
+++ b/example/speech_recognition/README.md
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**deepSpeech.mxnet: Rich Speech Example**
=========================================
diff --git a/example/ssd/README.md b/example/ssd/README.md
index 713a9ea33c1b..6d4caa481bd7 100644
--- a/example/ssd/README.md
+++ b/example/ssd/README.md
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# SSD: Single Shot MultiBox Object Detector
SSD is an unified framework for object detection with a single network.
diff --git a/example/ssd/dataset/pycocotools/README.md b/example/ssd/dataset/pycocotools/README.md
old mode 100755
new mode 100644
index d358f53105da..ed4411425f8c
--- a/example/ssd/dataset/pycocotools/README.md
+++ b/example/ssd/dataset/pycocotools/README.md
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This is a modified version of /~https://github.com/pdollar/coco python API.
No `make` is required, but this will not support mask functions.
diff --git a/example/ssd/model/README.md b/example/ssd/model/README.md
index e5bac52f5a83..7c77ee2475e8 100644
--- a/example/ssd/model/README.md
+++ b/example/ssd/model/README.md
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#### This is the default directory to store all the models, including `*.params` and `*.json`
diff --git a/example/ssd/symbol/README.md b/example/ssd/symbol/README.md
index 8fee31985a0d..d577b7067c92 100644
--- a/example/ssd/symbol/README.md
+++ b/example/ssd/symbol/README.md
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## How to compose SSD network on top of mainstream classification networks
1. Have the base network ready in this directory as `name.py`, such as `inceptionv3.py`.
diff --git a/example/ssd/tools/caffe_converter/README.md b/example/ssd/tools/caffe_converter/README.md
index 2e74fc56e022..3d4ab13f4694 100644
--- a/example/ssd/tools/caffe_converter/README.md
+++ b/example/ssd/tools/caffe_converter/README.md
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# Convert Caffe Model to Mxnet Format
This folder contains the source codes for this tool.
diff --git a/example/stochastic-depth/README.md b/example/stochastic-depth/README.md
index 08c466eb8b0e..4a41199893ac 100644
--- a/example/stochastic-depth/README.md
+++ b/example/stochastic-depth/README.md
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Stochastic Depth
================
diff --git a/example/svm_mnist/README.md b/example/svm_mnist/README.md
index 408f5108b44a..8d878fb76859 100644
--- a/example/svm_mnist/README.md
+++ b/example/svm_mnist/README.md
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# Use case with Support Vector Machine
To ensure that not only the implementation is learning, but is able to outsmart the softmax, as [this article](https://arxiv.org/pdf/1306.0239.pdf) suggests, I ran svm_mnist.py script. It was based on the MNIST experiment description on the article and [this tutorial](/~https://github.com/dmlc/mxnet-gtc-tutorial/blob/master/tutorial.ipynb).
diff --git a/example/svrg_module/README.md b/example/svrg_module/README.md
index 250995a57152..f77ecf8fd39d 100644
--- a/example/svrg_module/README.md
+++ b/example/svrg_module/README.md
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## SVRGModule Example
SVRGModule is an extension to the Module API that implements SVRG optimization, which stands for Stochastic
diff --git a/example/vae-gan/README.md b/example/vae-gan/README.md
index 469668b9b374..17e5b0e68e76 100644
--- a/example/vae-gan/README.md
+++ b/example/vae-gan/README.md
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# VAE-GAN in MXNet
* Implementation of [Autoencoding beyond pixels using a learned similarity metric](https://arxiv.org/abs/1512.09300),
diff --git a/julia/LICENSE.md b/julia/LICENSE.md
index 5ecf95ac60bc..3e2c5a2673b8 100644
--- a/julia/LICENSE.md
+++ b/julia/LICENSE.md
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The MXNet.jl package is licensed under version 2.0 of the Apache License:
> Copyright (c) 2015-2018:
diff --git a/julia/NEWS.md b/julia/NEWS.md
index 71ee86ff7da4..43b918939f65 100644
