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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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              <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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              <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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              <div class="media-content">TensorFlow系列推荐阅读：TensorFlow(一)——基础图模型TensorFlow(二)——逻辑回归TensorFlow(三)——卷积神经网络用于手写字识别TensorFlow(四)——构建自动编码器用于数据压缩及复原卷积神经网络系列推荐阅读：我所理解的深度学习（一）——BP图模型算法我所理解的深度学习（</div>
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              <div class="media-content">1GenerativeModelvsDiscriminativeModel首先，我们要明白机器学习中主要有两种模型，生成模型和判别模型。顾名思义，判别模型就是判别输入数据属于哪一类，例如卷积神经网络可以判别一幅图是不是包含猫；而生成模型并不知道数据是属于哪一类，它的作用是通过熟悉输入数据的特定分布，来产生一组新数据与</div>
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              <div class="media-content">TensorFlow系列延伸阅读：TensorFlow(一)——基础图模型TensorFlow(二)——逻辑回归TensorFlow(三)——卷积神经网络用于手写字识别1.什么是自动编码器自动编码器（Autoencoder）是一种数据压缩模型，它可以将输入数据编码成另外一种较小维度的表示，而后解码器将编码后的版本还原</div>
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              <h4 class="media-heading"><a href="http://testbbs.imefuture.com/article/4398" target="_blank">TensorFlow(三)——卷积神经网络用于手写字识别</a></h4>
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