[incubator-mxnet] branch master updated: [MXNET-340] Updated tutorials page. (#10621)

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The following commit(s) were added to refs/heads/master by this push:
     new 97511ba  [MXNET-340] Updated tutorials page. (#10621)
97511ba is described below

commit 97511ba943c436492ae044ae0de2046cd89621bf
Author: Thom Lane <[email protected]>
AuthorDate: Fri May 4 18:54:39 2018 -0700

    [MXNET-340] Updated tutorials page. (#10621)
    
    * Updated tutorials page.
    
    * Combined tutorial links
    
    Added "alternative" links.
    
    * Corrected typo
    
    * Force build.
    
    * Force build #2
    
    * Force build #3
    
    * Force #4
---
 docs/tutorials/index.md | 346 ++++++++++++++++--------------------------------
 1 file changed, 113 insertions(+), 233 deletions(-)

diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md
index 94ea050..f69e1b4 100644
--- a/docs/tutorials/index.md
+++ b/docs/tutorials/index.md
@@ -1,269 +1,149 @@
 # Tutorials
 
-MXNet has two primary high-level interfaces for its deep learning engine: the 
Gluon API and the Module API. Tutorials for each are provided below.
+MXNet tutorials can be found in this section. A variety of language bindings 
are available for MXNet (including Python, Scala, C++ and R) and we have a 
different tutorial section for each language.
 
-`TL;DR:` If you are new to deep learning or MXNet, you should start with the 
Gluon tutorials.
+Are you new to MXNet, and don't have a preference on language? We currently 
recommend starting with Python, and specifically the Gluon APIs (versus Module 
APIs) as they're more flexible and easier to debug.
 
-The difference between the two is an imperative versus symbolic programming 
style. Gluon makes it easy to prototype, build, and train deep learning models 
without sacrificing training speed by enabling both (1) intuitive imperative 
Python code development and (2) faster execution by automatically generating a 
symbolic execution graph using the hybridization feature.
+Another great resource for learning MXNet is our [examples 
section](https://github.com/apache/incubator-mxnet/tree/master/example) which 
includes a wide variety of models (from basic to state-of-the-art) for a wide 
variety of tasks including: object detection, style transfer, reinforcement 
learning, and many others.
 
-The Gluon and Module tutorials are in Python, but you can also find a variety 
of other MXNet tutorials, such as R, Scala, and C++ in the [Other Languages API 
Tutorials](#other-mxnet-api-tutorials) section below.
+<hr>
 
-[Example scripts and applications](#example-scripts-and-applications) as well 
as [contribution](#contributing-tutorials) info is below.
+## Python Tutorials
 
-<script type="text/javascript" src='../_static/js/options.js'></script>
+We have two types of API available for Python: Gluon APIs and Module APIs. 
[See here](/api/python/gluon/gluon.html) for a comparison.
 
+A comprehensive introduction to Gluon can be found at [The Straight 
Dope](http://gluon.mxnet.io/). Structured like a book, it build up from first 
principles of deep learning and take a theoretical walkthrough of progressively 
more complex models using the Gluon API. Also check out the [60-Minute Gluon 
Crash Course](http://gluon-crash-course.mxnet.io/) if you're short on time or 
have used other deep learning frameworks before.
 
-## Python API Tutorials
+Use the tutorial selector below to filter to the relevant tutorials. You might 
see a download link in the top right corner of some tutorials. Use this to 
download a Jupyter Notebook version of the tutorial, and re-run and adjust the 
code as you wish.
+
+<script type="text/javascript" src='../_static/js/options.js'></script>
 