--- a/julia/NEWS.md
+++ b/julia/NEWS.md
@@ -1,4 +1,40 @@
# v0.4.0 (#TBD)
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+# v1.5.0 (#TBD)
* Following material from `mx` module got exported (#TBD):
* `NDArray`
diff --git a/julia/README-DEV.md b/julia/README-DEV.md
index a1d6fa9012fc..25a6c6d93ab9 100644
--- a/julia/README-DEV.md
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# Workflow for making a release
1. Update `NEWS.md` to list important changes
diff --git a/julia/README.md b/julia/README.md
index a4299575f95e..3a28e926963f 100644
--- a/julia/README.md
+++ b/julia/README.md
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# MXNet
[](http://pkg.julialang.org/?pkg=MXNet)
diff --git a/julia/docs/src/api.md b/julia/docs/src/api.md
index 4984129863d0..60cb0831d1bf 100644
--- a/julia/docs/src/api.md
+++ b/julia/docs/src/api.md
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# API Documentation
```@contents
diff --git a/julia/docs/src/api/callback.md b/julia/docs/src/api/callback.md
index f67811cc41fe..5a35e5047120 100644
--- a/julia/docs/src/api/callback.md
+++ b/julia/docs/src/api/callback.md
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# Callback in training
```@autodocs
diff --git a/julia/docs/src/api/context.md b/julia/docs/src/api/context.md
index 93ccf83e51ba..2daabe2db41b 100644
--- a/julia/docs/src/api/context.md
+++ b/julia/docs/src/api/context.md
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# Context
```@autodocs
diff --git a/julia/docs/src/api/executor.md b/julia/docs/src/api/executor.md
index b560c7a0864d..c3037dfff60b 100644
--- a/julia/docs/src/api/executor.md
+++ b/julia/docs/src/api/executor.md
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# Executor
```@autodocs
diff --git a/julia/docs/src/api/initializer.md b/julia/docs/src/api/initializer.md
index d0aad2def4cd..b2515263f93a 100644
--- a/julia/docs/src/api/initializer.md
+++ b/julia/docs/src/api/initializer.md
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# Initializer
```@autodocs
diff --git a/julia/docs/src/api/io.md b/julia/docs/src/api/io.md
index 7312259dbf3c..34ad3c42bce7 100644
--- a/julia/docs/src/api/io.md
+++ b/julia/docs/src/api/io.md
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# Data Providers
Data providers are wrappers that load external data, be it images, text, or general tensors,
diff --git a/julia/docs/src/api/kvstore.md b/julia/docs/src/api/kvstore.md
index 34a5027f85fb..e6bf852b2f43 100644
--- a/julia/docs/src/api/kvstore.md
+++ b/julia/docs/src/api/kvstore.md
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# Key-Value Store
```@autodocs
diff --git a/julia/docs/src/api/metric.md b/julia/docs/src/api/metric.md
index 63cca0cc41ba..163f27a5b397 100644
--- a/julia/docs/src/api/metric.md
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# Evaluation Metrics
Evaluation metrics provide a way to evaluate the performance of a learned model.
diff --git a/julia/docs/src/api/model.md b/julia/docs/src/api/model.md
index f793c7c406c7..63137532de6a 100644
--- a/julia/docs/src/api/model.md
+++ b/julia/docs/src/api/model.md
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# Model
The model API provides convenient high-level interface to do training and predicting on
diff --git a/julia/docs/src/api/ndarray.md b/julia/docs/src/api/ndarray.md
index 5877d8257758..8cc4948e4dde 100644
--- a/julia/docs/src/api/ndarray.md
+++ b/julia/docs/src/api/ndarray.md
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# NDArray API
## Arithmetic Operations
diff --git a/julia/docs/src/api/nn-factory.md b/julia/docs/src/api/nn-factory.md
index 833d9a3efd53..70ecfd2f0157 100644
--- a/julia/docs/src/api/nn-factory.md
+++ b/julia/docs/src/api/nn-factory.md
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# Neural Network Factory
Neural network factory provide convenient helper functions to define
diff --git a/julia/docs/src/api/symbolic-node.md b/julia/docs/src/api/symbolic-node.md
index ef731d9f7d00..b4b1c0167e5e 100644
--- a/julia/docs/src/api/symbolic-node.md
