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 <li class="toctree-l3"><a class="reference external" href="/versions/1.4.1/tutorials/r/fiveMinutesNeuralNetwork.html">Basic Classification</a></li>
 <li class="toctree-l3"><a class="reference external" href="/versions/1.4.1/tutorials/r/classifyRealImageWithPretrainedModel.html">Using a pre-trained model for Image Classification</a></li>
 <li class="toctree-l3"><a class="reference external" href="/versions/1.4.1/tutorials/r/mnistCompetition.html">MNIST Handwritten Digit Classification with Convolutional Network</a></li>
 <li class="toctree-l3"><a class="reference external" href="/versions/1.4.1/tutorials/r/charRnnModel.html">Shakespeare generation with Character-level RNN</a></li>
 <li class="toctree-l3"><a class="reference external" href="/versions/1.4.1/tutorials/r/ndarray.html">NDArray API</a></li>
 <li class="toctree-l3"><a class="reference external" href="/versions/1.4.1/tutorials/r/symbol.html">Symbol API</a></li>
 <li class="toctree-l3"><a class="reference external" href="/versions/1.4.1/tutorials/r/CallbackFunction.html">Callbacks</a></li>
 <li class="toctree-l3"><a class="reference external" href="/versions/1.4.1/tutorials/r/CustomIterator.html">Custom Data Iterators</a></li>
 <li class="toctree-l3"><a class="reference external" href="/versions/1.4.1/tutorials/r/CustomLossFunction.html">Custom Loss Functions</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="#perl-tutorials">Perl Tutorials</a><ul>
 <li class="toctree-l3"><a class="reference external" href="http://blogs.perl.org/users/sergey_kolychev/2017/02/machine-learning-in-perl.html">Machine learning in Perl</a></li>
 <li class="toctree-l3"><a class="reference external" href="http://blogs.perl.org/users/sergey_kolychev/2017/04/machine-learning-in-perl-part2-a-calculator-handwritten-digits-and-roboshakespeare.html">Calculator and Robo-Shakespeare</a></li>
 <li class="toctree-l3"><a class="reference external" href="http://blogs.perl.org/users/sergey_kolychev/2017/10/machine-learning-in-perl-part3-deep-convolutional-generative-adversarial-network.html">DCGAN</a></li>
 <li class="toctree-l3"><a class="reference external" href="http://blogs.perl.org/users/sergey_kolychev/2018/07/machine-learning-in-perl-kyuubi-goes-to-a-modelzoo-during-the-starry-night.html">Image classification and Style transfer</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="#contributing-tutorials">Contributing Tutorials</a></li>
 </ul>
 </li>
 </ul>
 </div>
 </div>
 <div class="content">
 <div class="page-tracker"></div>
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 <!--- under the License. --><div class="section" id="tutorials">
 <span id="tutorials"></span><h1>Tutorials<a class="headerlink" href="#tutorials" title="Permalink to this headline">¶</a></h1>
 <div class="toctree-wrapper compound">
 </div>
 <p>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.</p>
 <p>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.</p>
 <p>Another great resource for learning MXNet is our <a class="reference external" href="https://github.com/apache/incubator-mxnet/tree/1.4.1/example">examples section</a> 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.</p>
 <hr/><div class="section" id="python-tutorials">
 <span id="python-tutorials"></span><h2>Python Tutorials<a class="headerlink" href="#python-tutorials" title="Permalink to this headline">¶</a></h2>
 <p>We have two types of API available for Python: Gluon APIs and Module APIs. <a class="reference external" href="/versions/1.4.1/api/python/gluon/gluon.html">See here</a> for a comparison.</p>
 <p>A comprehensive introduction to Gluon can be found at <a class="reference external" href="http://gluon.mxnet.io/">The Straight Dope</a>. 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 <a class="reference external" href="http://gluon-crash-course.mxnet.io/">60-Minute Gluon Crash Course</a> if you’re short on time or have used other deep learning frameworks before.</p>
 <p>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.</p>