 <!-- Gluon vs Module -->
+Select API:&nbsp;
 <div class="btn-group opt-group" role="group">
   <button type="button" class="btn btn-default opt active" 
style="font-size:22px">Gluon</button>
   <button type="button" class="btn btn-default opt"   
style="font-size:22px">Module</button>
 </div>
-
-
-<!-- Levels -->
-<div class="gluon module">
-<div class="btn-group opt-group" role="group">
-  <button type="button" class="btn btn-default opt 
active">Introduction</button>
-  <button type="button" class="btn btn-default opt">Applications</button>
-</div>
-</div>
-
-
-<!-- introduction Topics -->
-<div class="introduction">
-<div class="btn-group opt-group" role="group">
-  <button type="button" class="btn btn-default opt active">Basics</button>
-  <button type="button" class="btn btn-default opt">Neural Networks</button>
-  <button type="button" class="btn btn-default opt">Advanced</button>
-</div>
-</div>
-
-
-<!-- Intermediate Topics
-<div class="intermediate">
-<div class="btn-group opt-group" role="group">
-  <button type="button" class="btn btn-default opt active">Image 
Recognition</button>
-  <button type="button" class="btn btn-default opt">Human Language</button>
-  <button type="button" class="btn btn-default opt">Recommender 
Systems</button>
-  <button type="button" class="btn btn-default opt">Customization</button>
-</div>
-</div>
--->
-
-<!-- Advanced Topics
-<div class="advanced">
-<div class="btn-group opt-group" role="group">
-  <button type="button" class="btn btn-default opt active">Distributed 
Training</button>
-  <button type="button" class="btn btn-default opt">Optimization</button>
-  <button type="button" class="btn btn-default opt">Adversarial 
Networks</button>
-</div>
-</div>
--->
 <!-- END - Main Menu -->
-<hr>
-
+<br>
 <div class="gluon">
-<div class="introduction">
-
-
-<div class="basics">
-
-- [Manipulate data the MXNet way with 
ndarray](http://gluon.mxnet.io/chapter01_crashcourse/ndarray.html)
-
-- [Automatic differentiation with 
autograd](http://gluon.mxnet.io/chapter01_crashcourse/autograd.html)
-
-- [Linear regression with 
gluon](http://gluon.mxnet.io/chapter02_supervised-learning/linear-regression-gluon.html)
-
-- [Serialization - saving, loading and 
checkpointing](http://gluon.mxnet.io/chapter03_deep-neural-networks/serialization.html)
-
-- [Gluon Datasets and 
DataLoaders](http://mxnet.incubator.apache.org/tutorials/gluon/datasets.html)
-
-</div>
-
-
-<div class="neural-networks">
-
-- [Multilayer perceptrons in 
gluon](http://gluon.mxnet.io/chapter03_deep-neural-networks/mlp-gluon.html)
-
-- [Multi-class object detection using CNNs in 
gluon](http://gluon.mxnet.io/chapter04_convolutional-neural-networks/cnn-gluon.html)
-
-- [Advanced RNNs with 
gluon](http://gluon.mxnet.io/chapter05_recurrent-neural-networks/rnns-gluon.html)
-
-</div>
-
-
-<div class="advanced">
-
-- [Plumbing: A look under the hood of 
gluon](http://gluon.mxnet.io/chapter03_deep-neural-networks/plumbing.html)
-
-- [Designing a custom layer with gluon](/tutorials/gluon/custom_layer.html)
-
-- [Block and Parameter naming](/tutorials/gluon/naming.html)
-
-- [Fast, portable neural networks with Gluon 
HybridBlocks](http://gluon.mxnet.io/chapter07_distributed-learning/hybridize.html)
-
-- [Training on multiple GPUs with 
gluon](http://gluon.mxnet.io/chapter07_distributed-learning/multiple-gpus-gluon.html)
-
-- [Applying data augmentation](/tutorials/gluon/data_augmentation.html)
-
-</div>
-
-</div> <!--end of introduction-->
-
-
-<div class="applications">
-
-- [Creating custom operators with numpy](/tutorials/gluon/customop.html)
-
-- [Handwritten digit recognition (MNIST)](/tutorials/gluon/mnist.html)
-
-- [Hybrid network example](/tutorials/gluon/hybrid.html)
-
-- [Neural network building blocks with gluon](/tutorials/gluon/gluon.html)
-
-- [Simple autograd example](/tutorials/gluon/autograd.html)
-
-- [Data Augmentation with Masks (for Object 
Segmentation)](/tutorials/python/data_augmentation_with_masks.html)
-
-- [Inference using an ONNX model](/tutorials/onnx/inference_on_onnx_model.html)
-
-- [Fine-tuning an ONNX model on Gluon](/tutorials/onnx/fine_tuning_gluon.html)
-
-</div> <!--end of applications-->
 
