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        <h1>PArallel Distributed Deep LEarning</h1>
        <p>An Easy-to-use, Efficient, Flexible and Scalable Deep Learning Platform</p>
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        <h2><span>Diverse Application Scenarios</span></h2>
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        <p class="sub-title">Promoting 100+ new business products on the line</p>
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                <h3>Computer Vision</h3>
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                <p>Using Convolutional Neural Networks (CNN) for Image Recognition and Object Detection.</p>
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                <p>Using Recurrent Neural Network (RNN) for Sentiment Analysis.</p>
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                <h3>Recommender System</h3>
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                <p>Using Deep Learning on Recommendation Systems to Help Users Find Interests.</p>
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        <h2><span>Technical Features</span></h2>
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            <h3>Ease of use</h3>
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            <p>Provides an intuitive and flexible interface for loading data and specifying model structure.</p>
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            <h3>Flexibility</h3>
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            <p>Supports CNN, RNN and various variants and ease to configure complicated deep models.</p>
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            <h3>Efficiency</h3>
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            <p>Provides extremely optimized operations, memory recycling, network communication.</p>
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            <h3>Scalability</h3>
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            <p>Ease to scale heterogeneous computing resource and storage to accelerate training process.</p>
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        <h2>PaddlePaddle</h2>
        <p>An Easy-to-use, Efficient, Flexible and Scalable Deep Learning Platform</p>
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