 <script src="../_static/js/options.js" type="text/javascript"></script><!-- Gluon vs Module -->
 Select API:
 <div class="btn-group opt-group" role="group">
 <button class="btn btn-default opt active" style="font-size:22px" type="button">Gluon</button>
 <button class="btn btn-default opt" style="font-size:22px" type="button">Module</button>
 </div>
 <!-- END - Main Menu -->
 <br/>
 <div class="gluon"><ul>
 <li><p class="first">Getting Started</p>
 <ul class="simple">
 <li><a class="reference external" href="http://gluon-crash-course.mxnet.io/">60-Minute Gluon Crash Course</a> <img alt="External link" height="15px" src="https://upload.wikimedia.org/wikipedia/commons/6/6a/External_link_font_awesome.svg" style="margin: 0px 0px 3px 3px;"/></li>
 <li><a class="reference external" href="/versions/1.4.1/tutorials/gluon/mnist.html">MNIST Handwritten Digit Classification</a></li>
 </ul>
 </li>
 <li><p class="first">Models</p>
 <ul class="simple">
 <li><a class="reference external" href="/versions/1.4.1/tutorials/gluon/pretrained_models.html">Model Zoo: using pre-trained models</a></li>
 <li><a class="reference external" href="http://gluon.mxnet.io/chapter02_supervised-learning/linear-regression-gluon.html">Linear Regression</a> <img alt="External link" height="15px" src="https://upload.wikimedia.org/wikipedia/commons/6/6a/External_link_font_awesome.svg" style="margin: 0px 0px 3px 3px;"/></li>
 <li><a class="reference external" href="/versions/1.4.1/tutorials/gluon/logistic_regression_explained.html">Logistic Regression</a></li>
 <li><a class="reference external" href="http://gluon.mxnet.io/chapter05_recurrent-neural-networks/rnns-gluon.html">Word-level text generation with RNN, LSTM and GRU</a> <img alt="External link" height="15px" src="https://upload.wikimedia.org/wikipedia/commons/6/6a/External_link_font_awesome.svg" style="margin: 0px 0px 3px 3px;"/></li>
 <li><a class="reference external" href="http://gluon.mxnet.io/chapter08_computer-vision/visual-question-answer.html">Visual Question Answering</a> <img alt="External link" height="15px" src="https://upload.wikimedia.org/wikipedia/commons/6/6a/External_link_font_awesome.svg" style="margin: 0px 0px 3px 3px;"/></li>
 <li><a class="reference external" href="/versions/1.4.1/tutorials/gluon/info_gan.html">Image similiarity search with InfoGAN</a></li>
 </ul>
 </li>
 <li><p class="first">Practitioner Guides</p>
 <ul class="simple">
 <li><a class="reference external" href="/versions/1.4.1/tutorials/gluon/gotchas_numpy_in_mxnet.html">Gotchas using NumPy</a></li>
 <li><a class="reference external" href="http://gluon.mxnet.io/chapter07_distributed-learning/multiple-gpus-gluon.html">Multi-GPU training</a> <img alt="External link" height="15px" src="https://upload.wikimedia.org/wikipedia/commons/6/6a/External_link_font_awesome.svg" style="margin: 0px 0px 3px 3px;"/></li>
 <li><a class="reference external" href="/versions/1.4.1/tutorials/gluon/save_load_params.html">Checkpointing and Model Serialization (a.k.a. saving and loading)</a> <img alt="External link" height="15px" src="https://upload.wikimedia.org/wikipedia/commons/6/6a/External_link_font_awesome.svg" style="margin: 0px 0px 3px 3px;"/> (<a class="reference external" href="http://gluon.mxnet.io/chapter03_deep-neural-networks/serialization.html">Alternative</a>)</li>
 <li><a class="reference external" href="https://github.com/apache/incubator-mxnet/tree/1.4.1/example/distributed_training">Distributed Training</a></li>
 <li><a class="reference external" href="/versions/1.4.1/tutorials/onnx/inference_on_onnx_model.html">Inference using an ONNX model</a></li>
 <li><a class="reference external" href="/versions/1.4.1/tutorials/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model on Gluon</a></li>
 <li><a class="reference external" href="/versions/1.4.1/tutorials/vision/cnn_visualization.html">Visualizing Decisions of Convolutional Neural Networks</a></li>
 <li><a class="reference external" href="/versions/1.4.1/tutorials/gluon/learning_rate_finder.html">Learning Rate Finder</a></li>
