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 <p><a id='MXNet-Documentation-1'></a></p>
 <h1 id="mxnet-documentation">MXNet Documentation</h1>
 <p><a href="https://github.com/dmlc/MXNet.jl">MXNet.jl</a> is the <a href="http://julialang.org/">Julia</a> package of <a href="https://github.com/apache/mxnet">MXNet</a>. MXNet.jl brings flexible and efficient GPU computing and state-of-art deep learning to Julia. Some highlight of features include:</p>
 <ul>
 <li>Efficient tensor/matrix computation across multiple devices, including multiple CPUs, GPUs and distributed server nodes.</li>
 <li>Flexible symbolic manipulation to composite and construct state-of-the-art deep learning models.</li>
 </ul>
 <p>For more details, see documentation below. Please also checkout the <a href="https://github.com/apache/mxnet/tree/master/julia/examples">examples</a> directory.</p>
 <p><a id='Tutorials-1'></a></p>
 <h2 id="tutorials">Tutorials</h2>
 <ul>
 <li><a href="tutorial/mnist/#Digit-Recognition-on-MNIST-1">Digit Recognition on MNIST</a><ul>
 <li><a href="tutorial/mnist/#Simple-3-layer-MLP-1">Simple 3-layer MLP</a></li>
 <li><a href="tutorial/mnist/#Convolutional-Neural-Networks-1">Convolutional Neural Networks</a></li>
 <li><a href="tutorial/mnist/#Predicting-with-a-trained-model-1">Predicting with a trained model</a></li>
 </ul>
 </li>
 <li><a href="tutorial/char-lstm/#Generating-Random-Sentence-with-LSTM-RNN-1">Generating Random Sentence with LSTM RNN</a><ul>
 <li><a href="tutorial/char-lstm/#LSTM-Cells-1">LSTM Cells</a></li>
 <li><a href="tutorial/char-lstm/#Unfolding-LSTM-1">Unfolding LSTM</a></li>
 <li><a href="tutorial/char-lstm/#Data-Provider-for-Text-Sequences-1">Data Provider for Text Sequences</a></li>
 <li><a href="tutorial/char-lstm/#Training-the-LSTM-1">Training the LSTM</a></li>
 <li><a href="tutorial/char-lstm/#Sampling-Random-Sentences-1">Sampling Random Sentences</a></li>
 <li><a href="tutorial/char-lstm/#Visualizing-the-LSTM-1">Visualizing the LSTM</a></li>
 </ul>
 </li>
 </ul>
 <p><a id='User's-Guide-1'></a></p>
 <h2 id="users-guide">User's Guide</h2>
 <ul>
 <li><a href="user-guide/install/#Installation-Guide-1">Installation Guide</a><ul>
 <li><a href="user-guide/install/#Automatic-Installation-1">Automatic Installation</a></li>
 <li><a href="user-guide/install/#Manual-Compilation-1">Manual Compilation</a></li>
 </ul>
 </li>
 <li><a href="user-guide/overview/#Overview-1">Overview</a><ul>
 <li><a href="user-guide/overview/#MXNet.jl-Namespace-1">MXNet.jl Namespace</a></li>
 <li><a href="user-guide/overview/#Low-Level-Interface-1">Low Level Interface</a></li>
 <li><a href="user-guide/overview/#Intermediate-Level-Interface-1">Intermediate Level Interface</a></li>
 <li><a href="user-guide/overview/#High-Level-Interface-1">High Level Interface</a></li>
 </ul>
 </li>
 <li><a href="user-guide/faq/#FAQ-1">FAQ</a><ul>
 <li><a href="user-guide/faq/#Running-MXNet-on-AWS-GPU-instances-1">Running MXNet on AWS GPU instances</a></li>
 </ul>
 </li>
 </ul>
 <p><a id='API-Documentation-1'></a></p>
 <h2 id="api-documentation">API Documentation</h2>
 <ul>
 <li><a href="api/context/#Context-1">Context</a></li>
 <li><a href="api/ndarray/#NDArray-API-1">NDArray API</a><ul>
 <li><a href="api/ndarray/#Arithmetic-Operations-1">Arithmetic Operations</a></li>
 <li><a href="api/ndarray/#Trigonometric-Functions-1">Trigonometric Functions</a></li>
 <li><a href="api/ndarray/#Hyperbolic-Functions-1">Hyperbolic Functions</a></li>
 <li><a href="api/ndarray/#Activation-Functions-1">Activation Functions</a></li>
 <li><a href="api/ndarray/#Reference-1">Reference</a></li>
 </ul>
 </li>
 <li><a href="api/symbolic-node/#Symbolic-API-1">Symbolic API</a></li>
 <li><a href="api/model/#Model-1">Model</a></li>
 <li><a href="api/metric/#Evaluation-Metrics-1">Evaluation Metrics</a></li>
 <li><a href="api/io/#Data-Providers-1">Data Providers</a><ul>
 <li><a href="api/io/#AbstractDataProvider-interface-1">AbstractDataProvider interface</a></li>
 <li><a href="api/io/#AbstractDataBatch-interface-1">AbstractDataBatch interface</a></li>
 <li><a href="api/io/#Implemented-providers-and-other-methods-1">Implemented providers and other methods</a></li>
 </ul>
 </li>
 <li><a href="api/nn-factory/#Neural-Network-Factory-1">Neural Network Factory</a></li>
 <li><a href="api/executor/#Executor-1">Executor</a></li>
 <li><a href="api/kvstore/#Key-Value-Store-1">Key-Value Store</a></li>
 <li><a href="api/visualize/#Network-Visualization-1">Network Visualization</a></li>
 </ul>


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	<!–- Licensed to the Apache Software Foundation (ASF) under one –> <!–- or more contributor license agreements. See the NOTICE file –> <!–- distributed with this work for additional information –> <!–- regarding copyright ownership. The ASF licenses this file –> <!–- to you under the Apache License, Version 2.0 (the –> <!–- "License"); you may not use this file except in compliance –> <!–- with the License. You may obtain a copy of the License at –>

