Autoencoders: Compression, Reconstruction, and Beyond - Blog Post | Dave Bullock / eecue

> **_NOTE:_** This post is part of my [Machine Learning Series](#) where I discuss how AI/ML works and how it has evolved over the last few decades. Autoencoders are a type of neural network architecture used for tasks such as dimensionality reduction, feature extraction, and data denoising. With their ability to learn efficient representations of data, autoencoders have found applications in various fields, from image processing to anomaly detection. In this post, we'll explore the structure and functionality of autoencoders and delve into their use cases.