+++ b/julia/docs/src/api/symbolic-node.md
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# Symbolic API
```@autodocs
diff --git a/julia/docs/src/api/visualize.md b/julia/docs/src/api/visualize.md
index 429a927012e4..e401a888cc81 100644
--- a/julia/docs/src/api/visualize.md
+++ b/julia/docs/src/api/visualize.md
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# Network Visualization
```@autodocs
diff --git a/julia/docs/src/index.md b/julia/docs/src/index.md
index b6a51fc162ad..11a77670eae3 100644
--- a/julia/docs/src/index.md
+++ b/julia/docs/src/index.md
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# MXNet Documentation
[MXNet.jl](/~https://github.com/dmlc/MXNet.jl) is the
diff --git a/julia/docs/src/tutorial/char-lstm.md b/julia/docs/src/tutorial/char-lstm.md
index 369bcddd53e9..3961744f282d 100644
--- a/julia/docs/src/tutorial/char-lstm.md
+++ b/julia/docs/src/tutorial/char-lstm.md
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Generating Random Sentence with LSTM RNN
========================================
diff --git a/julia/docs/src/tutorial/mnist.md b/julia/docs/src/tutorial/mnist.md
index 76430fd1b1d0..84827eba049d 100644
--- a/julia/docs/src/tutorial/mnist.md
+++ b/julia/docs/src/tutorial/mnist.md
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Digit Recognition on MNIST
==========================
diff --git a/julia/docs/src/user-guide/faq.md b/julia/docs/src/user-guide/faq.md
index 8fd8a6b34551..2799584f5472 100644
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FAQ
===
diff --git a/julia/docs/src/user-guide/install.md b/julia/docs/src/user-guide/install.md
index f1d5eeefacfe..d1a90a808507 100644
--- a/julia/docs/src/user-guide/install.md
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Installation Guide
==================
diff --git a/julia/docs/src/user-guide/overview.md b/julia/docs/src/user-guide/overview.md
index a81d7ff30e9e..9cc60c78bd93 100644
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# Overview
## MXNet.jl Namespace
diff --git a/julia/examples/char-lstm/README.md b/julia/examples/char-lstm/README.md
index ff16ee0a3ae9..ac745dd4cc41 100644
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# LSTM char-rnn
Because we explicitly unroll the LSTM/RNN over time for a fixed sequence length,
diff --git a/julia/plugins/README.md b/julia/plugins/README.md
index 38882889f494..f9925cbdfbde 100644
--- a/julia/plugins/README.md
+++ b/julia/plugins/README.md
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# Plugins of MXNet.jl
This directory contains *plugins* of MXNet.jl. A plugin is typically a component that could be part of MXNet.jl, but excluded from the `mx` namespace. The plugins are included here primarily for two reasons:
diff --git a/matlab/README.md b/matlab/README.md
index 939b7011a4f2..d5ef5d09fc8d 100644
--- a/matlab/README.md
+++ b/matlab/README.md
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# MATLAB binding for MXNet
### How to use
diff --git a/perl-package/AI-MXNet/examples/gluon/style_transfer/README.md b/perl-package/AI-MXNet/examples/gluon/style_transfer/README.md
index 658a77530a92..9be9b8ad023e 100644
--- a/perl-package/AI-MXNet/examples/gluon/style_transfer/README.md
+++ b/perl-package/AI-MXNet/examples/gluon/style_transfer/README.md
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This directory provides AI::MXNet Implementation of MSG-Net real time style transfer, https://arxiv.org/abs/1703.06953
### Stylize Images Using Pre-trained MSG-Net
diff --git a/perl-package/AI-MXNet/examples/sparse/matrix_factorization/README.md b/perl-package/AI-MXNet/examples/sparse/matrix_factorization/README.md
index debad272206f..3eb1bab508e5 100644
--- a/perl-package/AI-MXNet/examples/sparse/matrix_factorization/README.md
+++ b/perl-package/AI-MXNet/examples/sparse/matrix_factorization/README.md
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Matrix Factorization w/ Sparse Embedding
===========
The example demonstrates the basic usage of the SparseEmbedding operator in MXNet, adapted based on @leopd's recommender examples.
