📄 中文摘要
在前一篇文章中，探讨了递归神经网络（RNN）的必要性及其应用案例。假设我们有昨日和今日的股票价格数据，RNN 可以有效处理这些时间序列数据。通过将序列数据逐步输入网络，RNN 能够捕捉时间依赖性，从而在预测未来数据时更加准确。RNN 的结构允许信息在序列中循环传播，使得模型能够记住之前的信息并影响后续的输出。这种特性使得 RNN 在自然语言处理、时间序列预测等领域具有广泛应用。
理解 RNN – 第二部分：在网络中运行序列数据
出处: Understanding RNNs – Part 2: Running Sequential Data Through the Network
发布: 2026年2月17日
📄 English Summary
Understanding RNNs – Part 2: Running Sequential Data Through the Network
The previous article explored the necessity of Recurrent Neural Networks (RNNs) and provided an example use case. Assuming we have stock price data from yesterday and today, RNNs can effectively handle such time series data. By sequentially feeding data into the network, RNNs capture temporal dependencies, enhancing accuracy in future predictions. The architecture of RNNs allows information to circulate within the sequence, enabling the model to retain previous information that influences subsequent outputs. This characteristic makes RNNs widely applicable in fields such as natural language processing and time series forecasting.