+* Getting Started
+    * [60-Minute Gluon Crash Course](http://gluon-crash-course.mxnet.io/) <img 
src="https://upload.wikimedia.org/wikipedia/commons/6/6a/External_link_font_awesome.svg";
 alt="External link" height="15px" style="margin: 0px 0px 3px 3px;"/>
+    * [MNIST Handwritten Digit Classification](/tutorials/gluon/mnist.html)
+* Models
+    * [Linear 
Regression](http://gluon.mxnet.io/chapter02_supervised-learning/linear-regression-gluon.html)
 <img 
src="https://upload.wikimedia.org/wikipedia/commons/6/6a/External_link_font_awesome.svg";
 alt="External link" height="15px" style="margin: 0px 0px 3px 3px;"/>
+    * [MNIST Handwritten Digit Classification](/tutorials/gluon/mnist.html)
+    * [Word-level text generation with RNN, LSTM and 
GRU](http://gluon.mxnet.io/chapter05_recurrent-neural-networks/rnns-gluon.html) 
<img 
src="https://upload.wikimedia.org/wikipedia/commons/6/6a/External_link_font_awesome.svg";
 alt="External link" height="15px" style="margin: 0px 0px 3px 3px;"/>
+    * [Visual Question 
Answering](http://gluon.mxnet.io/chapter08_computer-vision/visual-question-answer.html)
 <img 
src="https://upload.wikimedia.org/wikipedia/commons/6/6a/External_link_font_awesome.svg";
 alt="External link" height="15px" style="margin: 0px 0px 3px 3px;"/>
+* Practitioner Guides
+    * [Multi-GPU 
training](http://gluon.mxnet.io/chapter07_distributed-learning/multiple-gpus-gluon.html)
 <img 
src="https://upload.wikimedia.org/wikipedia/commons/6/6a/External_link_font_awesome.svg";
 alt="External link" height="15px" style="margin: 0px 0px 3px 3px;"/>
+    * [Checkpointing and Model Serialization (a.k.a. saving and 
loading)](http://gluon.mxnet.io/chapter03_deep-neural-networks/serialization.html)
 <img 
src="https://upload.wikimedia.org/wikipedia/commons/6/6a/External_link_font_awesome.svg";
 alt="External link" height="15px" style="margin: 0px 0px 3px 3px;"/>
+    * [Inference using an ONNX 
model](/tutorials/onnx/inference_on_onnx_model.html)
+    * [Fine-tuning an ONNX model on 
Gluon](/tutorials/onnx/fine_tuning_gluon.html)
+* API Guides
+    * Core APIs
+        * NDArray
+            * [NDArray API](/tutorials/gluon/ndarray.html) 
([Alternative](http://gluon.mxnet.io/chapter01_crashcourse/ndarray.html) <img 
src="https://upload.wikimedia.org/wikipedia/commons/6/6a/External_link_font_awesome.svg";
 alt="External link" height="15px" style="margin: 0px 0px 3px 3px;"/>)
+            * [Advanced NDArray API](/tutorials/basic/ndarray.html)
+            * [NDArray 
Indexing](https://mxnet.incubator.apache.org/tutorials/basic/ndarray_indexing.html)