diff --git a/perl-package/AI-MXNet/examples/sparse/wide_deep/README.md b/perl-package/AI-MXNet/examples/sparse/wide_deep/README.md
index 9a481d69edf2..4a01da4254e6 100644
--- a/perl-package/AI-MXNet/examples/sparse/wide_deep/README.md
+++ b/perl-package/AI-MXNet/examples/sparse/wide_deep/README.md
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## Wide and Deep Learning
The example demonstrates how to train [wide and deep model](https://arxiv.org/abs/1606.07792). The [Census Income Data Set](https://archive.ics.uci.edu/ml/datasets/Census+Income) that this example uses for training is hosted by the [UC Irvine Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/). Tricks of feature engineering are adapted from tensorflow's [wide and deep tutorial](/~https://github.com/tensorflow/models/tree/master/official/wide_deep).
diff --git a/perl-package/README.md b/perl-package/README.md
index 93e34d1af37a..893a11d19f88 100644
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+++ b/perl-package/README.md
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[Perl API](https://mxnet.incubator.apache.org/api/perl/index.html)
[](../LICENSE)
diff --git a/plugin/caffe/README.md b/plugin/caffe/README.md
index 466305cc9b88..a497176a996f 100644
--- a/plugin/caffe/README.md
+++ b/plugin/caffe/README.md
@@ -1,3 +1,20 @@
+
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# How to use Caffe operator in MXNet
[Caffe](http://caffe.berkeleyvision.org/) has been a well-known and widely-used deep learning framework. Now MXNet has supported calling most caffe operators(layers) and loss functions directly in its symbolic graph! Using one's own customized caffe layer is also effortless.
diff --git a/python/README.md b/python/README.md
index 1ab7aa4464a3..396d112f687a 100644
--- a/python/README.md
+++ b/python/README.md
@@ -1,3 +1,20 @@
+
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MXNet Python Package
====================
This directory and nested files contain MXNet Python package and language binding.
diff --git a/python/minpy/README.md b/python/minpy/README.md
index 4f028e3b21ad..0278ca1c00fb 100644
--- a/python/minpy/README.md
+++ b/python/minpy/README.md
@@ -1,3 +1,20 @@
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MXNet Python Package
====================
diff --git a/scala-package/README.md b/scala-package/README.md
index 20fbee2469b0..72b3d4d731a5 100644
--- a/scala-package/README.md
+++ b/scala-package/README.md
@@ -1,5 +1,21 @@
-
Deep Learning for Scala/Java
-=====
+
+
+
+
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+
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+MXNet Package for Scala/Java
[](http://jenkins.mxnet-ci.amazon-ml.com/job/incubator-mxnet/job/master/)
[](./LICENSE)
@@ -11,10 +27,10 @@ It brings flexible and efficient GPU/CPU computing and state-of-art deep learnin
in Scala, Java and other languages built on JVM.
- It also enables you to construct and customize the state-of-art deep learning models in JVM languages,
and apply them to tasks such as image classification and data science challenges.
-
+
Install
------------
-
+
Technically, all you need is the `mxnet-full_2.11-{arch}-{xpu}-{version}.jar` in your classpath.
It will automatically extract the native library to a tempfile and load it.
You can find the pre-built jar file in [here](https://search.maven.org/search?q=g:org.apache.mxnet)
diff --git a/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer/objectdetector/README.md b/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer/objectdetector/README.md
index 681253f39a88..7cfe73c69351 100644
--- a/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer/objectdetector/README.md
+++ b/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer/objectdetector/README.md
@@ -1,3 +1,20 @@
+
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# Single Shot Multi Object Detection using Java Inference API
In this example, you will learn how to use Java Inference API to run Inference on pre-trained Single Shot Multi Object Detection (SSD) MXNet model.
diff --git a/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer/predictor/README.md b/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer/predictor/README.md
index 1f2c9e0e813c..09189cb83268 100644
--- a/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer/predictor/README.md
+++ b/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer/predictor/README.md
@@ -1,3 +1,20 @@
+
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# Image Classification using Java Predictor
In this example, you will learn how to use Java Inference API to
diff --git a/scala-package/examples/src/main/scala/org/apache/mxnetexamples/benchmark/README.md b/scala-package/examples/src/main/scala/org/apache/mxnetexamples/benchmark/README.md
index 753cb3125410..d45469ac5296 100644
--- a/scala-package/examples/src/main/scala/org/apache/mxnetexamples/benchmark/README.md
+++ b/scala-package/examples/src/main/scala/org/apache/mxnetexamples/benchmark/README.md
@@ -1,3 +1,20 @@
+
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# Benchmarking Scala Inference APIs
This folder contains a base class [ScalaInferenceBenchmark](/~https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/scala/org/apache/mxnetexamples/benchmark/) and provides a mechanism for benchmarking [MXNet Inference APIs]((/~https://github.com/apache/incubator-mxnet/tree/master/scala-package/infer)) in Scala.
