We describe recurrent neural networks (RNNs), which have attracted great attention on sequential tasks, such as handwriting recognition, speech recognition and image to text. However, compared to general feedforward neural networks, RNNs have feedback loops, which makes it a little hard to understand the backpropagation step. Thus, we focus on basics, especially the error backpropagation to compute gradients with respect to model parameters. Further, we go into detail on how error backpropagation algorithm is applied on long short-term memory (LSTM) by unfolding the memory unit.
翻译：我们描述经常神经网络(RNN),这些网络吸引了对相继任务的极大关注,如笔迹识别、语音识别和文字图像等。然而,与一般反馈神经网络相比,RNN拥有反馈环,这使得很难理解后向调整步骤。因此,我们侧重于基础,特别是计算模型参数梯度的反向偏差。此外,我们详细介绍如何通过运行记忆单元对长期短期内存(LSTM)应用错误反向调整算法(LSTM)。
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