Dr. James McCaffrey of Microsoft Research provides full code and step-by-step examples of anomaly detection, used to find items in a dataset that are different from the majority for tasks like ...

Autoencoders are a class of unsupervised neural networks designed to learn efficient data representations by encoding inputs into a compact latent space and then reconstructing them. Their versatility ...

A variational autoencoder (VAE) is one of several generative models that use deep learning to generate new content, detect anomalies and remove noise. Suited for generating synthetic time series data ...

[Click on image for larger view.] Figure 1: Autoencoder Anomaly Detection in Action This article assumes you have an intermediate or better familiarity with a C-family programming language, preferably ...