diff --git a/scala-package/examples/src/main/scala/org/apache/mxnetexamples/cnntextclassification/README.md b/scala-package/examples/src/main/scala/org/apache/mxnetexamples/cnntextclassification/README.md
index 5e3602e8ab15..3b28e3ef4463 100644
--- a/scala-package/examples/src/main/scala/org/apache/mxnetexamples/cnntextclassification/README.md
+++ b/scala-package/examples/src/main/scala/org/apache/mxnetexamples/cnntextclassification/README.md
@@ -1,3 +1,20 @@
+
+
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+
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# CNN Text Classification Example for Scala
This is the example using Scala type-safe api doing CNN text classification.
This example is only for Illustration and not modeled to achieve the best accuracy.
diff --git a/scala-package/examples/src/main/scala/org/apache/mxnetexamples/customop/README.md b/scala-package/examples/src/main/scala/org/apache/mxnetexamples/customop/README.md
index 886fa2cc9d46..a3952aabfb44 100644
--- a/scala-package/examples/src/main/scala/org/apache/mxnetexamples/customop/README.md
+++ b/scala-package/examples/src/main/scala/org/apache/mxnetexamples/customop/README.md
@@ -1,3 +1,20 @@
+
+
+
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+
+
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+
+
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+
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# Custom Operator Example for Scala
This is the example using Custom Operator for type-safe api of Scala.
In the example, a `Softmax` operator is implemented to run the MNIST example.
diff --git a/scala-package/examples/src/main/scala/org/apache/mxnetexamples/gan/README.md b/scala-package/examples/src/main/scala/org/apache/mxnetexamples/gan/README.md
index 40db092727c4..a4536a7662e4 100644
--- a/scala-package/examples/src/main/scala/org/apache/mxnetexamples/gan/README.md
+++ b/scala-package/examples/src/main/scala/org/apache/mxnetexamples/gan/README.md
@@ -1,3 +1,20 @@
+
+
+
+
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# GAN MNIST Example for Scala
This is the GAN MNIST Training Example implemented for Scala type-safe api
diff --git a/scala-package/examples/src/main/scala/org/apache/mxnetexamples/imclassification/README.md b/scala-package/examples/src/main/scala/org/apache/mxnetexamples/imclassification/README.md
index cec750acdc92..e533130aa71c 100644
--- a/scala-package/examples/src/main/scala/org/apache/mxnetexamples/imclassification/README.md
+++ b/scala-package/examples/src/main/scala/org/apache/mxnetexamples/imclassification/README.md
@@ -1,3 +1,20 @@
+
+
+
+
+
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# Image Classification Models
This examples contains a number of image classification models that can be run on various datasets.
diff --git a/scala-package/examples/src/main/scala/org/apache/mxnetexamples/infer/imageclassifier/README.md b/scala-package/examples/src/main/scala/org/apache/mxnetexamples/infer/imageclassifier/README.md
index 541e0ce8dd31..e32a66bca832 100644
--- a/scala-package/examples/src/main/scala/org/apache/mxnetexamples/infer/imageclassifier/README.md
+++ b/scala-package/examples/src/main/scala/org/apache/mxnetexamples/infer/imageclassifier/README.md
@@ -1,3 +1,20 @@
+
+
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# Image Classification
This folder contains an example for image classification with the [MXNet Scala Infer API](/~https://github.com/apache/incubator-mxnet/tree/master/scala-package/infer).
