GitHub

This repository contains two Jupyter notebooks that provide comprehensive tutorials on implementing an Autoencoder and a Variational Autoencoder using the MNIST dataset.

Autoencoder (AE) Tutorial: This notebook guides you through the basics of building and training an Autoencoder, demonstrating how to use it for data compression and reconstruction with the MNIST ...
GitHub

PyOD Tutorial - autoencoder.ipynb

"- We do not use the iris dataset because it only has 4 variables. We build a large dataset with 25 variables in order to show the power of neural network." "from pyod.utils.data import ...
Visual Studio Magazine

Data Anomaly Detection Using a Neural Autoencoder with C#

Dr. James McCaffrey of Microsoft Research tackles the process of examining a set of source data to find data items that are different in some way from the majority of the source items. Data anomaly ...
IEEE

A Light-Weight and Robust Tensor Convolutional Autoencoder for Anomaly Detection

Abstract: Robust PCA is a popular anomaly detection technique and has been widely used in many applications. Although Robust PCA is promising, it is usually designed in a two-order matrix form, which ...