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RpUQNFBgmrKQSBeJQuomylpKmWevbqUYl6NmnZEoHv+6/XM5VKpW1E2RMarBgrwuNIzNaNSpDdem94e+804Hgj7W6d5TeHxUH23xacQyKCn0eck78HbtUyZXUFCzCZ/c3DQ8hOSr/ExlkEdBoNngG6gWX620SnTi5NRprLNY2tQ4M0YR0i3yAoNk2+QbjcMrC6gY6Yei4JESsY+Cu0rrYyL9c0GGk6gLUJ1bMZWGXRyUolSDuwNFxP3/czwOWpVGpZmCg/VyX+CB3gNTooHwAuUBForzFowxiH+EpMkvUZ5zarPnN4iGzj1VL1XeDPei5KIT9DRTzzmetLIYrv+0u0wW2d164dscAYRKssAyFI06WN3Wac31DkzFdKmjhE1TO4tgixNPYWUM9mJcOxRdSzGbGotikxy6lni6ZpSqhtYvvT9/1FqVRqTqAj3KKi0xlqlboQCTmZA3wWiaOKIskky2qSA+5nf5/M/cDnVOn/kOb3kIpjxyObrqJI4gHvBg6zzFa3FGvt8n2/NWbwhDtljWWGzTcolxgd0VJC5yVFkkLq2WTcU2g9O0P/rymgzKuM1WlVCRPImgRJUkg9W3zf7wyIktUBd7cqzzcC1+n5zyhxamIG00Jkl2OAXdoI5h6VPZrPI8gLIdI6Cz+peXwipsCeNpBZjj7E71MsFlvO9Vjk6dZQZ7Yb1zIRcv7iUNssshA7nYecxaaJw1KLqDUBsXL2RtRzSUQ9TfGsPVTm1gLLXE7bmGJgoM/0ltg2Zn92pFIpzyLGtcdZna4HfqFiUpsmtplwp6gyX2M07KMFFvYO7bwB4FsRZPF0BTrSOL8JuJPC/A8mzE46S+s4xzL4W0MKbbiO0zVNW4RS2moh4nTNI51HoS0mTSEK8uuDQQ0sT1sG3ZlaT/P89FDbZCzKdFyZuxNsm5ON/7v0/jkltIvZn2mgx/f9HCP9MQBNvu+3RhFlANlP8gfEKbjUIvZ4KnrVG0r8JmRrbqG4DjExTwK+gkQEhDFaO9EkyqNq7SrFf2Iu32tjVonxlvvToUHTHZHHeMtgyIdS0hRTz0zMvf2W+8MriW3Gb8pT5qsTrKdZtjtDdWqqYLsAZOK87k8BPwL+CXwAicwNv7zheMSDH57Rd+jA2VpEoXcAv1SlvFXzOSJ0vVb/N19S8SzRe+iLRbM2XrNF9OizNGRraFaM6qQ+i+jQEiES9ZaRJraDLeWewLCTN3xcHjP7mnUOl6E/QkSKG7yl1LPXomM06bG4yLbJWOrYm0qlvIgjnS885THgp9oYX0BC6gN9ZI5FkX8F+HuM8h+FB4FfqXJ/CvBFhuO9JrP/a4geVuMACRJlV4RI0hMhC2/Q1SxKIe2x5LFR03RFrGilpImDec8atVoFfwvBmZr/Bstg7LXk0aztGDeJlVLPOy2k36WHKUqvy1Mnsz+bgI2+73f6vr9R3QYjYBLlMCRit1lFqkEVb7pVue9Sy5ht2d2H+FxKDSVZD/xElf6ztYPGaCNMMe69BwmPATFxvx2JV/uYim5JWUa6Qg3aUWTa3phZ2pzdVpSRJg7LLHpFu7ZpewFWnzi0FVnmctumu0A9LV3gJNJhIWuXTnyr1AFpJUqj6girgcuQaN/piA/lMs28HvhGLpdry+Vyps7wJBLBW444tE7FvYnAV5GAyVMYGTkwpCtdPxL2Px+4RK11q4EbVCQsF92MdCCuLWFAdMToMMFAWGDMbqWkiRuQC2Jk8FWWFbRQkvQYhOx5A9pmQR6ypCnc+bjWYogJTyhh18CI4MfzdKaZoTP4STrob0UCJ68A3g+ckM1m53ue59XV1ZnK4COIA7K+xMH5HzUGPAK8J5fLrfY8L2UQ+lk1Gpyms+JsZMNWU8jAMK4MgvToLLY2onPXqRy9qEBltE3TLA6lCUSWFREDvtg06RgZPK1icqeKK82hgbKCws2rgaFjWUR+C3SFWhoSR03jSLrMema0Lu2apiX03KuVeJkiDAXdmsdSbZsmTb/WmCRHbAW+QAswy1DQ92nifblc7hDP81KWcPdBxMG4g8KigaMwqMr7RPb39gfw1SpXq6KfZxBtO/BlxBFpf4CYAU0LnkN8uPr/JIoJsw9wLbKR6nxlVq1amurRQMSAGJb9IDV6z6FvRN2QkJuwKPYaErS5HjFrP+jGvEOSCBNlN3CVLoWzkDeqtCJbcfPtTylnFSkHPmK+DhS9PiTyeNB1rUOliAISoPgiEmyYVl1lGhKfdVBoBj9AFf3Jg4OD3tDQ0LZUKnUfw98siSJTvf7dW4B1LKv5HKPPHb1z587a+vr6LY2NjX1IGMxdyH6UhynOydnjuj5WD4nSKf5vUcjriupVAQz0gSBU/mzgvKGhocm5XG5XbW3tlSq+1UUo855e8yhs78ig3nss8E1g1sDAAHV1db9uaGi4QpWw7a4LHd6MFcWGfQxv5w1jG3BgTU3N+Yi390Tgb4iVLMnP0T2m1o2Dx40bNwHZsnyI00Mc3kjUdnV1lZq2X2f8uarHTNLf25FNWEmRJavi4FwVA6eqOLYbMRXvcd3oUGmU+zb7DUhQo6/i1pGIw/Askvs+ZCAr34W8/BslSifi+znQdaNDtRPlFR3AwTdQRunKshyxmiWJlYyM95mOOAA/z/7RqA4OVUUUkODEq5Q0IJaqCcjLuc9OsKy9wH2M/FjQNOBLSLDmaNedDtVMlD2qxId3NB6gCvfFSMxYUrgG+JNxLlhZTk6oPg4OFSEKSIj0JYgljJAYdjTi6Z+XUD4vKCm3MPJ7jbORz3TPdF3qUM1EAQl9v5nh6OHg2ymnqh4xNaF8fot8os6MBJirutEM160O1UyUrcg++y2IlzzwZI5GPiW3BInTKhevIZa2Gxjp3feQsPyLsL9oz8GhZJTjR7HhJcSPMp/hkJeALDNV4U/CUfgy4luZwcgPCoGYqLNIqL7zsThUJVFyiLMxp6JQ+MM+45Bgy77QPeXqK3sRr32jkqVGdaPDdYXbw/CrXx0cqkL0CjCIfIqum2GTcXi276D475/YMIRsU77OyMfTVeYcPRwcqpIowcqyEtkfYr7FcXyC+e5EfDg3sX9o/UwSfImzgyNKpfCEzvb3MPxu4CwSjpLk57S3At9GgjEDcW4v8lLx9a6LHZJAXYWffzMSizUG2f14GxKj9WrC+bwEXIpY1Y5Wg8FKJPLYwaFs/HcAFxKGcNXflm4AAAAASUVORK5CYII=> 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class=ch-title>易学易用的分布式深度学习平台<span></span></span></h1> <p>正在为100+项产品提供深度学习算法支持</p> <div> <a