循环神经网络（RNN）旨在处理序列数据，而传统的神经网络则独立处理每个输入。RNN维护一个“隐藏状态”，充当记忆，总结先前步骤的信息以告知当前步骤。这使得它们能够理解数据中的上下文，其中顺序至关重要，例如文本、时间序列和语音。 AI
影响 解释了处理序列数据的基本架构，这对于自然语言处理和时间序列分析等许多AI应用至关重要。
排序理由 该条目是一篇关于特定类型神经网络架构的解释性文章。
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RNNs详解：神经网络如何理解序列数据
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RNNs详解：神经网络如何理解序列数据
<h4><em>CNNs learned to look at images. RNNs learn to read, listen and follow along.</em></h4><h3>Introduction</h3><p>In the last article, CNNs showed us how a network can understand <em>where</em> things are in an image. But some data isn’t about space at all. It’s about <strong…