 <li><a class="reference external" href="/versions/1.4.1/tutorials/gluon/learning_rate_schedules.html">Learning Rate Schedules</a></li>
 <li><a class="reference external" href="/versions/1.4.1/tutorials/gluon/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li>
 <li><a class="reference external" href="/versions/1.4.1/tutorials/python/profiler.html">Profiling MXNet Models</a></li>
 <li><a class="reference external" href="/versions/1.4.1/tutorials/control_flow/ControlFlowTutorial.html">Hybridize Gluon models with control flows</a></li>
 </ul>
 </li>
 <li><p class="first">API Guides</p>
 <ul>
 <li><p class="first">Core APIs</p>
 <ul>
 <li><p class="first">NDArray</p>
 <ul>
 <li><p class="first"><a class="reference external" href="/versions/1.4.1/tutorials/gluon/ndarray.html">NDArray API</a> (<a class="reference external" href="http://gluon.mxnet.io/chapter01_crashcourse/ndarray.html">Alternative</a> <img alt="External link" height="15px" src="https://upload.wikimedia.org/wikipedia/commons/6/6a/External_link_font_awesome.svg" style="margin: 0px 0px 3px 3px;"/>)</p>
 </li>
 <li><p class="first"><a class="reference external" href="/versions/1.4.1/tutorials/basic/ndarray.html">Advanced NDArray API</a></p>
 </li>
 <li><p class="first"><a class="reference external" href="/versions/1.4.1/tutorials/basic/ndarray_indexing.html">NDArray Indexing</a></p>
 </li>
 <li><p class="first">Sparse NDArray</p>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/sparse/row_sparse.html">Sparse Gradient Updates (RowSparseNDArray)</a></li>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/sparse/csr.html">Compressed Sparse Row Storage Format (CSRNDArray)</a></li>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/sparse/train.html">Linear Regression with Sparse Symbols</a></li>
 </ul>
 </div>
 </li>
 </ul>
 </li>
 <li><p class="first">Symbol</p>
 <ul class="simple">
 <li><a class="reference external" href="/versions/1.4.1/tutorials/basic/symbol.html">Symbol API</a> (Caution: written before Gluon existed)</li>
 </ul>
 </li>
 <li><p class="first">KVStore</p>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/python/kvstore.html">Key-Value Store API</a></li>
 </ul>
 </div>
 </li>
 </ul>
 </li>
 <li><p class="first">Gluon APIs</p>
 <ul>
 <li><p class="first">Blocks and Operators</p>
 <ul class="simple">
 <li><a class="reference external" href="/versions/1.4.1/tutorials/gluon/gluon.html">Blocks</a> (<a class="reference external" href="http://gluon.mxnet.io/chapter03_deep-neural-networks/plumbing.html">Alternative</a> <img alt="External link" height="15px" src="https://upload.wikimedia.org/wikipedia/commons/6/6a/External_link_font_awesome.svg" style="margin: 0px 0px 3px 3px;"/>)</li>
 <li><a class="reference external" href="/versions/1.4.1/tutorials/gluon/custom_layer.html">Custom Blocks</a> (<a class="reference external" href="http://gluon.mxnet.io/chapter03_deep-neural-networks/custom-layer.html">Alternative</a> <img alt="External link" height="15px" src="https://upload.wikimedia.org/wikipedia/commons/6/6a/External_link_font_awesome.svg" style="margin: 0px 0px 3px 3px;"/>)</li>
 <li><a class="reference external" href="/versions/1.4.1/tutorials/gluon/hybrid.html">HybridBlocks</a> (<a class="reference external" href="http://gluon.mxnet.io/chapter07_distributed-learning/hybridize.html">Alternative</a> <img alt="External link" height="15px" src="https://upload.wikimedia.org/wikipedia/commons/6/6a/External_link_font_awesome.svg" style="margin: 0px 0px 3px 3px;"/>)</li>
 <li><a class="reference external" href="/versions/1.4.1/tutorials/gluon/naming.html">Block Naming</a></li>
 <li><a class="reference external" href="/versions/1.4.1/tutorials/gluon/customop.html">Custom Operators</a></li>
 </ul>
 </li>
 <li><p class="first">Autograd</p>
 <ul class="simple">
 <li><a class="reference external" href="/versions/1.4.1/tutorials/gluon/autograd.html">AutoGrad API</a></li>