class=quick-start href=http://book.paddlepaddle.org/index.cn.html target=_blank>快速入门</a> </div> <div> <div class=github-counter> <span><i class="fa fa-star" aria-hidden=true></i>Star</span> <span id=star-counter></span> </div> <div class=github-counter> <span><i class="fa fa-code-fork" aria-hidden=true></i>Fork</span> <span id=fork-counter></span> </div> </div> </div> </header> <section class=services> <div class=row> <h2><span>丰富的算法服务</span></h2> </div> <div class=row> <div> <img class=service-icon src=./images/service-1.png> </div> <div> <div class=service-desc> <h3>机器视觉</h3> <p>卷积神经网络可以识别图像中的主要对象,并输出分类结果</p> <div> <a role=button class=view-more href=http://book.paddlepaddle.org/03.image_classification/index.cn.html target=_blank>查看更多</a> </div> </div> </div> </div> <div class=row> <div> <div class=service-desc> <h3>自然语言理解</h3> <p>利用LSTM网络从IMDB电影评论的中分析出评论者情绪的正面和负面</p> <div> <a role=button 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src=data:image/png;base64,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> </div> <h3>易用性</h3> <p>为用户提供了直观、灵活的数据接口和模型配置接口</p> </div> <div class=feature-desc> <div class=feature-icon> <img 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> </div> <h3>灵活性</h3> <p>支持CNN、RNN等多种神经网络结构和优化算法。简单书写配置文件即可实现复杂模型</p> </div> <div class=feature-desc> <div class=feature-icon> <img src=data:image/png;base64,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> </div> <h3>高效性</h3> <p>在计算、存储、通信、架构等方面都做了高效优化，充分发挥各种资源的性能</p> </div> <div class=feature-desc> <div class=feature-icon> <img src=data:image/png;base64,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> </div> <h3>扩展性</h3> <p>全面支持多核、多GPU、多机环境。轻松应对大规模数据训练需求</p> </div> </div> </section> <section class=get-started> <div class=row> <h2>现在开始使用PaddlePaddle</h2> <p>易学易用的分布式深度学习平台</p> <div> <a role=button class=quick-start href=http://book.paddlepaddle.org/index.cn.html target=_blank>快速入门</a> </div> </div> </section> <footer class=footer-nav> <div class=row> <div class=tr-code> <img src=./images/pr-code.png> <p>PaddlePaddle 微信公众号</p> </div> <div class=contact-us> <img 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> 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class=ch-title>易学易用的分布式深度学习平台<span></span></span></h1> <p>正在为100+项产品提供深度学习算法支持</p> <div> <a class=quick-start href=http://book.paddlepaddle.org/index.cn.html target=_blank>快速入门</a> </div> <div> <div class=github-counter> <span><i class="fa fa-star" aria-hidden=true></i>Star</span> <span id=star-counter></span> </div> <div class=github-counter> <span><i class="fa fa-code-fork" aria-hidden=true></i>Fork</span> <span id=fork-counter></span> </div> </div> </div> </header> <section class=services> <div class=row> <h2><span>丰富的算法服务</span></h2> </div> <div class=row> <div> <img class=service-icon src=./images/service-1.png> </div> <div> <div class=service-desc> <h3>机器视觉</h3> <p>卷积神经网络可以识别图像中的主要对象,并输出分类结果</p> <div> <a role=button class=view-more href=http://book.paddlepaddle.org/03.image_classification/index.cn.html target=_blank>查看更多</a> </div> </div> </div> </div> <div class=row> <div> <div class=service-desc> <h3>自然语言理解</h3> <p>利用LSTM网络从IMDB电影评论的中分析出评论者情绪的正面和负面</p> <div> <a role=button 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src=data:image/png;base64,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> </div> <h3>易用性</h3> <p>为用户提供了直观、灵活的数据接口和模型配置接口</p> </div> <div class=feature-desc> <div class=feature-icon> <img 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> </div> <h3>灵活性</h3> <p>支持CNN、RNN等多种神经网络结构和优化算法。