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	<p><a id='MXNet-Documentation-1'></a></p>
	<h1 id="mxnet-documentation">MXNet Documentation</h1>
	<p><a href="https://github.com/dmlc/MXNet.jl">MXNet.jl</a> is the <a href="http://julialang.org/">Julia</a> package of <a href="https://github.com/apache/mxnet">MXNet</a>. MXNet.jl brings flexible and efficient GPU computing and state-of-art deep learning to Julia. Some highlight of features include:</p>
	<ul>
	<li>Efficient tensor/matrix computation across multiple devices, including multiple CPUs, GPUs and distributed server nodes.</li>
	<li>Flexible symbolic manipulation to composite and construct state-of-the-art deep learning models.</li>
	</ul>
	<p>For more details, see documentation below. Please also checkout the <a href="https://github.com/apache/mxnet/tree/master/julia/examples">examples</a> directory.</p>
	<p><a id='Tutorials-1'></a></p>
	<h2 id="tutorials">Tutorials</h2>
	<ul>
	<li><a href="tutorial/mnist/#Digit-Recognition-on-MNIST-1">Digit Recognition on MNIST</a><ul>
	<li><a href="tutorial/mnist/#Simple-3-layer-MLP-1">Simple 3-layer MLP</a></li>
	<li><a href="tutorial/mnist/#Convolutional-Neural-Networks-1">Convolutional Neural Networks</a></li>
	<li><a href="tutorial/mnist/#Predicting-with-a-trained-model-1">Predicting with a trained model</a></li>
	</ul>
	</li>
	<li><a href="tutorial/char-lstm/#Generating-Random-Sentence-with-LSTM-RNN-1">Generating Random Sentence with LSTM RNN</a><ul>
	<li><a href="tutorial/char-lstm/#LSTM-Cells-1">LSTM Cells</a></li>
	<li><a href="tutorial/char-lstm/#Unfolding-LSTM-1">Unfolding LSTM</a></li>
	<li><a href="tutorial/char-lstm/#Data-Provider-for-Text-Sequences-1">Data Provider for Text Sequences</a></li>
	<li><a href="tutorial/char-lstm/#Training-the-LSTM-1">Training the LSTM</a></li>
	<li><a href="tutorial/char-lstm/#Sampling-Random-Sentences-1">Sampling Random Sentences</a></li>
	<li><a href="tutorial/char-lstm/#Visualizing-the-LSTM-1">Visualizing the LSTM</a></li>
	</ul>
	</li>
	</ul>
	<p><a id='User's-Guide-1'></a></p>
	<h2 id="users-guide">User's Guide</h2>
	<ul>
	<li><a href="user-guide/install/#Installation-Guide-1">Installation Guide</a><ul>
	<li><a href="user-guide/install/#Automatic-Installation-1">Automatic Installation</a></li>
	<li><a href="user-guide/install/#Manual-Compilation-1">Manual Compilation</a></li>
	</ul>
	</li>
	<li><a href="user-guide/overview/#Overview-1">Overview</a><ul>
	<li><a href="user-guide/overview/#MXNet.jl-Namespace-1">MXNet.jl Namespace</a></li>
	<li><a href="user-guide/overview/#Low-Level-Interface-1">Low Level Interface</a></li>
	<li><a href="user-guide/overview/#Intermediate-Level-Interface-1">Intermediate Level Interface</a></li>
	<li><a href="user-guide/overview/#High-Level-Interface-1">High Level Interface</a></li>
	</ul>
	</li>
	<li><a href="user-guide/faq/#FAQ-1">FAQ</a><ul>
	<li><a href="user-guide/faq/#Running-MXNet-on-AWS-GPU-instances-1">Running MXNet on AWS GPU instances</a></li>
	</ul>
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	<p><a id='API-Documentation-1'></a></p>
	<h2 id="api-documentation">API Documentation</h2>
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	<li><a href="api/context/#Context-1">Context</a></li>
	<li><a href="api/ndarray/#NDArray-API-1">NDArray API</a><ul>
	<li><a href="api/ndarray/#Arithmetic-Operations-1">Arithmetic Operations</a></li>
	<li><a href="api/ndarray/#Trigonometric-Functions-1">Trigonometric Functions</a></li>
	<li><a href="api/ndarray/#Hyperbolic-Functions-1">Hyperbolic Functions</a></li>
	<li><a href="api/ndarray/#Activation-Functions-1">Activation Functions</a></li>
	<li><a href="api/ndarray/#Reference-1">Reference</a></li>
	</ul>
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	<li><a href="api/model/#Model-1">Model</a></li>
	<li><a href="api/metric/#Evaluation-Metrics-1">Evaluation Metrics</a></li>
	<li><a href="api/io/#Data-Providers-1">Data Providers</a><ul>
	<li><a href="api/io/#AbstractDataProvider-interface-1">AbstractDataProvider interface</a></li>
	<li><a href="api/io/#AbstractDataBatch-interface-1">AbstractDataBatch interface</a></li>
	<li><a href="api/io/#Implemented-providers-and-other-methods-1">Implemented providers and other methods</a></li>
	</ul>
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	<li><a href="api/nn-factory/#Neural-Network-Factory-1">Neural Network Factory</a></li>
	<li><a href="api/executor/#Executor-1">Executor</a></li>
	<li><a href="api/kvstore/#Key-Value-Store-1">Key-Value Store</a></li>
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