[1]Hochreiter, Sepp; Schmidhuber, Jürgen.Long Short-Term Memory[J].Neural Computation,1997,9(8)

Long Short-Term Memory Networks With Python Develop Deep Learning Models for your Sequence Prediction Problems by Jason Brownlee 14 step-by-step lessons, 246 pages. 6 LSTM model architectures. 长期短期记忆网络（简称 LSTM）是一种经常性神经网络，可在具有挑战性的预测问题上取得最先进的结果。 在这本以LSTM为中心的电子书中，你习惯了友好的机器学习掌握风格，最后切入了关于 LSTM 的数学、研究论文和综合描述。 使用清晰的解释，标准的Python库（Keras和TensorFlow2）和分步教程课程，您将发现什么是 LSTM，以及如何开发一套 LSTM 模型，以充分利用您的序列预测问题的方法

机器学习专家Jason Brownlee所著，详细讲解了创造lstm的起因，优势，反向传播算法，数据集的归一化，Vanilla LSTMs，Stacked LSTMs，CNN LSTMs原理及原码实现。让您一本书就可以基本完全了解lstm!!

数据科学家，学生，机器学习工程师

对LSTM的详细介绍书籍（全英文版），需要的可以看看

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Welcome to Long Short-Term Memory Networks With Python. Long Short-Term Memory (LSTM) recurrent neural networks are one of the most interesting types of deep learning at the moment. They have been used to demonstrate world-class results in complex problem domains such as language translation, automatic image captioning, and text generation. LSTMs are very di↵erent to other deep learning techniques, such as Multilayer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs), in that they are designed specifically for sequence prediction problems. I designed this book for you to rapidly discover what LSTMs are, how they work, and how you can bring this important technology to your own sequence prediction problems.

作者为Jason Brownlee. 请支持正版！本资源供非商业用途共享！ About the Ebook: 3 parts, 14 step-by-step tutorial lessons, 246 pages. 6 LSTM model architectures. 45 Python (.py) files.

Long Short Term Memory Networks for Anomaly Detection in Time Series - LSTM在时序数据中的应用