简单书写配置文件即可实现复杂模型</p> </div> <div class=feature-desc> <div class=feature-icon> <img src=data:image/png;base64,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> </div> <h3>高效性</h3> <p>在计算、存储、通信、架构等方面都做了高效优化，充分发挥各种资源的性能</p> </div> <div class=feature-desc> <div class=feature-icon> <img src=data:image/png;base64,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> </div> <h3>扩展性</h3> <p>全面支持多核、多GPU、多机环境。轻松应对大规模数据训练需求</p> </div> </div> </section> <section class=get-started> <div class=row> <h2>现在开始使用PaddlePaddle</h2> <p>易学易用的分布式深度学习平台</p> <div> <a role=button class=quick-start href=http://book.paddlepaddle.org/index.cn.html target=_blank>快速入门</a> </div> </div> </section> <footer class=footer-nav> <div class=row> <div class=tr-code> <img src=./images/pr-code.png> <p>PaddlePaddle 微信公众号</p> </div> <div class=contact-us> <img 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src=data:image/png;base64,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> 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Platform</h1> <p>Providing deep learning algorithms for 100+ products</p> <div> <a class=quick-start href=http://book.paddlepaddle.org/index.html target=_blank>Quick Start</a> </div> <div> <div class=github-counter> <span><i class="fa fa-star" aria-hidden=true></i>Star</span> <span id=star-counter></span> </div> <div class=github-counter> <span><i class="fa fa-code-fork" aria-hidden=true></i>Fork</span> <span id=fork-counter></span> </div> </div> </div> </header> <section class=services> <div class=row> <h2><span>Extensive Algorithmic Service</span></h2> </div> <div class=row> <div> <img class=service-icon src=./images/service-1.png> </div> <div> <div class=service-desc> <h3>Machine Vision</h3> <p>The convoluted neural network can identify the main object in the image and output the classification result</p> <div> <a role=button class=view-more href=http://book.paddlepaddle.org/03.image_classification/index.html target=_blank>Read more</a> </div> </div> </div> </div> <div class=row> <div> <div class=service-desc> <h3>Natural Language Understanding</h3> <p>Using the LSTM network to analyze the positive and negative aspects of the commenter's emotions from IMDB film review</p> <div> <a role=button class=view-more href=http://book.paddlepaddle.org/06.understand_sentiment/index.html target=_blank>Read more</a> </div> </div> </div> <div> <img class=service-icon src=./images/service-2.png> </div> </div> <div class=row> <div> <img class=service-icon src=./images/service-3.png> </div> <div> <div class=service-desc> <h3>Search Engine Ranking</h3> <p>Analyze user characteristics, movie features, rating scores, predict new users' ratings for different movies</p> <div> <a role=button class=view-more href=http://book.paddlepaddle.org/05.recommender_system/index.html target=_blank>Read more</a> </div> </div> </div> </div> </section> <section class=features> <div class=row> <h2><span>Technology and Service Advantages</span></h2> </div> <div class=row> <div class=feature-desc> <div class=feature-icon> <img