diff --git a/scala-package/examples/src/main/scala/org/apache/mxnetexamples/infer/objectdetector/README.md b/scala-package/examples/src/main/scala/org/apache/mxnetexamples/infer/objectdetector/README.md
index 77aec7bb5dee..e5d3bbee0490 100644
--- a/scala-package/examples/src/main/scala/org/apache/mxnetexamples/infer/objectdetector/README.md
+++ b/scala-package/examples/src/main/scala/org/apache/mxnetexamples/infer/objectdetector/README.md
@@ -1,3 +1,20 @@
+
+
+
+
+
+
+
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+
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+
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+
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+
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# Single Shot Multi Object Detection using Scala Inference API
In this example, you will learn how to use Scala Inference API to run Inference on pre-trained Single Shot Multi Object Detection (SSD) MXNet model.
diff --git a/scala-package/examples/src/main/scala/org/apache/mxnetexamples/neuralstyle/README.md b/scala-package/examples/src/main/scala/org/apache/mxnetexamples/neuralstyle/README.md
index fe849343c9d7..0dc4fb892691 100644
--- a/scala-package/examples/src/main/scala/org/apache/mxnetexamples/neuralstyle/README.md
+++ b/scala-package/examples/src/main/scala/org/apache/mxnetexamples/neuralstyle/README.md
@@ -1,3 +1,20 @@
+
+
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# Neural Style Example for Scala
## Introduction
diff --git a/scala-package/examples/src/main/scala/org/apache/mxnetexamples/rnn/README.md b/scala-package/examples/src/main/scala/org/apache/mxnetexamples/rnn/README.md
index 5289fc7b1b4e..2ef2f47c7983 100644
--- a/scala-package/examples/src/main/scala/org/apache/mxnetexamples/rnn/README.md
+++ b/scala-package/examples/src/main/scala/org/apache/mxnetexamples/rnn/README.md
@@ -1,3 +1,20 @@
+
+
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# RNN Example for MXNet Scala
This folder contains the following examples writing in new Scala type-safe API:
- [x] LSTM Bucketing
diff --git a/scala-package/memory-management.md b/scala-package/memory-management.md
index 33c36b6e6ab0..b97bcbcb9026 100644
--- a/scala-package/memory-management.md
+++ b/scala-package/memory-management.md
@@ -1,3 +1,20 @@
+
+
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# JVM Memory Management
The Scala and Java bindings of Apache MXNet use native memory (memory from the C++ heap in either RAM or GPU memory) for most of the MXNet objects such as NDArray, Symbol, Executor, KVStore, Data Iterators, etc.
The associated Scala classes act only as wrappers. The operations done on these wrapper objects are then directed to the high performance MXNet C++ backend via the Java Native Interface (JNI). Therefore, the bytes are stored in the C++ native heap which allows for fast access.
diff --git a/scala-package/mxnet-demo/java-demo/README.md b/scala-package/mxnet-demo/java-demo/README.md
index 55b4d914b834..952775dd964e 100644
--- a/scala-package/mxnet-demo/java-demo/README.md
+++ b/scala-package/mxnet-demo/java-demo/README.md
@@ -1,3 +1,20 @@
+
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# MXNet Java Sample Project
This is an project created to use Maven-published Scala/Java package with two Java examples.
## Setup
diff --git a/scala-package/mxnet-demo/scala-demo/README.md b/scala-package/mxnet-demo/scala-demo/README.md
index 300fc7b2e108..26afe7c9e9b7 100644
--- a/scala-package/mxnet-demo/scala-demo/README.md
+++ b/scala-package/mxnet-demo/scala-demo/README.md
@@ -1,3 +1,20 @@
+
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# MXNet Scala Sample Project
This is an project created to use Maven-published Scala package with two Scala examples.
## Setup
diff --git a/scala-package/spark/README.md b/scala-package/spark/README.md
index 06106648c059..5a249557d078 100644
--- a/scala-package/spark/README.md
+++ b/scala-package/spark/README.md
@@ -1,3 +1,20 @@
+
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Deep Learning on Spark
=====
diff --git a/tests/README.md b/tests/README.md
index e528edf2a9da..de5d8107a790 100644
--- a/tests/README.md
+++ b/tests/README.md
@@ -1,3 +1,20 @@
+
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# Testing MXNET
## Running CPP Tests
diff --git a/tests/nightly/README.md b/tests/nightly/README.md
old mode 100755
new mode 100644
index fa1771a7eeb0..774a975edad6
--- a/tests/nightly/README.md
+++ b/tests/nightly/README.md
@@ -1,3 +1,20 @@
+
+
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# Nightly Tests for MXNet
These are some longer running tests that are scheduled to run every night.
