ts13_install tf env_RNN_Bidirectional LSTM_GRU_Minimal gated_TimeDistributed_Time Series Forecasting

最新推荐文章于 2024-12-28 08:30:00 发布

LIQING LIN

最新推荐文章于 2024-12-28 08:30:00 发布

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分类专栏： pythonForFinance2 文章标签：深度学习人工智能

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本章关注使用深度学习进行时间序列预测，特别是利用Keras和PyTorch实现不同深度学习架构。讨论了RNN、LSTM、GRU等网络在序列数据上的应用，以及如何使用Keras的TimeDistributed层。通过案例展示了这些模型在能源消耗、每日温度和航空旅客数据上的表现。

If you have been searching the web for topics on data science, artifcial intelligence, or machine learning, it is hard to escape headlines on deep learning. Deep learning is a subset of machine learning and excels when dealing with large and complex data, as it can extract complex features with minimal human involvement. Deep learning works well with structured and unstructured data and can be used in supervised, unsupervised, and semi-supervised learning. Several innovations have contributed to its wide adoption, such as the transfer learning technique allowing data scientists to leverage existing pre-trained models