src=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAGQAAABkCAYAAABw4pVUAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAKTWlDQ1BQaG90b3Nob3AgSUNDIHByb2ZpbGUAAHjanVN3WJP3Fj7f92UPVkLY8LGXbIEAIiOsCMgQWaIQkgBhhBASQMWFiApWFBURnEhVxILVCkidiOKgKLhnQYqIWotVXDjuH9yntX167+3t+9f7vOec5/zOec8PgBESJpHmomoAOVKFPDrYH49PSMTJvYACFUjgBCAQ5svCZwXFAADwA3l4fnSwP/wBr28AAgBw1S4kEsfh/4O6UCZXACCRAOAiEucLAZBSAMguVMgUAMgYALBTs2QKAJQAAGx5fEIiAKoNAOz0ST4FANipk9wXANiiHKkIAI0BAJkoRyQCQLsAYFWBUiwCwMIAoKxAIi4EwK4BgFm2MkcCgL0FAHaOWJAPQGAAgJlCLMwAIDgCAEMeE80DIEwDoDDSv+CpX3CFuEgBAMDLlc2XS9IzFLiV0Bp38vDg4iHiwmyxQmEXKRBmCeQinJebIxNI5wNMzgwAABr50cH+OD+Q5+bk4eZm52zv9MWi/mvwbyI+IfHf/ryMAgQAEE7P79pf5eXWA3DHAbB1v2upWwDaVgBo3/ldM9sJoFoK0Hr5i3k4/EAenqFQyDwdHAoLC+0lYqG9MOOLPv8z4W/gi372/EAe/tt68ABxmkCZrcCjg/1xYW52rlKO58sEQjFu9+cj/seFf/2OKdHiNLFcLBWK8ViJuFAiTcd5uVKRRCHJleIS6X8y8R+W/QmTdw0ArIZPwE62B7XLbMB+7gECiw5Y0nYAQH7zLYwaC5EAEGc0Mnn3AACTv/mPQCsBAM2XpOMAALzoGFyolBdMxggAAESggSqwQQcMwRSswA6cwR28wBcCYQZEQAwkwDwQQgbkgBwKoRiWQRlUwDrYBLWwAxqgEZrhELTBMTgN5+ASXIHrcBcGYBiewhi8hgkEQcgIE2EhOogRYo7YIs4IF5mOBCJhSDSSgKQg6YgUUSLFyHKkAqlCapFdSCPyLXIUOY1cQPqQ28ggMor8irxHMZSBslED1AJ1QLmoHxqKxqBz0XQ0D12AlqJr0Rq0Hj2AtqKn0UvodXQAfYqOY4DRMQ5mjNlhXIyHRWCJWBomxxZj5Vg1Vo81Yx1YN3YVG8CeYe8IJAKLgBPsCF6EEMJsgpCQR1hMWEOoJewjtBK6CFcJg4Qxwicik6hPtCV6EvnEeGI6sZBYRqwm7iEeIZ4lXicOE1+TSCQOyZLkTgohJZAySQtJa0jbSC2kU6Q+0hBpnEwm65Btyd7kCLKArCCXkbeQD5BPkvvJw+S3FDrFiOJMCaIkUqSUEko1ZT/lBKWfMkKZoKpRzame1AiqiDqfWkltoHZQL1OHqRM0dZolzZsWQ8ukLaPV0JppZ2n3aC/pdLoJ3YMeRZfQl9Jr6Afp5+mD9HcMDYYNg8dIYigZaxl7GacYtxkvmUymBdOXmchUMNcyG5lnmA+Yb1VYKvYqfBWRyhKVOpVWlX6V56pUVXNVP9V5qgtUq1UPq15WfaZGVbNQ46kJ1Bar1akdVbupNq7OUndSj1DPUV+jvl/9gvpjDbKGhUaghkijVGO3xhmNIRbGMmXxWELWclYD6yxrmE1iW7L57Ex2Bfsbdi97TFNDc6pmrGaRZp3mcc0BDsax4PA52ZxKziHODc57LQMtPy2x1mqtZq1+rTfaetq+2mLtcu0W7eva73VwnUCdLJ31Om0693UJuja6UbqFutt1z+o+02PreekJ9cr1Dund0Uf1bfSj9Rfq79bv0R83MDQINpAZbDE4Y/DMkGPoa5hpuNHwhOGoEctoupHEaKPRSaMnuCbuh2fjNXgXPmasbxxirDTeZdxrPGFiaTLbpMSkxeS+Kc2Ua5pmutG003TMzMgs3KzYrMnsjjnVnGueYb7ZvNv8jYWlRZzFSos2i8eW2pZ8ywWWTZb3rJhWPlZ5VvVW16xJ1lzrLOtt1ldsUBtXmwybOpvLtqitm63Edptt3xTiFI8p0in1U27aMez87ArsmuwG7Tn2YfYl9m32zx3MHBId1jt0O3xydHXMdmxwvOuk4TTDqcSpw+lXZxtnoXOd8zUXpkuQyxKXdpcXU22niqdun3rLleUa7rrStdP1o5u7m9yt2W3U3cw9xX2r+00umxvJXcM970H08PdY4nHM452nm6fC85DnL152Xlle+70eT7OcJp7WMG3I28Rb4L3Le2A6Pj1l+s7pAz7GPgKfep+Hvqa+It89viN+1n6Zfgf8nvs7+sv9j/i/4XnyFvFOBWABwQHlAb2BGoGzA2sDHwSZBKUHNQWNBbsGLww+FUIMCQ1ZH3KTb8AX8hv5YzPcZyya0RXKCJ0VWhv6MMwmTB7WEY6GzwjfEH5vpvlM6cy2CIjgR2yIuB9pGZkX+X0UKSoyqi7qUbRTdHF09yzWrORZ+2e9jvGPqYy5O9tqtnJ2Z6xqbFJsY+ybuIC4qriBeIf4RfGXEnQTJAntieTE2MQ9ieNzAudsmjOc5JpUlnRjruXcorkX5unOy553PFk1WZB8OIWYEpeyP+WDIEJQLxhP5aduTR0T8oSbhU9FvqKNolGxt7hKPJLmnVaV9jjdO31D+miGT0Z1xjMJT1IreZEZkrkj801WRNberM/ZcdktOZSclJyjUg1plrQr1zC3KLdPZisrkw3keeZtyhuTh8r35CP5c/PbFWyFTNGjtFKuUA4WTC+oK3hbGFt4uEi9SFrUM99m/ur5IwuCFny9kLBQuLCz2Lh4WfHgIr9FuxYji1MXdy4xXVK6ZHhp8NJ9y2jLspb9UOJYUlXyannc8o5Sg9KlpUMrglc0lamUycturvRauWMVYZVkVe9ql9VbVn8qF5VfrHCsqK74sEa45uJXTl/VfPV5bdra3kq3yu3rSOuk626s91m/r0q9akHV0IbwDa0b8Y3lG19tSt50oXpq9Y7NtM3KzQM1YTXtW8y2rNvyoTaj9nqdf13LVv2tq7e+2Sba1r/dd3vzDoMdFTve75TsvLUreFdrvUV99W7S7oLdjxpiG7q/5n7duEd3T8Wej3ulewf2Re/ranRvbNyvv7+yCW1SNo0eSDpw5ZuAb9qb7Zp3tXBaKg7CQeXBJ9+mfHvjUOihzsPcw83fmX+39QjrSHkr0jq/dawto22gPaG97+iMo50dXh1Hvrf/fu8x42N1xzWPV56gnSg98fnkgpPjp2Snnp1OPz3Umdx590z8mWtdUV29Z0PPnj8XdO5Mt1/3yfPe549d8Lxw9CL3Ytslt0utPa49R35w/eFIr1tv62X3y+1XPK509E3rO9Hv03/6asDVc9f41y5dn3m978bsG7duJt0cuCW69fh29u0XdwruTNxdeo94r/y+2v3qB/oP6n+0/rFlwG3g+GDAYM/DWQ/vDgmHnv6U/9OH4dJHzEfVI0YjjY+dHx8bDRq98mTOk+GnsqcTz8p+Vv9563Or59/94vtLz1j82PAL+YvPv655qfNy76uprzrHI8cfvM55PfGm/K3O233vuO+638e9H5ko/ED+UPPR+mPHp9BP9z7nfP78L/eE8/sl0p8zAAAAIGNIUk0AAHolAACAgwAA+f8AAIDpAAB1MAAA6mAAADqYAAAXb5JfxUYAAAdDSURBVHja7J1dbBRVFMd/O7ulFrFQaBEIkAKlIFAUkO+PgMZA6AvGRAxUjKHWCEYTTfDRjzd8wPjQmljRBBBDVNIYMUEJkdpqhUpCq0ChQKWtBVqlYIUWuosP92671m73a2b2zuz9JZt+ZHfnnvO/d+bOnXPO9TS3tqM4OcBcYDYwFcgFxgHZwGggDRgh39sF3AX+AjqAK0ATcBH4DagDlDbYp2Cb8oC1wHL5mhzDZ4PCZAHTwrznMlAtX4eBRpWM9ygyQmYCRcBGKYidNAIHgH3A2VQWZDSwSQqxWJEO+rMUZr887aWEIDOA14AtwH2Knsq7gT3ALqDBzgMbNh4rX/a+00CJwmIg21Yi2/qpbLtrBMkGSuUsZ7PNncAM/2ySbS+VtjhWEA+wFTgPbFN0RhfLbHSbtKVY2uYoQXKBo8BHwCjcwyigXNqW6xRBioBTwGrcy2ppY5HKgqTL3rMXyMT9ZEpby6XtSgkyDvhBnl9TjWJp+3hVBJkjb6gWkrosBGqkL5IqyBKgktjWm9zKZOmLJckSZBXwHWIhTyPIkj5ZZbcgi4FD9K+uavoZAXxNnOtz8QhSgFi21mKE5wHpowKrBZkAfAOM1D6PyEjpq4lWCTIMOBjrAVKcicAX0nemC1KKOs8tnMRioMxsQYpS9KbPLLYS5TJLNA+ochHrNpnarwlxE3gYEXQR9wjxAB9rMUwhE/iECEv3RhRDbY0bvVNWkUZZRZrdh10d6dQ/lCBjgJ1u7a5NbR6a2jzJOPRO6duYBXkbERmiMZcs4J1YBckHXtS+s4wSwgROhBPkTZz9DFx1fMBb0QoyA3hG+8xyNiIiNiMK8jrOCtVxKgYiYHBIQbKx4MG9JixFDIj1GijIFiBD+8k2MoDnhhLkhVTwwq0e8XNYmhLNKQ4nyILBLjJu5PhpLwCTxgZUaM5M6fv/CfK024W43QNV9V4OHxeCrJwbUKVpfb4PXe09j/3JMqZx5neDylMGzdcM7tyN/P7HF/hZu8ivSvMbgemE3PxNc7IY357wcqTWG/F9w9LEaWrl3ACzcgMqmZAnNbgQFGSdk0fGkVovXgMKl/mZn+9neLojTVkHlAavISucKkjlKWFC4VI/KwocK0afBkFBljvVipZrwoT5M/w4nD5BcoBJTrWiR17AHTwygkwEcgxEUr5GDR4xEBUSNGrwkIEoV6FRg6kGMEX7QS1BJmg/KMN4AxtyrzVRk+Npbm2/jgWpy/4AHPrRy8lz3r7lbiczPB3m5/spXObHa93z1E4f4LXimw/95KWq3uuarntLrhT7fLB+iWU3oT4fIrnEdE42CDG2bbhL7vh7jhekqc1DWUUax097rRRkhGFljwJcIUaoHVaffg3gb30tVYYuA/BrPyhDrwF0aj8oww0DUb1TowbtPuAPu46244P+3Md3X7rjuP/bQJsBXNIdUxkueZpb218B3rdqNNjcw2wZ4Rba9KqBKPSoUYMzBiLDVqMGdQaiFnqL9kXSaQGuBuOyqrAhSUfPsoakCvrDgKp0B0061dAf2zsNk3cJ0LOsmMkDLgRHyAX50iSHPv+HLr8f1H5JGl8GfwkV5ID2S9L4PPhLaC76L4gNTSzLokpktmMWCs6yGoDawUYIiFrtGnspD/1jYL2sbMQeTRlmjQY9yxqS24h6vx3hRkgHYtMVjT3sY8DzqMGCHHYBAe0rywkA7w3852CCnNUzLls4AJyJRhAQlWp6tc8soxdRj4xoBTkHfJjIEYMZTUmq2mY6TVc8/7HLhJnVoLu/DVWVdAwidz2uYvtfVXupqvO6rmuvnudPNHKxE7Fu9WcsIwT5gTfiPWowKzYj3R1CZKTDigJTig3sCCdGpBECoqTpUdy9n5SdfA88BoSNr40U23sPeB5RBFiTGDelL4cMdo4m2LoJ2K79mTAvE6GqdbSCBO8od2ufxs1uxI5uEYllc+J0xB5Li7R/Y6IWUaUhqkSGWPJDeoCn0BEqsdACPBmtGLEKEjzAeuCG9nVEbkhfxdSB48mgqkeUEurSPg9Ll/RRfawfjDelrQYo1KKEFaNQ+gi7BEFe4J9AJ/yE0il9UhnvFySa9FmD2ETxstaCy9IXNYl8iRlZuPWI7UZPpLAYJ4Cl8VwzrBAEoE32jlS8edwtbTclE83MPPVuRJXmZ0mNta+biNLsxdJ2VBMkyD5gHnDMxWIckzbuNfuLrarkcBGxmViJy2ZhndKmNdJGnCIIiGXmckTF5jKc/Yy+V9owXdpkWb0QOzZu6UAs388B9uOsEKMA8Jls+3ZsyOm3cyedBmAzMAsRQNGtsBDdso2zgU2ECUiwgliW380mJ2SWoso2GWcR8c17ELmXtpNMQUJ5FLFJ1gbs3xSgEahABK7VJtsRqggSSh6wFlH+fCXm79/eilhrqgYOY3IqnxsFGchYRPXtOYiStlOABxGR+lmIzePvl+/9B7gDXJcX4KuI0iGXgF+BOuCaysb+OwCZ5+L5P1LViQAAAABJRU5ErkJggg==> </div> <h3>Ease of use</h3> <p>Provids an intuitive and flexible interface for loading data and specifying model structure.</p> </div> <div class=feature-desc> <div class=feature-icon> <img 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> </div> <h3>Flexibility</h3> <p>Supports CNN, RNN and other neural network. Easy to configure complex models.</p> </div> <div class=feature-desc> <div class=feature-icon> <img src=data:image/png;base64,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> </div> <h3>Efficiency</h3> <p>Efficient optimization of computing, memory, communications and architecture.