Explainable AI (XAI) design for unsupervised deep anomaly detector

 By Medium - 2021-03-14

Description

An interpretable prototype of unsupervised deep convolutional neural network & lstm autoencoders based real-time anomaly detection from high-dimensional heterogeneous/homogeneous time series…

Summary

  • In this blog post, I will take you through the new features of the package “msda”.
  • We use the unlabeled data to capture, and learn the data distribution that is used to forecast the normal behavior of a time-series.
  • The parameters of the network are optimized using ADAM optimizer.
  • For more details peek here 4) Post-processing data to input into the anomaly detector Next, we are inputting data with no missing values, removal of unwanted fields, assert the timestamp field, etc.

 

Topics

  1. Machine_Learning (0.37)
  2. Backend (0.35)
  3. Database (0.16)

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