+            * Sparse NDArray
+                * [Sparse Gradient Updates 
(RowSparseNDArray)](/tutorials/sparse/row_sparse.html)
+                * [Compressed Sparse Row Storage Format 
(CSRNDArray)](/tutorials/sparse/csr.html)
+                * [Linear Regression with Sparse 
Symbols](/tutorials/sparse/train.html)
+        * Symbol
+            * [Symbol API](/tutorials/basic/symbol.html) (Caution: written 
before Gluon existed)
+        * KVStore
+            * [Key-Value Store API](/tutorials/python/kvstore.html)
+    * Gluon APIs
+        * Blocks and Operators
+            * [Blocks](/tutorials/gluon/gluon.html) 
([Alternative](http://gluon.mxnet.io/chapter03_deep-neural-networks/plumbing.html)
 <img 
src="https://upload.wikimedia.org/wikipedia/commons/6/6a/External_link_font_awesome.svg";
 alt="External link" height="15px" style="margin: 0px 0px 3px 3px;"/>)
+            * [Custom Blocks](/tutorials/gluon/custom_layer.html) 
([Alternative](http://gluon.mxnet.io/chapter03_deep-neural-networks/custom-layer.html)
 <img 
src="https://upload.wikimedia.org/wikipedia/commons/6/6a/External_link_font_awesome.svg";
 alt="External link" height="15px" style="margin: 0px 0px 3px 3px;"/>)
+            * [HybridBlocks](/tutorials/gluon/hybrid.html) 
([Alternative](http://gluon.mxnet.io/chapter07_distributed-learning/hybridize.html)
 <img 
src="https://upload.wikimedia.org/wikipedia/commons/6/6a/External_link_font_awesome.svg";
 alt="External link" height="15px" style="margin: 0px 0px 3px 3px;"/>)
+            * [Block Naming](/tutorials/gluon/naming.html)
+            * [Custom Operators](/tutorials/gluon/customop.html)
+        * Autograd
+            * [AutoGrad API](/tutorials/gluon/autograd.html)
+            * [AutoGrad API with chain 
rule](http://gluon.mxnet.io/chapter01_crashcourse/autograd.html) <img 
src="https://upload.wikimedia.org/wikipedia/commons/6/6a/External_link_font_awesome.svg";
 alt="External link" height="15px" style="margin: 0px 0px 3px 3px;"/>
+            * [AutoGrad API with Python control 
flow](http://gluon-crash-course.mxnet.io/autograd.html) <img 
src="https://upload.wikimedia.org/wikipedia/commons/6/6a/External_link_font_awesome.svg";
 alt="External link" height="15px" style="margin: 0px 0px 3px 3px;"/>
+        * Data
+            * [Datasets and DataLoaders](/tutorials/gluon/datasets.html)
+            * [Data Augmentation with Masks (for Object 
Segmentation)](https://mxnet.incubator.apache.org/tutorials/python/data_augmentation_with_masks.html)
 </div> <!--end of gluon-->
 