 <li><a class="reference external" href="http://gluon.mxnet.io/chapter01_crashcourse/autograd.html">AutoGrad API with chain rule</a> <img alt="External link" height="15px" src="https://upload.wikimedia.org/wikipedia/commons/6/6a/External_link_font_awesome.svg" style="margin: 0px 0px 3px 3px;"/></li>
 <li><a class="reference external" href="http://gluon-crash-course.mxnet.io/autograd.html">AutoGrad API with Python control flow</a> <img alt="External link" height="15px" src="https://upload.wikimedia.org/wikipedia/commons/6/6a/External_link_font_awesome.svg" style="margin: 0px 0px 3px 3px;"/></li>
 </ul>
 </li>
 <li><p class="first">Data</p>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/gluon/datasets.html">Datasets and DataLoaders</a></li>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/gluon/data_augmentation.html">Applying Data Augmentation</a></li>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/python/data_augmentation_with_masks.html">Data Augmentation with Masks (for Object Segmentation)</a></li>
 </ul>
 </div>
 </li>
 </ul>
 </li>
 </ul>
 </li>
 </ul>
 </div> <!--end of gluon--><div class="module"><ul>
 <li><p class="first">Getting Started</p>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/basic/module.html">Module API</a></li>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/python/mnist.html">MNIST Handwritten Digit Classification</a></li>
 </ul>
 </div>
 </li>
 <li><p class="first">Models</p>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/python/linear-regression.html">Linear Regression</a></li>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/sparse/train.html">Linear Regression with Sparse Symbols</a></li>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/python/mnist.html">MNIST Handwritten Digit Classification</a></li>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/nlp/cnn.html">Movie Review Classification using Convolutional Networks</a></li>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/unsupervised_learning/gan.html">Generative Adversarial Networks (GANs)</a></li>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/python/matrix_factorization.html">Recommender Systems using Matrix Factorization</a></li>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/speech_recognition/ctc.html">Speech Recognition with Connectionist Temporal Classification Loss</a></li>
 </ul>
 </div>
 </li>
 <li><p class="first">Practitioner Guides</p>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/python/predict_image.html">Predicting on new images using a pre-trained ImageNet model</a></li>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/faq/finetune.html">Fine-Tuning a pre-trained ImageNet model with a new dataset</a></li>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/vision/large_scale_classification.html">Large-Scale Multi-Host Multi-GPU Image Classification</a></li>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/onnx/super_resolution.html">Importing an ONNX model into MXNet</a></li>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/tensorrt/inference_with_trt.html">Optimizing Deep Learning Computation Graphs with TensorRT</a></li>
 </ul>
 </div>
 </li>
 <li><p class="first">API Guides</p>
 <ul>
 <li><p class="first">Core APIs</p>
 <ul>
 <li><p class="first">NDArray</p>
 <ul>
 <li><p class="first"><a class="reference external" href="/versions/1.4.1/tutorials/gluon/ndarray.html">NDArray API</a></p>
 </li>
 <li><p class="first"><a class="reference external" href="/versions/1.4.1/tutorials/basic/ndarray.html">Advanced NDArray API</a></p>
 </li>
 <li><p class="first"><a class="reference external" href="/versions/1.4.1/tutorials/basic/ndarray_indexing.html">NDArray Indexing</a></p>
 </li>
 <li><p class="first">Sparse NDArray</p>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/sparse/row_sparse.html">Sparse Gradient Updates (RowSparseNDArray)</a></li>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/sparse/csr.html">Compressed Sparse Row Storage Format (CSRNDArray)</a></li>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/sparse/train.html">Linear Regression with Sparse Symbols</a></li>