diff --git a/tests/nightly/apache_rat_license_check/README.md b/tests/nightly/apache_rat_license_check/README.md
old mode 100755
new mode 100644
index e8578a857224..70ec665fa57f
--- a/tests/nightly/apache_rat_license_check/README.md
+++ b/tests/nightly/apache_rat_license_check/README.md
@@ -1,3 +1,20 @@
+
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# Apache RAT License Check
This is a nightly test that runs the Apache Tool RAT to check the License Headers on all source files
diff --git a/tests/nightly/apache_rat_license_check/rat-excludes b/tests/nightly/apache_rat_license_check/rat-excludes
index 24624eb4fcfa..b118b25e7f77 100755
--- a/tests/nightly/apache_rat_license_check/rat-excludes
+++ b/tests/nightly/apache_rat_license_check/rat-excludes
@@ -5,18 +5,15 @@
.*html
.*json
.*txt
-.*md
3rdparty/*
R-package/*
trunk/*
-docker/*
.*\\.m
.*\\.mk
.*\\.R
.*svg
.*cfg
.*config
-docs/*
__init__.py
build/*
.*\\.t
@@ -37,7 +34,6 @@ special_functions-inl.h
im2col.cuh
im2col.h
pool.h
-README.rst
dataset.cPickle
image-classification/*
rat-excludes
@@ -49,3 +45,5 @@ REQUIRE
include/*
.*.iml
.*.json.ref
+searchtools_custom.js
+theme.conf
diff --git a/tests/nightly/broken_link_checker_test/README.md b/tests/nightly/broken_link_checker_test/README.md
old mode 100755
new mode 100644
index c39abd0d6175..d493390c844f
--- a/tests/nightly/broken_link_checker_test/README.md
+++ b/tests/nightly/broken_link_checker_test/README.md
@@ -1,3 +1,20 @@
+
+
+
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+
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+
+
+
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+
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# Broken link checker test
This folder contains the scripts that are required to run the nightly job of checking the broken links.
diff --git a/tests/nightly/model_backwards_compatibility_check/README.md b/tests/nightly/model_backwards_compatibility_check/README.md
index 7a2116ac564e..ae7c7c21015e 100644
--- a/tests/nightly/model_backwards_compatibility_check/README.md
+++ b/tests/nightly/model_backwards_compatibility_check/README.md
@@ -1,3 +1,20 @@
+
+
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# Model Backwards Compatibility Tests
This folder contains the scripts that are required to run the nightly job of verifying the compatibility and inference results of models (trained on earlier versions of MXNet) when loaded on the latest release candidate. The tests flag if:
diff --git a/tests/nightly/straight_dope/README.md b/tests/nightly/straight_dope/README.md
old mode 100755
new mode 100644
index 65a615b58d7e..88153aebbc36
--- a/tests/nightly/straight_dope/README.md
+++ b/tests/nightly/straight_dope/README.md
@@ -1,3 +1,20 @@
+
+
+
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+
+
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+
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+
+
# Nightly Tests for MXNet: The Straight Dope
These are some longer running tests that are scheduled to run every night.
diff --git a/tests/python/README.md b/tests/python/README.md
index 02dcb6ea6818..8c09ff03ed1a 100644
--- a/tests/python/README.md
+++ b/tests/python/README.md
@@ -1,3 +1,20 @@
+
+
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Python Test Case
================
This folder contains test cases for mxnet in python.
diff --git a/tools/accnn/README.md b/tools/accnn/README.md
index 02f10d111e2d..9c08cb799d6b 100644
--- a/tools/accnn/README.md
+++ b/tools/accnn/README.md
@@ -1,3 +1,20 @@
+
+
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+
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# Accelerate Convolutional Neural Networks
This tool aims to accelerate the test-time computation and decrease number of parameters of deep CNNs.
