Use Unsupervised Neural Networks to effectively detect and isolate anomalies from a large dataset !
In this two-parts blog series, I will take you through a simple practical example to illustrate how Autoencoder, a kind of Neural Networks, can be an effective option to identify and isolate anomalies present in a large numerical dataset. At the end of this exercise, a beginner would have a good idea on what anomaly detection involves, where it is applicable, the various options to detect anomalies and how Autoencoder can be used as one of the effective means.
To avoid boring you with a very long blog, I have split this guide in to two parts. In the preceding part 1 of the series, I have covered the theoretical concepts of anomaly detection, different approaches to anomaly detection in ML, the fundamentals of Autoencoders and how they help in anomaly detection. So, if you have not gone through the part 1 of the blog, please read it as it sets the required context to follow further.
Be the first to comment