-
 <div class="module">
 
-
-<div class="introduction">
-
-
-<div class="basics">
-
-- [Imperative tensor operations on CPU/GPU](/tutorials/basic/ndarray.html)
-
-- [NDArray Indexing](/tutorials/basic/ndarray_indexing.html)
-
-- [Symbol API](/tutorials/basic/symbol.html)
-
-- [Module API](/tutorials/basic/module.html)
-
-- [Iterators - Loading data](/tutorials/basic/data.html)
-
-</div>
-
-
-<div class="neural-networks">
-
-- [Linear regression](/tutorials/python/linear-regression.html)
-
-- [MNIST - handwriting recognition](/tutorials/python/mnist.html)
-
-- [Large scale image 
classification](/tutorials/vision/large_scale_classification.html)
-
-<!-- broken #9532
-- [Image recognition](/tutorials/python/predict_image.html)
--->
-</div>
-
-
-<div class="advanced">
-
-- [NDArray in Compressed Sparse Row storage format](/tutorials/sparse/csr.html)
-
-- [Sparse gradient updates](/tutorials/sparse/row_sparse.html)
-
-- [Train a linear regression model with sparse 
symbols](/tutorials/sparse/train.html)
-
-- [Applying data augmentation](/tutorials/python/data_augmentation.html)
-
-- [Types of data 
augmentation](/tutorials/python/types_of_data_augmentation.html)
-
-</div>
-
-</div> <!--end of introduction-->
-
-
-<div class="applications">
-
-- [Connectionist Temporal 
Classification](../tutorials/speech_recognition/ctc.html)
-
-- [Distributed key-value store](/tutorials/python/kvstore.html)
-
-- [Fine-tuning a pre-trained ImageNet model with a new 
dataset](/faq/finetune.html)
-
-- [Generative Adversarial Networks](/tutorials/unsupervised_learning/gan.html)
-
-- [Matrix factorization in recommender 
systems](/tutorials/python/matrix_factorization.html)
-
-- [Text classification (NLP) on Movie Reviews](/tutorials/nlp/cnn.html)
-
-- [Importing an ONNX model into 
MXNet](http://mxnet.incubator.apache.org/tutorials/onnx/super_resolution.html) 
-
-</div> <!--end of applications-->
-
+* Getting Started
+    * [Module API](/tutorials/basic/module.html)
+    * [MNIST Handwritten Digit Classification](/tutorials/python/mnist.html)
+* Models
+    * [Linear Regression](/tutorials/python/linear-regression.html)
+    * [Linear Regression with Sparse Symbols](/tutorials/sparse/train.html)
+    * [MNIST Handwritten Digit Classification](/tutorials/python/mnist.html)
+    * [Movie Review Classification using Convolutional 
Networks](/tutorials/nlp/cnn.html)
+    * [Generative Adversarial Networks 
(GANs)](/tutorials/unsupervised_learning/gan.html)
+    * [Recommender Systems using Matrix 
Factorization](/tutorials/python/matrix_factorization.html)
+    * [Speech Recognition with Connectionist Temporal Classification 
Loss](https://mxnet.incubator.apache.org/tutorials/speech_recognition/ctc.html)
+* Practitioner Guides
+    * [Fine-Tuning a pre-trained ImageNet model with a new 
dataset](/faq/finetune.html)
+    * [Large-Scale Multi-Host Multi-GPU Image 
Classification](/tutorials/vision/large_scale_classification.html)
+* API Guides
+    * Core APIs
+        * NDArray
+            * [NDArray API](/tutorials/gluon/ndarray.html)
+            * [Advanced NDArray API](/tutorials/basic/ndarray.html)
+            * [NDArray 
Indexing](https://mxnet.incubator.apache.org/tutorials/basic/ndarray_indexing.html)
+            * Sparse NDArray
+                * [Sparse Gradient Updates 
(RowSparseNDArray)](/tutorials/sparse/row_sparse.html)
+                * [Compressed Sparse Row Storage Format 
(CSRNDArray)](/tutorials/sparse/csr.html)
+                * [Linear Regression with Sparse 
Symbols](/tutorials/sparse/train.html)
+        * Symbol
+            * [Symbol API](/tutorials/basic/symbol.html)
+        * KVStore
+            * [Key-Value Store API](/tutorials/python/kvstore.html)
+    * Module APIs
+        * [Module API](/tutorials/basic/module.html)
+        * [Data Iterators](/tutorials/basic/data.html)
 </div> <!--end of module-->
 
-
 <hr>
 
-## Other Languages API Tutorials
+## Scala Tutorials
 
+* Getting Started
+    * [MXNet and JetBrain's 
IntelliJ](/tutorials/scala/mxnet_scala_on_intellij.html)
+* Models
+    * [MNIST Handwritten Digit Recognition with Fully Connected 
Network](/tutorials/scala/mnist.html)
+    * [Barack Obama speech generation with Character-level 
LSTM](/tutorials/scala/char_lstm.html)
 
