Visual Studio Magazine

Autoencoder Anomaly Detection Using PyTorch

Anomaly detection is the process of finding items in a dataset that are different in some way from the majority of the items. For example, you could examine a dataset of credit card transactions to ...
note

A Detour Copy-Writing of "Anomaly Detection with Python" - Chapter 2, Section 2.4 "Anomaly Detection via Advanced Feature Extraction" ④ Autoencoder (AE)

Autoencoder (AE) The idea of using an autoencoder for anomaly detection is very similar to principal component analysis: "dimensionality reduction" and "re-dimensionalization". Let's look at a ...
GitHub

A Periodic Functional Autoencoder for Motion Generation via Phase Manifolds

Official implementation of FunPhase: A Periodic Functional Autoencoder for Motion Generation via Phase Manifolds (Pegoraro et al., 2025 — accepted at ICML 2026). Concretely, a Perceiver-based ...
Analytics Insight

Introduction to Neural Networks and Deep Learning with Python, Harvard University

Introduction to Neural Networks and Deep Learning with Python course by Harvard School of Engineering and Applied Sciences provides this course fully online, de ...
Visual Studio Magazine

Autoencoder Anomaly Detection Using PyTorch

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 ...