Beginning Anomaly Detection Using

autoencoders, restricted 

Boltzmann machines, RNN/LSTM networks, and temporal convolutional networks.

In the next chapter, we will look at 

unsupervised anomaly detection with 

autoencoders.

Chapter 3   IntroduCtIon to deep LearnIng

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© Sridhar Alla, Suman Kalyan Adari 2019 

S. Alla and S. K. Adari, Beginning Anomaly Detection Using Python-Based Deep Learning,  

https://doi.org/10.1007/978-1-4842-5177-5_4

CHAPTER 4

Autoencoders

In this chapter, you will learn about autoencoder neural networks and the different types 

of autoencoders. You will also learn how autoencoders can be used to detect anomalies 

and how you can implement anomaly detection using autoencoders.

In a nutshell, the following topics will be covered throughout this chapter:

•  What are autoencoders?

•  Simple autoencoders

•  Sparse autoencoders

•  Deep autoencoders

•  Convolutional autoencoders

•  Denoising autoencoders

•  Variational autoencoders