Abstract
In this chapter, you will learn about recurrent neural networks and long short-term memory models. You will also learn how LSTMs work and how they can be used to detect anomalies and how you can implement anomaly detection using LSTM. You will work through several datasets depicting time series of different types of data such as CPU utilization, taxi demand, etc. to illustrate how to detect anomalies. This chapter introduces you to many concepts using LSTM so as to enable you to explore further using the Jupyter notebooks provided as part of the book material.
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© 2019 Sridhar Alla, Suman Kalyan Adari
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Alla, S., Adari, S.K. (2019). Long Short-Term Memory Models. In: Beginning Anomaly Detection Using Python-Based Deep Learning. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-5177-5_6
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DOI: https://doi.org/10.1007/978-1-4842-5177-5_6
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Publisher Name: Apress, Berkeley, CA
Print ISBN: 978-1-4842-5176-8
Online ISBN: 978-1-4842-5177-5
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