About this document
Csen 3083 DL (Autoencoders) by vmahadas is a document available to read on EtoBox.
Autoencoders are unsupervised neural networks that compress input data into a lower-dimensional representation and reconstruct it back to the original input. They consist of three main components: an encoder, a bottleneck (latent space), and a decoder, with the goal of minimizing reconstruction error. Various types of autoencoders exist, including undercomplete, sparse, denoising, and variational autoencoders, each with specific applications and advantages.
- Author
- vmahadas
- Language
- EN