diff --git a/tools/bandwidth/README.md b/tools/bandwidth/README.md
index f087af7fd147..4109a3ce39a8 100644
--- a/tools/bandwidth/README.md
+++ b/tools/bandwidth/README.md
@@ -1,3 +1,20 @@
+
+
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# Measure communication bandwidth
MXNet provides multiple ways to communicate data. The best choice depends on
diff --git a/tools/caffe_converter/README.md b/tools/caffe_converter/README.md
index d8ffc5cb83e5..22164d550e9d 100644
--- a/tools/caffe_converter/README.md
+++ b/tools/caffe_converter/README.md
@@ -1,3 +1,20 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
# Convert Caffe Model to Mxnet Format
This folder contains the source codes for this tool.
diff --git a/tools/caffe_translator/README.md b/tools/caffe_translator/README.md
index ad111617b7ed..7ec7b7275dd3 100644
--- a/tools/caffe_translator/README.md
+++ b/tools/caffe_translator/README.md
@@ -1,3 +1,20 @@
+
+
+
+
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+
+
+
+
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+
# Caffe Translator
Caffe Translator is a migration tool that helps developers migrate their existing Caffe code to MXNet and continue further development using MXNet. Note that this is different from the Caffe to MXNet model converter which is available [here](/~https://github.com/apache/incubator-mxnet/tree/master/tools/caffe_converter).
diff --git a/tools/caffe_translator/build_from_source.md b/tools/caffe_translator/build_from_source.md
index 09af64e41460..c08a423a44e7 100644
--- a/tools/caffe_translator/build_from_source.md
+++ b/tools/caffe_translator/build_from_source.md
@@ -1,3 +1,20 @@
+
+
+
+
+
+
+
+
+
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+
+
+
+
+
+
+
### Build Caffe Translator from source
#### Prerequisites:
diff --git a/tools/caffe_translator/faq.md b/tools/caffe_translator/faq.md
index 99d19fef500b..e187257e66a9 100644
--- a/tools/caffe_translator/faq.md
+++ b/tools/caffe_translator/faq.md
@@ -1,3 +1,20 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
### Frequently asked questions
[**Why is Caffe required to run the translated code?**](#why_caffe)
diff --git a/tools/cfn/Readme.md b/tools/cfn/Readme.md
index 677a1826fbb7..16e3cf5802c4 100644
--- a/tools/cfn/Readme.md
+++ b/tools/cfn/Readme.md
@@ -1,2 +1,19 @@
+
+
+
+
+
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+
**Distributed Deep Learning Made Easy has found more love and new home, please visit
[awslabs/deeplearning-cfn](/~https://github.com/awslabs/deeplearning-cfn)**
\ No newline at end of file
diff --git a/tools/coreml/README.md b/tools/coreml/README.md
index 45f19b608bdb..31982babea78 100644
--- a/tools/coreml/README.md
+++ b/tools/coreml/README.md
@@ -1,3 +1,20 @@
+
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# Convert MXNet models into Apple CoreML format.
This tool helps convert MXNet models into [Apple CoreML](https://developer.apple.com/documentation/coreml) format which can then be run on Apple devices.
diff --git a/tools/coreml/pip_package/README.rst b/tools/coreml/pip_package/README.rst
index 875d89fcd208..c2e66f708e85 100644
--- a/tools/coreml/pip_package/README.rst
+++ b/tools/coreml/pip_package/README.rst
@@ -1,3 +1,20 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+ or more contributor license agreements. See the NOTICE file
+ distributed with this work for additional information
+ regarding copyright ownership. The ASF licenses this file
+ to you under the Apache License, Version 2.0 (the
+ "License"); you may not use this file except in compliance
+ with the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing,
+ software distributed under the License is distributed on an
+ "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ KIND, either express or implied. See the License for the
+ specific language governing permissions and limitations
+ under the License.
+
MXNET -> CoreML Converter
=========================
diff --git a/tools/pip_package/README.md b/tools/pip_package/README.md
new file mode 100644
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+MXNet Python Package
+====================
+MXNet is a deep learning framework designed for both *efficiency* and *flexibility*.
+It allows you to mix the flavours of deep learning programs together to maximize the efficiency and your productivity.
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+Installation
+------------
+To install, check [Build Instruction](http://mxnet.io/get_started/setup.html)
