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 <li class="toctree-l4"><a class="reference external" href="http://gluon.mxnet.io/chapter01_crashcourse/ndarray.html">Manipulate data the MXNet way with ndarray</a></li>
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 <li class="toctree-l4"><a class="reference external" href="http://gluon.mxnet.io/chapter04_convolutional-neural-networks/cnn-gluon.html">Convolutional Neural Networks in gluon</a></li>
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 <li class="toctree-l4"><a class="reference external" href="http://gluon.mxnet.io/chapter03_deep-neural-networks/custom-layer.html">Designing a custom layer with gluon</a></li>
 <li class="toctree-l4"><a class="reference external" href="http://gluon.mxnet.io/chapter07_distributed-learning/hybridize.html">Fast, portable neural networks with Gluon HybridBlocks</a></li>
 <li class="toctree-l4"><a class="reference external" href="http://gluon.mxnet.io/chapter07_distributed-learning/multiple-gpus-gluon.html">Training on multiple GPUs with gluon</a></li>
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 <li class="toctree-l4"><a class="reference internal" href="basic/ndarray.html">NDArray - Imperative tensor operations on CPU/GPU</a></li>
 <li class="toctree-l4"><a class="reference internal" href="basic/symbol.html">Symbol - Neural network graphs and auto-differentiation</a></li>
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 <li class="toctree-l4"><a class="reference internal" href="python/mnist.html">Handwritten Digit Recognition</a></li>
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 <li class="toctree-l4"><a class="reference internal" href="sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li>
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 <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="section" id="gluon">
 <span id="gluon"></span><h2>Gluon<a class="headerlink" href="#gluon" title="Permalink to this headline">¶</a></h2>
 <p>Gluon is the high-level interface for MXNet. It is more intuitive and easier to use than the lower level interface.
 Gluon supports dynamic (define-by-run) graphs with JIT-compilation to achieve both flexibility and efficiency.</p>
 <p>This is a selected subset of Gluon tutorials that explains basic usage of Gluon and fundamental concepts in deep learning. For the comprehensive tutorial on Gluon that covers topics from basic statistics and probability theory to reinforcement learning and recommender systems, please see <a class="reference external" href="http://gluon.mxnet.io">gluon.mxnet.io</a>.</p>
 <div class="section" id="basics">
 <span id="basics"></span><h3>Basics<a class="headerlink" href="#basics" title="Permalink to this headline">¶</a></h3>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference external" href="http://gluon.mxnet.io/chapter01_crashcourse/ndarray.html">Manipulate data the MXNet way with ndarray</a></li>
 <li class="toctree-l1"><a class="reference external" href="http://gluon.mxnet.io/chapter01_crashcourse/autograd.html">Automatic differentiation with autograd</a></li>
 <li class="toctree-l1"><a class="reference external" href="http://gluon.mxnet.io/chapter02_supervised-learning/linear-regression-gluon.html">Linear regression with gluon</a></li>
 <li class="toctree-l1"><a class="reference external" href="http://gluon.mxnet.io/chapter03_deep-neural-networks/serialization.html">Serialization - saving, loading and checkpointing</a></li>
 </ul>
 </div>
 </div>
 <div class="section" id="neural-networks">
 <span id="neural-networks"></span><h3>Neural Networks<a class="headerlink" href="#neural-networks" title="Permalink to this headline">¶</a></h3>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference external" href="http://gluon.mxnet.io/chapter03_deep-neural-networks/mlp-gluon.html">Multilayer perceptrons in gluon</a></li>
 <li class="toctree-l1"><a class="reference external" href="http://gluon.mxnet.io/chapter04_convolutional-neural-networks/cnn-gluon.html">Convolutional Neural Networks in gluon</a></li>
 <li class="toctree-l1"><a class="reference external" href="http://gluon.mxnet.io/chapter05_recurrent-neural-networks/rnns-gluon.html">Recurrent Neural Networks with gluon</a></li>
 </ul>
 </div>
 </div>
 <div class="section" id="advanced">
 <span id="advanced"></span><h3>Advanced<a class="headerlink" href="#advanced" title="Permalink to this headline">¶</a></h3>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference external" href="http://gluon.mxnet.io/chapter03_deep-neural-networks/plumbing.html">Plumbing: A look under the hood of gluon</a></li>
 <li class="toctree-l1"><a class="reference external" href="http://gluon.mxnet.io/chapter03_deep-neural-networks/custom-layer.html">Designing a custom layer with gluon</a></li>
 <li class="toctree-l1"><a class="reference external" href="http://gluon.mxnet.io/chapter07_distributed-learning/hybridize.html">Fast, portable neural networks with Gluon HybridBlocks</a></li>
 <li class="toctree-l1"><a class="reference external" href="http://gluon.mxnet.io/chapter07_distributed-learning/multiple-gpus-gluon.html">Training on multiple GPUs with gluon</a></li>
 </ul>
 </div>
 </div>
 </div>
 <div class="section" id="mxnet">
 <span id="mxnet"></span><h2>MXNet<a class="headerlink" href="#mxnet" title="Permalink to this headline">¶</a></h2>
 <p>These tutorials introduce a few fundamental concepts in deep learning and how to implement them in <em>MXNet</em>. The <em>Basics</em> section contains tutorials on manipulating arrays, building networks, loading/preprocessing data, etc. The <em>Training and Inference</em> section talks about implementing Linear Regression, training a Handwritten digit classifier using MLP and CNN, running inferences using a pre-trained model, and lastly, efficiently training a large scale image classifier.</p>
 <div class="section" id="basics">
 <span id="id1"></span><h3>Basics<a class="headerlink" href="#basics" title="Permalink to this headline">¶</a></h3>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="basic/ndarray.html">NDArray - Imperative tensor operations on CPU/GPU</a></li>
 <li class="toctree-l1"><a class="reference internal" href="basic/symbol.html">Symbol - Neural network graphs and auto-differentiation</a></li>
 <li class="toctree-l1"><a class="reference internal" href="basic/module.html">Module - Neural network training and inference</a></li>
 <li class="toctree-l1"><a class="reference internal" href="basic/data.html">Iterators - Loading data</a></li>
 </ul>
 </div>
 </div>
 <div class="section" id="training-and-inference">
 <span id="training-and-inference"></span><h3>Training and Inference<a class="headerlink" href="#training-and-inference" title="Permalink to this headline">¶</a></h3>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="python/linear-regression.html">Linear Regression</a></li>
 <li class="toctree-l1"><a class="reference internal" href="python/mnist.html">Handwritten Digit Recognition</a></li>
 <li class="toctree-l1"><a class="reference internal" href="python/predict_image.html">Predict with pre-trained models</a></li>
 <li class="toctree-l1"><a class="reference internal" href="vision/large_scale_classification.html">Large Scale Image Classification</a></li>
 </ul>
 </div>
 </div>
 <div class="section" id="sparse-ndarray">
 <span id="sparse-ndarray"></span><h3>Sparse NDArray<a class="headerlink" href="#sparse-ndarray" title="Permalink to this headline">¶</a></h3>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li>
 <li class="toctree-l1"><a class="reference internal" href="sparse/train.html">Train a Linear Regression Model with Sparse Symbols</a></li>
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 More tutorials and examples are available in the GitHub <a class="reference external" href="https://github.com/dmlc/mxnet/tree/master/example">repository</a>.</p>
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 <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>Want to contribute an MXNet tutorial? To get started, download the <a class="reference external" href="https://github.com/dmlc/mxnet/tree/master/example/MXNetTutorialTemplate.ipynb">tutorial template</a>.</p>
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