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        <div id="banner-title">Flexible and Efficient Library for Deep Learning</div>
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          <a href="get_started/" id="get_start_btn">Get Started</a>
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        <h3><i class="fa fa-flag"></i> Flexible</h3>
        <p>Supports both imperative and symbolic programming</p>
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        <h3><i class="fa fa-cube"></i> Portable</h3>
        <p>Runs on CPUs or GPUs, on clusters, servers, desktops, or mobile phones</p>
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        <h3><i class="fa fa-wrench"></i>Multiple Languages</h3>
        <p>Supports multiple languages, including C++, Python, R, Scala, Julia, Perl, Matlab and Javascript - All with the same amazing performance.</p>
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        <h3><i class="fa fa-cogs"></i> Auto-Differentiation</h3>
        <p>Calculates the gradient automatically for training a model</p>
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        <h3><i class="fa fa-cloud"></i>Distributed on Cloud</h3>
        <p>Supports distributed training on multiple CPU/GPU machines, including AWS,
          GCE, Azure, and Yarn clusters</p>
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        <h3><i class="fa fa-rocket"></i> Performance</h3>
        <p>Optimized C++ backend engine parallelizes both I/O and computation</p>
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      <p>
        MXNet is developed by collaborators from multiple universities and
        companies. We sincerely thank the following organizations for supporting
        MXNet and sponsoring its major developers (alphabetical order).
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