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              <h4 class="media-heading"><a href="http://testbbs.imefuture.com/article/4408" target="_blank">漫谈RNN之序列建模（机器翻译篇）</a></h4>
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                <li class="label-item"><i class="glyphicon glyphicon-calendar"></i>2016-12-09 13:10</li>
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                                    <a href="http://testbbs.imefuture.com/topic/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0" data-id="889">深度学习</a>
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              <div class="media-content">本期讲解运用LSTM进行序列建模，并且介绍了机器翻译的原理，阅读本文可以参考以下几篇历史文章：《漫谈RNN之基本概念》《漫谈RNN之训练方法》《漫谈RNN之梯度消失及梯度爆炸》《漫谈RNN之长短期记忆模型LSTM》《漫谈RNN之长短期记忆模型LSTM（续）》1.原理概述序列学习其实是深度学习中的一个应用非常广泛的概念</div>
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              <h4 class="media-heading"><a href="http://testbbs.imefuture.com/article/4407" target="_blank">漫谈RNN之长短期记忆模型LSTM（续）</a></h4>
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                <li class="label-item"><i class="glyphicon glyphicon-calendar"></i>2016-12-09 11:46</li>
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                                    <a href="http://testbbs.imefuture.com/topic/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0" data-id="889">深度学习</a>
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              <div class="media-content">本期讲解LSTM为什么可以取代RNN从而解决了梯度消失的问题，阅读本文可以参考以下几篇历史文章：《漫谈RNN之基本概念》《漫谈RNN之训练方法》《漫谈RNN之梯度消失及梯度爆炸》《漫谈RNN之长短期记忆模型LSTM》在《漫谈RNN之梯度消失及梯度爆炸》一文中，已经阐述了为什么RNN会引起梯度消失的问题，我觉得总结起来</div>
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              <h4 class="media-heading"><a href="http://testbbs.imefuture.com/article/4406" target="_blank">漫谈RNN之长短期记忆模型LSTM</a></h4>
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                <li class="label-item"><i class="glyphicon glyphicon-calendar"></i>2016-12-09 11:40</li>
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                                    <a href="http://testbbs.imefuture.com/topic/LSTM%E6%A8%A1%E5%9E%8B" data-id="3495">LSTM模型</a>
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              <div class="media-content">正因为RNN的梯度消失问题导致RNN的时间记忆性不够长，因此HochreiterSepp,JürgenSchmidhuber等人于1995年发明了长短期记忆网络（LongShort-termMemory，LSTM）来克服RNN的记忆缺陷，本期将介绍什么是LSTM模型，下期将介绍LSTM为什么可以解决RNN的记忆缺陷问</div>
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              <h4 class="media-heading"><a href="http://testbbs.imefuture.com/article/4405" target="_blank">漫谈RNN之梯度消失及梯度爆炸</a></h4>
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                <li class="label-item"><i class="glyphicon glyphicon-calendar"></i>2016-12-09 11:31</li>
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              <div class="media-content">昨天提到RNN的训练方法，提到了最流行的算法BPTT，其实训练RNN除了BPTT以外，还有两种算法：Real-timeRecurrentLearning（RTRL）以及ExtendedKalmanFiltering（EKF），不过工业上基本上都被BPTT占领了，所以后面两个算法就不再介绍。那么本期将讲解为什么训练RN</div>
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              <h4 class="media-heading"><a href="http://testbbs.imefuture.com/article/4404" target="_blank">漫谈RNN之训练方法</a></h4>
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                <li class="label-item"><i class="glyphicon glyphicon-calendar"></i>2016-12-09 11:22</li>
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                                    <a href="http://testbbs.imefuture.com/topic/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0" data-id="889">深度学习</a>
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              <div class="media-content">RNN的训练因其递归性而变得复杂，现在深度学习大多系统中通常采用的是Back-propagationThroughTime算法，本文不打算运用公式来讲解，而是将公式转化成易懂的文字来描述这些算法。阅读本期可以参考我的历史文章：《漫谈RNN之基本概念》《我所理解的深度学习（一）——BP图模型算法》RNN是一个循环递归网</div>
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              <h4 class="media-heading"><a href="http://testbbs.imefuture.com/article/4403" target="_blank">漫谈RNN之基本概念</a></h4>
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                <li class="label-item"><i class="glyphicon glyphicon-calendar"></i>2016-12-09 11:08</li>
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                                    <a href="http://testbbs.imefuture.com/topic/RNN%E7%9A%84%E5%9F%BA%E6%9C%AC%E6%A6%82%E5%BF%B5" data-id="3492">RNN的基本概念</a>
                                    <a href="http://testbbs.imefuture.com/topic/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0" data-id="889">深度学习</a>
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              <div class="media-content">RNN是一个图灵完全的模型，便意味着只要设计好RNN的外部存储机制，RNN模型的应用之广泛将远超我们的想象。为了更好地了解时序模型，从这期开始谈谈以RNN为核心的可扩展时序模型的研究状况及应用。在神经网络模型中，一般分为非时序模型和时序模型，对于非时序模型而言，其应用场景有单一手写字识别、图像物体分类等等，最基本的模</div>
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              <h4 class="media-heading"><a href="http://testbbs.imefuture.com/article/4402" target="_blank">Tensorflow|如何保存或导入训练好的模型</a></h4>
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                <li class="label-item"><i class="glyphicon glyphicon-calendar"></i>2016-12-09 10:46</li>
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              <div class="media-content"> 在深度学习实践中，我们通常要先搭建好模型如何经过长时间的训练才能使用。那么，对于训练好的模型，我们自然想把它保存起来以便调用。  曾经，我写过一个比较大的深度学习模型，由于每次迭代需要长达1-2个小时，并且用的是nohup在服务器后台上运行，但是鉴于nohup有时会不稳定，因此我的后台程序随时可能会被kill掉，于</div>
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