 </ul>
 </div>
 </li>
 </ul>
 </li>
 <li><p class="first">Symbol</p>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/basic/symbol.html">Symbol API</a></li>
 </ul>
 </div>
 </li>
 <li><p class="first">KVStore</p>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/python/kvstore.html">Key-Value Store API</a></li>
 </ul>
 </div>
 </li>
 </ul>
 </li>
 <li><p class="first">Module APIs</p>
 <ul>
 <li><p class="first"><a class="reference external" href="/versions/1.4.1/tutorials/basic/module.html">Module API</a></p>
 </li>
 <li><p class="first">Data</p>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/basic/data.html">Data Iterators</a></li>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/python/data_augmentation.html">Applying Data Augmentation</a></li>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/python/types_of_data_augmentation.html">Types of Data Augmentation</a></li>
 </ul>
 </div>
 </li>
 </ul>
 </li>
 </ul>
 </li>
 </ul>
 </div> <!--end of module--><hr/></div>
 <div class="section" id="scala-tutorials">
 <span id="scala-tutorials"></span><h2>Scala Tutorials<a class="headerlink" href="#scala-tutorials" title="Permalink to this headline">¶</a></h2>
 <ul>
 <li><p class="first"><a class="reference external" href="/versions/1.4.1/tutorials/scala">MXNet-Scala Tutorials Overview</a></p>
 </li>
 <li><p class="first">Getting Started</p>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/scala/mxnet_scala_on_intellij.html">MXNet and JetBrain’s IntelliJ</a></li>
 </ul>
 </div>
 </li>
 <li><p class="first">Models</p>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/scala/mnist.html">MNIST Handwritten Digit Recognition with Fully Connected Network</a></li>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/scala/char_lstm.html">Barack Obama speech generation with Character-level LSTM</a></li>
 </ul>
 </div>
 </li>
 <li><p class="first"><a class="reference external" href="https://github.com/apache/incubator-mxnet/tree/1.4.1/scala-package/examples/src/main/scala/org/apache/mxnetexamples">MXNet-Scala  Examples</a></p>
 </li>
 </ul>
 <hr/></div>
 <div class="section" id="java-tutorials">
 <span id="java-tutorials"></span><h2>Java Tutorials<a class="headerlink" href="#java-tutorials" title="Permalink to this headline">¶</a></h2>
 <ul>
 <li><p class="first">Getting Started</p>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/java/mxnet_java_on_intellij.html">Developer Environment Setup on IntelliJ IDE</a></li>
 </ul>
 </div>
 </li>
 <li><p class="first"><a class="reference external" href="/versions/1.4.1/tutorials/java/ssd_inference.html">Multi Object Detection using pre-trained Single Shot Detector (SSD) Model</a></p>
 </li>
 <li><p class="first"><a class="reference external" href="https://github.com/apache/incubator-mxnet/tree/1.4.1/scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer">MXNet-Java  Examples</a></p>
 </li>
 </ul>
 <hr/></div>
 <div class="section" id="c-tutorials">
 <span id="c-tutorials"></span><h2>C++ Tutorials<a class="headerlink" href="#c-tutorials" title="Permalink to this headline">¶</a></h2>
 <ul>
 <li><p class="first">Models</p>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/c%2B%2B/basics.html">MNIST Handwritten Digit Recognition with Fully Connected Network</a></li>
 </ul>
 </div>
 </li>
 <li><p class="first">Backends</p>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/c%2B%2B/subgraphAPI.html">Subgraph API</a></li>
 </ul>
 </div>
 </li>
 </ul>
 <hr/></div>
 <div class="section" id="r-tutorials">
 <span id="r-tutorials"></span><h2>R Tutorials<a class="headerlink" href="#r-tutorials" title="Permalink to this headline">¶</a></h2>
 <ul>
 <li><p class="first">Getting Started</p>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/r/fiveMinutesNeuralNetwork.html">Basic Classification</a></li>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/r/classifyRealImageWithPretrainedModel.html">Using a pre-trained model for Image Classification</a></li>
 </ul>
 </div>