</p> </div> <div class=feature-desc> <div class=feature-icon> <img src=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAGQAAABkCAYAAABw4pVUAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAKTWlDQ1BQaG90b3Nob3AgSUNDIHByb2ZpbGUAAHjanVN3WJP3Fj7f92UPVkLY8LGXbIEAIiOsCMgQWaIQkgBhhBASQMWFiApWFBURnEhVxILVCkidiOKgKLhnQYqIWotVXDjuH9yntX167+3t+9f7vOec5/zOec8PgBESJpHmomoAOVKFPDrYH49PSMTJvYACFUjgBCAQ5svCZwXFAADwA3l4fnSwP/wBr28AAgBw1S4kEsfh/4O6UCZXACCRAOAiEucLAZBSAMguVMgUAMgYALBTs2QKAJQAAGx5fEIiAKoNAOz0ST4FANipk9wXANiiHKkIAI0BAJkoRyQCQLsAYFWBUiwCwMIAoKxAIi4EwK4BgFm2MkcCgL0FAHaOWJAPQGAAgJlCLMwAIDgCAEMeE80DIEwDoDDSv+CpX3CFuEgBAMDLlc2XS9IzFLiV0Bp38vDg4iHiwmyxQmEXKRBmCeQinJebIxNI5wNMzgwAABr50cH+OD+Q5+bk4eZm52zv9MWi/mvwbyI+IfHf/ryMAgQAEE7P79pf5eXWA3DHAbB1v2upWwDaVgBo3/ldM9sJoFoK0Hr5i3k4/EAenqFQyDwdHAoLC+0lYqG9MOOLPv8z4W/gi372/EAe/tt68ABxmkCZrcCjg/1xYW52rlKO58sEQjFu9+cj/seFf/2OKdHiNLFcLBWK8ViJuFAiTcd5uVKRRCHJleIS6X8y8R+W/QmTdw0ArIZPwE62B7XLbMB+7gECiw5Y0nYAQH7zLYwaC5EAEGc0Mnn3AACTv/mPQCsBAM2XpOMAALzoGFyolBdMxggAAESggSqwQQcMwRSswA6cwR28wBcCYQZEQAwkwDwQQgbkgBwKoRiWQRlUwDrYBLWwAxqgEZrhELTBMTgN5+ASXIHrcBcGYBiewhi8hgkEQcgIE2EhOogRYo7YIs4IF5mOBCJhSDSSgKQg6YgUUSLFyHKkAqlCapFdSCPyLXIUOY1cQPqQ28ggMor8irxHMZSBslED1AJ1QLmoHxqKxqBz0XQ0D12AlqJr0Rq0Hj2AtqKn0UvodXQAfYqOY4DRMQ5mjNlhXIyHRWCJWBomxxZj5Vg1Vo81Yx1YN3YVG8CeYe8IJAKLgBPsCF6EEMJsgpCQR1hMWEOoJewjtBK6CFcJg4Qxwicik6hPtCV6EvnEeGI6sZBYRqwm7iEeIZ4lXicOE1+TSCQOyZLkTgohJZAySQtJa0jbSC2kU6Q+0hBpnEwm65Btyd7kCLKArCCXkbeQD5BPkvvJw+S3FDrFiOJMCaIkUqSUEko1ZT/lBKWfMkKZoKpRzame1AiqiDqfWkltoHZQL1OHqRM0dZolzZsWQ8ukLaPV0JppZ2n3aC/pdLoJ3YMeRZfQl9Jr6Afp5+mD9HcMDYYNg8dIYigZaxl7GacYtxkvmUymBdOXmchUMNcyG5lnmA+Yb1VYKvYqfBWRyhKVOpVWlX6V56pUVXNVP9V5qgtUq1UPq15WfaZGVbNQ46kJ1Bar1akdVbupNq7OUndSj1DPUV+jvl/9gvpjDbKGhUaghkijVGO3xhmNIRbGMmXxWELWclYD6yxrmE1iW7L57Ex2Bfsbdi97TFNDc6pmrGaRZp3mcc0BDsax4PA52ZxKziHODc57LQMtPy2x1mqtZq1+rTfaetq+2mLtcu0W7eva73VwnUCdLJ31Om0693UJuja6UbqFutt1z+o+02PreekJ9cr1Dund0Uf1bfSj9Rfq79bv0R83MDQINpAZbDE4Y/DMkGPoa5hpuNHwhOGoEctoupHEaKPRSaMnuCbuh2fjNXgXPmasbxxirDTeZdxrPGFiaTLbpMSkxeS+Kc2Ua5pmutG003TMzMgs3KzYrMnsjjnVnGueYb7ZvNv8jYWlRZzFSos2i8eW2pZ8ywWWTZb3rJhWPlZ5VvVW16xJ1lzrLOtt1ldsUBtXmwybOpvLtqitm63Edptt3xTiFI8p0in1U27aMez87ArsmuwG7Tn2YfYl9m32zx3MHBId1jt0O3xydHXMdmxwvOuk4TTDqcSpw+lXZxtnoXOd8zUXpkuQyxKXdpcXU22niqdun3rLleUa7rrStdP1o5u7m9yt2W3U3cw9xX2r+00umxvJXcM970H08PdY4nHM452nm6fC85DnL152Xlle+70eT7OcJp7WMG3I28Rb4L3Le2A6Pj1l+s7pAz7GPgKfep+Hvqa+It89viN+1n6Zfgf8nvs7+sv9j/i/4XnyFvFOBWABwQHlAb2BGoGzA2sDHwSZBKUHNQWNBbsGLww+FUIMCQ1ZH3KTb8AX8hv5YzPcZyya0RXKCJ0VWhv6MMwmTB7WEY6GzwjfEH5vpvlM6cy2CIjgR2yIuB9pGZkX+X0UKSoyqi7qUbRTdHF09yzWrORZ+2e9jvGPqYy5O9tqtnJ2Z6xqbFJsY+ybuIC4qriBeIf4RfGXEnQTJAntieTE2MQ9ieNzAudsmjOc5JpUlnRjruXcorkX5unOy553PFk1WZB8OIWYEpeyP+WDIEJQLxhP5aduTR0T8oSbhU9FvqKNolGxt7hKPJLmnVaV9jjdO31D+miGT0Z1xjMJT1IreZEZkrkj801WRNberM/ZcdktOZSclJyjUg1plrQr1zC3KLdPZisrkw3keeZtyhuTh8r35CP5c/PbFWyFTNGjtFKuUA4WTC+oK3hbGFt4uEi9SFrUM99m/ur5IwuCFny9kLBQuLCz2Lh4WfHgIr9FuxYji1MXdy4xXVK6ZHhp8NJ9y2jLspb9UOJYUlXyannc8o5Sg9KlpUMrglc0lamUycturvRauWMVYZVkVe9ql9VbVn8qF5VfrHCsqK74sEa45uJXTl/VfPV5bdra3kq3yu3rSOuk626s91m/r0q9akHV0IbwDa0b8Y3lG19tSt50oXpq9Y7NtM3KzQM1YTXtW8y2rNvyoTaj9nqdf13LVv2tq7e+2Sba1r/dd3vzDoMdFTve75TsvLUreFdrvUV99W7S7oLdjxpiG7q/5n7duEd3T8Wej3ulewf2Re/ranRvbNyvv7+yCW1SNo0eSDpw5ZuAb9qb7Zp3tXBaKg7CQeXBJ9+mfHvjUOihzsPcw83fmX+39QjrSHkr0jq/dawto22gPaG97+iMo50dXh1Hvrf/fu8x42N1xzWPV56gnSg98fnkgpPjp2Snnp1OPz3Umdx590z8mWtdUV29Z0PPnj8XdO5Mt1/3yfPe549d8Lxw9CL3Ytslt0utPa49R35w/eFIr1tv62X3y+1XPK509E3rO9Hv03/6asDVc9f41y5dn3m978bsG7duJt0cuCW69fh29u0XdwruTNxdeo94r/y+2v3qB/oP6n+0/rFlwG3g+GDAYM/DWQ/vDgmHnv6U/9OH4dJHzEfVI0YjjY+dHx8bDRq98mTOk+GnsqcTz8p+Vv9563Or59/94vtLz1j82PAL+YvPv655qfNy76uprzrHI8cfvM55PfGm/K3O233vuO+638e9H5ko/ED+UPPR+mPHp9BP9z7nfP78L/eE8/sl0p8zAAAAIGNIUk0AAHolAACAgwAA+f8AAIDpAAB1MAAA6mAAADqYAAAXb5JfxUYAAAorSURBVHja7J17cBXVHcc/u3sveQEJScyDEskQCO+HUBLQaCkoKPioI5RWUFsqfdhRqZ3pVDt9TqcdOoUq01q0tRURtIV2tAMVBEtB5BEjNQQJARIegQmPBAiPkOTe3e0fZy+EkMe9e3f3PrLfmczk3pl7ztnz3fM7v9/vex5S7cmzRDFuAb4KzAUKgT6AF5CD+K0OaMBV4DTwEfAKsCOaH1iKMkIGAz8B7gfSAMmmelqBQ8BvgDeiqQPkKGjDMGA94DM66TGgn41kAPQCRgIrjJFUB3yzJxOSDiwGLgOVwEzAE8F+yDHMmQqUAxN6CiFDga1APfADICXKTLgMjAHKgEbgyXglpBDYCRwA7rLZHFmFvsCfgCZgQbwQkgmsBaqAScQmkoDXgHNAcawSIgELjcnyEeID/YBdhtusxBIh+UAF8GqEJ2q7MBloAWbHAiHzDdd1JPENBVgDfBCthCQAK40/Dz0HU4ELRkYhagjJMXz3+fRMpAKngLujgZBRwH4jvujJkIFNwDORJGQS8LHhfbgQeAn4VSQIuQvYBiS6HNyE54EXnSSk2BieXrfvO8WzwC+dIGQ08F9EttRF1/gR8D07CekP/Mc1UyFhKTDLDkJ6Af9C5KZchIZ3EXKDpYT8gQhqBHEQ1ddYSch8IqALxGHwGFSapTtNPd8I/JLMtKLymMy2cpnaMzKtvtjouV5eyMvSuGusxvCBmtXFz0HIEaYIkYxJfIqZmt//WGFzmRLTr/W0CSozilQri/QbTlGnhXaVCPyGWTIqj8lsLlNQZJh1u8r4QpXkhNggoakF9hxUWL9D4YNPFPKydEbkWzZSPMB2RAo/pDkkA1hittZt5aLYWZNVSkbHDhkAyQlQMvr6yPhwr+WS0SRgYqiE/AyhKZvCiTOi2PFDVWIVRSPUG57FYmwIhZAhwFPh1Nbiu/62xSoCbW+xxxlJp5OFEx0R8lNs0tpffsfLy++4KTADy4IhZCjwqF0tOFoncbROcqkQSKGD1ZLtCfk+sbFmKl6wuCtCMoHH4/Gp25vK7j47iDTarfVqS8jjiMUK8Qfd+Av2s7P4S2eB4cJ4tQtPPewL6bPDGN7RCJmA2BbgwnlIwI/bE/JlJ2rOz9XJz9FdCm7Gd9qbrIcdMR1f8rld3zFy2o6QAiM6twwJhsPS1BK7PRRoey9nnC8JmBcg5F6rSx+QJbKje6piN/1eul+0PS9Lc6rK5wKElFhd8p1jxEOs36mwvULhagyNlKstsL1CYWOpcsOzOIBhIASq40Ce1aVvLBV6QizDBoGqW9Ml20EGgOQmYMzgdlu2DriKoWkssCXN7iqGpvF5W2pzFUPT6G9Lba5iaBp9HaXfVQy7hdfR/YCuWtgtZNntgyhjxO0ClxAXLiEuIS5MwlEvKz9Xj+Rigphor6OExJpiGIn22mKy4kExjBA0W0bIgCyN6pMye6oUSsZEVz7r7AWJ3ftlDp2QabgooaqQ2ltnyACdcUM0CvprkWyeT6o9edZyK7n/qMzr73mupd8nFKokRTiv5fPDup0edn8mo3XxxEMGaHxlmkqf5IhMdvW2EALWKYb5uXrYtry5FV5b5+XYaQlFhuIRKuMGa+Rk6Hg9UH9BoqJG5sO9Qm5O662z8AE/t6Q5Tkq5bZO6ZYqhHv7PV2/ycOy0RHpfnSfu9ZObcWOh2ek62ekqk0eqvLHRy5E6idff8/D0Iz4SnT2zosyWEVJ5TOav//ZERDE8Uidx6IRMcwtkpIJfFYstUhLhmdk++vXRuzVty9/1UntGomi4xuwpficJmeIBTgADrCy1vWLoBK62wOrNHqqOd+w4Plji75YMEPnvudP8LH3bS1mVzD0TJVJTHDNdW2XErlBL4bRiqOuwcqMgIyURptym8sAdKoV51z2mUExPVprOqEEamgYVNY4lM5oCcYjlhDitGO6tljl8UqZvis6zc3zMnKRy5xiVJ+/3c98k8VKs2xGagxFY2FBz0jENpyoQqW9wqsaAWmh1BPzpYfEWTyi8OYaYcpvKxSvQJzn48i5clq4pRY1XHCNkSYCQauAw4qoIW2GHYnj+kkTtGVHulv8pbCtX+OE8H6m9hd2XgIdKgjedjZclFq/yohrcNlyUOH9JCmr+CdMZXNU2dfLPWMwzVFTLLPmbl4vGW5zWW2forRrJieY7LzlRlJFmENrUDL9928vealvnklOBfwJxyN8RtxXEDA4cl1m1yYOmw9gCjRnFKpmp4b/FXg987T7h6tY3SmzcrVBeLbN6k4deXj/DbrUltbI88E+A9k8QNxfEBFp9sGaLgqbD1PEq86b7LSGjPTJTdeZN9zN1vIqmizptONVIB37RnhCAP8cKIaWVCpeaJAZm68wott+1nlGsMjBb51KTRGml5QvIK9t+aEvICsQh81GPfUdEs0vGqI5sqpeMusCWuGRBZ4TUY/MFWVbtMaxrEDQM6u9c8i9Q16lzlr4CF4Ddbb9on1xcijjOz5YXz6r4I7ABKBxvyoz31TbotQjPt/+i/fg7ALwVbi12K4a9k0TnXHQuaLtWV6BuC3ClrXfVGSEAP0dcyGgadu8xHGiYvYO1zhESqCsvyzJCFnX0ZUeEHAT+GE5Ndu8xHFsgyt/6qYLPgey4zy/qAhg72JI45FxnXm1nh2BmAEcQV52agp2Koa7Di2u81DVIjBqk8ejdfjw2bWf0qyKtv69GJjdDZ9EcnxXiWxHiZomgRghAA+KoJvO+e5HK12f6KficFt5e7w4shCTB/Ol+khJgX43MsrVeyqtlmpqtI6KpWWSRl631sq9GJilB1GkBGbs6I6OrERJwv7cAX4jWeOT0OYkVGzzUN9o7l2SmCuk3Oz3s+aPbY2JtPUjZCagalO6XKa9WOHVOsmyUJCdCTrrO2AKVohEaijXxYFgHKQcwH3HZl4vwsAVxiViXCIb3NxE3Xbowj8ZgyAiWEIDvAnvcfjUFjRAO9wmWkBbgIUS+y0VoeBA4azUhIJYLTQWa3T4OGs8B60P5Qai+QwXwRaDV7etu8Wvgd6H+yIwztwu4B3CPh+scLwEvmPmhWe96G+IuQ9d8dTwyFpn9cTjhzi5ETua8y8ENc8YL4RQQbvxZgbiqu8p1bZlhZs6wmhCAOmAcsLoHB339gfetKMwqxb4ZcarmY4gEWk/BFsT57aetKtDqJRRvIq68qIxzIlTEtR5TrS7YjvWRNca88u04HS27EJcWvGVH4XYtWNWBV4Bc4B9xQsR5xIVek+lCz4hWQgKoB2YjzqTdHaNEXEXcHJHuxDM4tT2oyni7hhlBZSwcsHEJ+BaQjIPLbJ0+fKYKIQlnA78n+pau6kYb70BcG/iq0w2I1GlAZ4GnEfryROAjO+1yEGjg+u10w4AdkWpINBzPVIY4f96DEHJWGhOonWatFfgMeAKxmCOTNlsCIolgNPVIIsvw9+caZPUBvAS39lg3UhrNwBnjrV+ODZtcrcT/BwCq4yQEbCXfVgAAAABJRU5ErkJggg==> </div> <h3>Scalability</h3> <p>Easy to use many CPUs/GPUs and machines to speed up your training and handle large-scale data easily.