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new 97511ba [MXNET-340] Updated tutorials page. (#10621)
97511ba is described below
commit 97511ba943c436492ae044ae0de2046cd89621bf
Author: Thom Lane <thom.e.l...@gmail.com>
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:
<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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