 </li>
 <li><p class="first">Models</p>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/r/mnistCompetition.html">MNIST Handwritten Digit Classification with Convolutional Network</a></li>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/r/charRnnModel.html">Shakespeare generation with Character-level RNN</a></li>
 </ul>
 </div>
 </li>
 <li><p class="first">API Guides</p>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/r/ndarray.html">NDArray API</a></li>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/r/symbol.html">Symbol API</a></li>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/r/CallbackFunction.html">Callbacks</a></li>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/r/CustomIterator.html">Custom Data Iterators</a></li>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/r/CustomLossFunction.html">Custom Loss Functions</a></li>
 </ul>
 </div>
 </li>
 </ul>
 <hr/></div>
 <div class="section" id="perl-tutorials">
 <span id="perl-tutorials"></span><h2>Perl Tutorials<a class="headerlink" href="#perl-tutorials" title="Permalink to this headline">¶</a></h2>
 <ul>
 <li><p class="first">Getting Started</p>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference external" href="http://blogs.perl.org/users/sergey_kolychev/2017/02/machine-learning-in-perl.html">Machine learning in Perl</a></li>
 <li class="toctree-l1"><a class="reference external" href="http://blogs.perl.org/users/sergey_kolychev/2017/04/machine-learning-in-perl-part2-a-calculator-handwritten-digits-and-roboshakespeare.html">Calculator and Robo-Shakespeare</a></li>
 </ul>
 </div>
 </li>
 <li><p class="first">Gluon</p>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference external" href="http://blogs.perl.org/users/sergey_kolychev/2017/10/machine-learning-in-perl-part3-deep-convolutional-generative-adversarial-network.html">DCGAN</a></li>
 <li class="toctree-l1"><a class="reference external" href="http://blogs.perl.org/users/sergey_kolychev/2018/07/machine-learning-in-perl-kyuubi-goes-to-a-modelzoo-during-the-starry-night.html">Image classification and Style transfer</a></li>
 </ul>
 </div>
 </li>
 </ul>
 <hr/></div>
 <div class="section" id="contributing-tutorials">
 <span id="contributing-tutorials"></span><h2>Contributing Tutorials<a class="headerlink" href="#contributing-tutorials" title="Permalink to this headline">¶</a></h2>
 <p>We really appreciate contributions, and tutorials are a great way to share your knowledge and help the community. After you have followed <a class="reference external" href="https://github.com/apache/incubator-mxnet/tree/master/example#contributing">these steps</a>, please submit a pull request on Github.</p>
 <p>And if you have any feedback on this section please raise an issue on Github.</p>
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 <li><a class="reference internal" href="#">Tutorials</a><ul>
 <li><a class="reference internal" href="#python-tutorials">Python Tutorials</a><ul>
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 <li><a class="reference internal" href="#scala-tutorials">Scala Tutorials</a><ul>
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 <li><a class="reference internal" href="#java-tutorials">Java Tutorials</a></li>
 <li><a class="reference internal" href="#c-tutorials">C++ Tutorials</a><ul>
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 <li><a class="reference internal" href="#r-tutorials">R Tutorials</a><ul>
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 </li>
 <li><a class="reference internal" href="#perl-tutorials">Perl Tutorials</a><ul>
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 <li><a class="reference internal" href="#contributing-tutorials">Contributing Tutorials</a></li>
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             Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF), <strong>sponsored by the <i>Apache Incubator</i></strong>. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF.
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