</p> </div> </div> </section> <section class=get-started> <div class=row> <h2>Start Using PaddlePaddle</h2> <p>Easy to Learn and Use Distributed Deep Learning Platform</p> <div> <a role=button class=quick-start href=http://book.paddlepaddle.org/index.html target=_blank>Quick Start</a> </div> </div> </section> <footer class=footer-nav> <div class=row> <div class=contact-us> <img 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> 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<div> <div class=service-desc> <h3>Natural Language Understanding</h3> <p>Using the LSTM network to analyze the positive and negative aspects of the commenter's emotions from IMDB film review</p> <div> <a role=button class=view-more href=http://book.paddlepaddle.org/06.understand_sentiment/index.html target=_blank>Read more</a> </div> </div> </div> <div> <img class=service-icon src=./images/service-2.png> </div> </div> <div class=row> <div> <img class=service-icon src=./images/service-3.png> </div> <div> <div class=service-desc> <h3>Search Engine Ranking</h3> <p>Analyze user characteristics, movie features, rating scores, predict new users' ratings for different movies</p> <div> <a role=button class=view-more href=http://book.paddlepaddle.org/05.recommender_system/index.html target=_blank>Read more</a> </div> </div> </div> </div> </section> <section class=features> <div class=row> <h2><span>Technology and Service Advantages</span></h2> </div> <div class=row> <div class=feature-desc> <div class=feature-icon> <img src=data:image/png;base64,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> </div> <h3>Ease of use</h3> <p>Provids an intuitive and flexible interface for loading data and specifying model structure.</p> </div> <div class=feature-desc> <div class=feature-icon> <img 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> </div> <h3>Flexibility</h3> <p>Supports CNN, RNN and other neural network. Easy to configure complex models.</p> </div> <div class=feature-desc> <div class=feature-icon> <img src=data:image/png;base64,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> </div> <h3>Efficiency</h3> <p>Efficient optimization of computing, memory, communications and architecture.</p> </div> <div class=feature-desc> <div class=feature-icon> <img src=data:image/png;base64,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> </div> <h3>Scalability</h3> <p>Easy to use many CPUs/GPUs and machines to speed up your training and handle large-scale data easily.</p> </div> </div> </section> <section class=get-started> <div class=row> <h2>Start Using PaddlePaddle</h2> <p>Easy to Learn and Use Distributed Deep Learning Platform</p> <div> <a role=button class=quick-start href=http://book.paddlepaddle.org/index.html target=_blank>Quick Start</a> </div> </div> </section> <footer class=footer-nav> <div class=row> <div class=contact-us> <img src=data:image/png;base64,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> 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