-<div class="btn-group opt-group" role="group">
-  <button type="button" class="btn btn-default opt active">C</button>
-  <button type="button" class="btn btn-default opt">Scala</button>
-  <button type="button" class="btn btn-default opt">R</button>
-</div>
 <hr>
 
-<div class="c">
-
-- [MNIST with the MXNet C++ API](/tutorials/c%2B%2B/basics.html)
-</div> <!--end of c++-->
-
-
-<div class="r">
+## C++ Tutorials
 
-- [NDArray: Vectorized Tensor Computations on CPUs and GPUs with 
R](/tutorials/r/ndarray.html)
-- [Symbol API with R](/tutorials/r/symbol.html)
-- [Custom Iterator](/tutorials/r/CustomIterator.html)
-- [Callback Function](/tutorials/r/CallbackFunction.html)
-- [Five minute neural network](/tutorials/r/fiveMinutesNeuralNetwork.html)
-- [MNIST with R](/tutorials/r/mnistCompetition.html)
-- [Classify images via R with a pre-trained 
model](/tutorials/r/classifyRealImageWithPretrainedModel.html)
-- [Char RNN Example with R](/tutorials/r/charRnnModel.html)
-- [Custom loss functions in R](/tutorials/r/CustomLossFunction.html)
-
-
-</div> <!--end of r-->
-
-
-<div class="scala">
-
-- [Setup your MXNet with Scala on 
IntelliJ](/tutorials/scala/mxnet_scala_on_intellij.html)
-- [MNIST with the Scala API](/tutorials/scala/mnist.html)
-- [Use Scala to build a Long Short-Term Memory network that generates Barack 
Obama's speech patterns](/tutorials/scala/char_lstm.html)
-
-</div> <!--end of scala-->
+* Models
+    * [MNIST Handwritten Digit Recognition with Fully Connected 
Network](/tutorials/c%2B%2B/basics.html)
 
 <hr>
 
-
-## Example Scripts and Applications
-
-More tutorials and examples are available in the [GitHub 
repository](https://github.com/apache/incubator-mxnet/tree/master/example).
-
-
-## Learn More About Gluon!
-
-Most of the Gluon tutorials are hosted on 
[gluon.mxnet.io](http://gluon.mxnet.io), and you may want to follow the 
chapters on directly the Gluon site.
-
-
+## R Tutorials
+
+* Getting Started
+    * [Basic Classification & 
Regression](/tutorials/r/fiveMinutesNeuralNetwork.html)
+    * [Using a pre-trained model for Image 
Classification](/tutorials/r/classifyRealImageWithPretrainedModel.html)
+* Models
+    * [MNIST Handwritten Digit Classification with Convolutional 
Network](/tutorials/r/mnistCompetition.html)
+    * [Shakespeare generation with Character-level 
RNN](/tutorials/r/charRnnModel.html)
+* API Guides
+    * [NDArray API](/tutorials/r/ndarray.html)
+    * [Symbol API](/tutorials/r/symbol.html)
+    * [Callbacks](/tutorials/r/CallbackFunction.html)
+    * [Custom Data Iterators](/tutorials/r/CustomIterator.html)
+    * [Custom Loss Functions](/tutorials/r/CustomLossFunction.html)
+ 
+<hr>
+ 
 ## Contributing Tutorials
 
-Want to contribute an MXNet tutorial? To get started, [review these 
details](https://github.com/apache/incubator-mxnet/tree/master/example#contributing)
 on example and tutorial writing.
+We really appreciate contributions, and tutorials are a great way to share 
your knowledge and help the community. After you have followed [these 
steps](https://github.com/apache/incubator-mxnet/tree/master/example#contributing),
 please submit a pull request on Github.
+
+And if you have any feedback on this section please raise an issue on Github.

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