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<span id="tutorials"></span><h1>Tutorials<a class="headerlink" href="#tutorials" title="Permalink to this headline"></a></h1>
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<span id="ndarray"></span><h2>NDArray<a class="headerlink" href="#ndarray" title="Permalink to this headline"></a></h2>
<p>NDArray is MXNet’s primary tool for storing and transforming data. NDArrays are similar to NumPy’s multi-dimensional array. However, they confer a few key advantages. First, NDArrays support asynchronous computation on CPU, GPU, and distributed cloud architectures. Second, they provide support for automatic differentiation. These properties make NDArray an ideal library for machine learning, both for researchers and engineers launching production systems.</p>
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<span id="automatic-gradients"></span><h2>Automatic gradients<a class="headerlink" href="#automatic-gradients" title="Permalink to this headline"></a></h2>
<p>MXNet makes it easier to calculate derivatives by automatically calculating them while writing ordinary imperative code. Every time you a make pass through your model, autograd builds a graph on the fly, through which it can immediately backpropagate gradients.</p>
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<li class="toctree-l1"><a class="reference external" href="http://gluon.mxnet.io/chapter01_crashcourse/autograd.html">Automatic differentiation with autograd</a></li>
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<span id="gluon"></span><h2>Gluon<a class="headerlink" href="#gluon" title="Permalink to this headline"></a></h2>
<p>Gluon is MXNet’s imperative API. It is more intuitive and easier to use than the symbolic API. 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 gluon.mxnet.io.</p>
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<span id="basics"></span><h3>Basics<a class="headerlink" href="#basics" title="Permalink to this headline"></a></h3>
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<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>
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<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/multiple-gpus-gluon.html">Training on multiple GPUs with gluon</a></li>
</ul>
</div>
</div>
</div>
<div class="section" id="symbolic-interface">
<span id="symbolic-interface"></span><h2>Symbolic Interface<a class="headerlink" href="#symbolic-interface" title="Permalink to this headline"></a></h2>
<p>MXNet’s symbolic interface lets users define a computation graph first and then execute it using MXNet. This enables MXNet to perform a lot of optimizations that are not possible in imperative execution (like operator folding and safe reuse of memory used by temporary variables).</p>
<div class="toctree-wrapper compound">
<ul>
<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>
<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>
</ul>
</div>
</div>
<div class="section" id="hybrid-networks">
<span id="hybrid-networks"></span><h2>Hybrid Networks<a class="headerlink" href="#hybrid-networks" title="Permalink to this headline"></a></h2>
<p>Imperative programs are very intuitive to write and are very flexible. But symbolic programs tend to be more efficient. MXNet combines both these paradigms to give users the best of both worlds. Users can write intuitive imperative code during development and MXNet will automatically generate symbolic execution graph for faster execution.</p>
<div class="toctree-wrapper compound">
<ul>
<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>
</ul>
</div>
</div>
<div class="section" id="sparse-operations">
<span id="sparse-operations"></span><h2>Sparse operations<a class="headerlink" href="#sparse-operations" title="Permalink to this headline"></a></h2>
<p>A lot of real-world datasets are very sparse (very few nonzeros). MXNet’s sparse operations help store these sparse matrices is a memory efficient way and perform computations on them much faster.</p>
<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>
</ul>
</div>
</div>
<div class="section" id="performance">
<span id="performance"></span><h2>Performance<a class="headerlink" href="#performance" title="Permalink to this headline"></a></h2>
<p>A lot of real-world datasets are too huge to train models on a single GPU or a single machine. MXNet solves this problem by scaling almost linearly across multiple GPUs and multiple machines.</p>
<div class="toctree-wrapper compound">
<ul>
<li class="toctree-l1"><a class="reference internal" href="vision/large_scale_classification.html">Large Scale Image Classification</a></li>
</ul>
</div>
<p><br/>
More tutorials and examples are available in the GitHub <a class="reference external" href="https://github.com/apache/incubator-mxnet/tree/master/example">repository</a>.</p>
</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>Want to contribute an MXNet tutorial? To get started, download the <a class="reference external" href="https://github.com/apache/incubator-mxnet/tree/master/example/MXNetTutorialTemplate.ipynb">tutorial template</a>.</p>
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<h3><a href="../index.html">Table Of Contents</a></h3>
<ul>
<li><a class="reference internal" href="#">Tutorials</a><ul>
<li><a class="reference internal" href="#ndarray">NDArray</a></li>
<li><a class="reference internal" href="#automatic-gradients">Automatic gradients</a></li>
<li><a class="reference internal" href="#gluon">Gluon</a><ul>
<li><a class="reference internal" href="#basics">Basics</a></li>
<li><a class="reference internal" href="#neural-networks">Neural Networks</a></li>
<li><a class="reference internal" href="#advanced">Advanced</a></li>
</ul>
</li>
<li><a class="reference internal" href="#symbolic-interface">Symbolic Interface</a></li>
<li><a class="reference internal" href="#hybrid-networks">Hybrid Networks</a></li>
<li><a class="reference internal" href="#sparse-operations">Sparse operations</a></li>
<li><a class="reference internal" href="#performance">Performance</a></li>
<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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